Note : Les descriptions sont présentées dans la langue officielle dans laquelle elles ont été soumises.
WO 2022/221591
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MULTI-CHANNEL PROTEIN VOXELIZATION TO PREDICT VARIANT
PATHOGENICITY USING DEEP CONVOLUTIONAL NEURAL NETWORKS
PRIORITY APPLICATIONS
[0001] This application claims priority to U.S. Nonprovisional
Patent Application No.
17/703,935 titled "Multi-channel Protein Voxelization To Predict Variant
Pathogenicity Using
Deep Convolutional Neural Networks," filed March 24, 2022 (Attorney Docket No.
ILLM 1047-2/IP-2142-US) which claims priority to U.S. Provisional Patent
Application No.
63/175,495, titled "Multi-channel Protein Voxelization To Predict Variant
Pathogenicity Using
Deep Convolutional Neural Networks," filed on April 15, 2021, (Atty. Docket
No.
ILLM 1047-1/IP-2142-PRV).
[0002] This application also claims priority to U.S. Nonprovisional
Patent Application
No. 17/703,958 titled "Efficient Voxelization For Deep Learning," filed March
24, 2022
(Attorney Docket No. ILLM 1048-2/IP-2143-US), which claims priority to U.S.
Provisional
Patent Application No. 63/175,767, titled "Efficient Voxelization For Deep
Learning," filed on
April 16, 2021, (Atty. Docket No. ILLM 1048-1/IP-2143-PRV).
[0003] The priority applications are hereby incorporated by
reference for all purposes.
RELATED APPLICATION
[0004] This application is related to PCT Patent Application titled
"Efficient Voxelization
For Deep Learning," (Attorney Docket No. ILLM 1048-3/IP-2143-PCT) filed
contemporaneously. The related application is hereby incorporated by reference
for all purposes.
FIELD OF THE TECHNOLOGY DISCLOSED
[0005] The technology disclosed relates to artificial intelligence
type computers and digital
data processing systems and corresponding data processing methods and products
for emulation
of intelligence (i.e., knowledge based systems, reasoning systems, and
knowledge acquisition
systems); and including systems for reasoning with uncertainty (e.g., fuzzy
logic systems),
adaptive systems, machine learning systems, and artificial neural networks. In
particular, the
technology disclosed relates to using deep convolutional neural networks to
analyze multi-
channel voxelized data.
INCORPORATIONS
[0006] The following are incorporated by reference for all purposes
as if fully set forth
herein:
[0007] Sundaram, L. et al. Predicting the clinical impact of human
mutation with deep neural
networks. Nat. Genet. 50, 1161-1170 (2018);
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[0008] Jaganathan, K. et al. Predicting splicing from primary
sequence with deep learning.
Cell 176, 535-548 (2019);
[0009] US Provisional Patent Application No. 62/573,144, titled
"TRAINING A DEEP
PATHOGENICITY CLASSIFIER USING LARGE-SCALE BENIGN TRAINING DATA,"
filed October 16, 2017 (Attorney Docket No. ILLM 1000-1/IP-1611-PRV);
[0010] US Provisional Patent Application No. 62/573,149, titled
"PATHOGENICITY
CLASSIFIER BASED ON DEEP CONVOLUTIONAL NEURAL NETWORKS (CNNs)," filed
October 16, 2017 (Attorney Docket No. ILLM 1000-2/IP-1612-PRV);
[0011] US Provisional Patent Application No. 62/573,153, titled
"DEEP SEMI-
SUPERVISED LEARNING THAT GENERATES LARGE-SCALE PATHOGENIC
TRAINING DATA," filed October 16, 2017 (Attorney Docket No. ILLM 1000-3/IP-
1613-
PRV);
[0012] US Provisional Patent Application No. 62/582,898, titled
"PATHOGENICITY
CLASSIFICATION OF GENOMIC DATA USING DEEP CONVOLUTIONAL NEURAL
NETWORKS (CNNs)," filed November 7, 2017 (Attorney Docket No. ILLM 1000-4/IP-
1618-
PRV);
[0013] US Nonprovisional Patent Application No. 16/160,903, titled
"DEEP LEARNING-
BASED TECHNIQUES FOR TRAINING DEEP CONVOLUTIONAL NEURAL
NETWORKS," filed on October 15, 2018 (Attorney Docket No. ILLM 1000-5/IP-1611-
US);
[0014] US Nonprovisional Patent Application No. 16/160,986, titled -
DEEP
CONVOLUTIONAL NEURAL NETWORKS FOR VARIAN 1 CLASSIFICATION," filed on
October 15, 2018 (Attorney Docket No. ILLM 1000-6/IP-1612-US);
[0015] US Nonprovisional Patent Application No. 16/160,968, titled
"SEM1-SUPERVISED
LEARNING FOR TRAINING AN ENSEMBLE OF DEEP CONVOLUTIONAL NEURAL
NETWORKS," filed on October 15, 2018 (Attorney Docket No. ILLM 1000-7/IP-1613-
US);
and
[0016] US Nonprovisional Patent Application No. 16/407,149, titled
"DEEP LEARNING-
BASED TECHNIQUES FOR PRE-TRAINING DEEP CONVOLUTIONAL NEURAL
NETWORKS," filed May 8, 2019 (Attorney Docket No. ILLM 1010-1/1P-1734-US).
BACKGROUND
[0017] The subject matter discussed in this section should not be
assumed to be prior art
merely as a result of its mention in this section. Similarly, a problem
mentioned in this section or
associated with the subject matter provided as background should not be
assumed to have been
previously recognized in the prior art. The subject matter in this section
merely represents
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different approaches, which in and of themselves can also correspond to
implementations of the
claimed technology.
[0018] Genomics, in the broad sense, also referred to as functional
genomics, aims to
characterize the function of every genomic element of an organism by using
genome-scale
assays such as genome sequencing, transcriptome profiling and proteomics.
Genomics arose as a
data-driven science ¨ it operates by discovering novel properties from
explorations of genome-
scale data rather than by testing preconceived models and hypotheses.
Applications of genomics
include finding associations between genotype and phenotype, discovering
biomarkers for
patient stratification, predicting the function of genes, and charting
biochemically active genomic
regions such as transcriptional enhancers.
[0019] Genomics data are too large and too complex to be mined
solely by visual
investigation of pairwise correlations. Instead, analytical tools are required
to support the
discovery of unanticipated relationships, to derive novel hypotheses and
models and to make
predictions. Unlike some algorithms, in which assumptions and domain expertise
are hard coded,
machine learning algorithms are designed to automatically detect patterns in
data. Hence,
machine learning algorithms are suited to data-driven sciences and, in
particular, to genomics.
However, the performance of machine learning algorithms can strongly depend on
how the data
are represented, that is, on how each variable (also called a feature) is
computed. For instance, to
classify a tumor as malign or benign from a fluorescent microscopy image, a
preprocessing
algorithm could detect cells, identify the cell type, and generate a list of
cell counts for each cell
type.
[0020] A machine learning model can take the estimated cell counts,
which are examples of
handcrafted features, as input features to classify the tumor. A central issue
is that classification
performance depends heavily on the quality and the relevance of these
features. For example,
relevant visual features such as cell morphology, distances between cells or
localization within
an organ are not captured in cell counts, and this incomplete representation
of the data may
reduce classification accuracy.
[0021] Deep learning, a subdiscipline of machine learning,
addresses this issue by
embedding the computation of features into the machine learning model itself
to yield end-to-end
models. This outcome has been realized through the development of deep neural
networks,
machine learning models that comprise successive elementary operations, which
compute
increasingly more complex features by taking the results of preceding
operations as input. Deep
neural networks are able to improve prediction accuracy by discovering
relevant features of high
complexity, such as the cell morphology and spatial organization of cells in
the above example.
The construction and training of deep neural networks have been enabled by the
explosion of
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data, algorithmic advances, and substantial increases in computational
capacity, particularly
through the use of graphical processing units (GPUs).
[0022] The goal of supervised learning is to obtain a model that
takes features as input and
returns a prediction for a so-called target variable. An example of a
supervised learning problem
is one that predicts whether an intron is spliced out or not (the target)
given features on the RNA
such as the presence or absence of the canonical splice site sequence, the
location of the splicing
branchpoint or intron length. Training a machine learning model refers to
learning its parameters,
which commonly involves minimizing a loss function on training data with the
aim of making
accurate predictions on unseen data.
[0023] For many supervised learning problems in computational
biology, the input data can
be represented as a table with multiple columns, or features, each of which
contains numerical or
categorical data that are potentially useful for making predictions. Some
input data are naturally
represented as features in a table (such as temperature or time), whereas
other input data need to
be first transformed (such as deoxyribonucleic acid (DNA) sequence into k-mer
counts) using a
process called feature extraction to fit a tabular representation. For the
intron-splicing prediction
problem, the presence or absence of the canonical splice site sequence, the
location of the
splicing branchpoint and the intron length can be preprocessed features
collected in a tabular
format. Tabular data are standard for a wide range of supervised machine
learning models,
ranging from simple linear models, such as logistic regression, to more
flexible nonlinear
models, such as neural networks and many others.
[0024] Logistic regression is a binary classifier, that is, a
supervised learning model that
predicts a binary target variable. Specifically, logistic regression predicts
the probability of the
positive class by computing a weighted sum of the input features mapped to the
[0,1] interval
using the sigmoid function, a type of activation function. The parameters of
logistic regression,
or other linear classifiers that use different activation functions, are the
weights in the weighted
sum. Linear classifiers fail when the classes, for instance, that of an intron
spliced out or not,
cannot be well discriminated with a weighted sum of input features. To improve
predictive
performance, new input features can be manually added by transforming or
combining existing
features in new ways, for example, by taking powers or pairwise products.
[0025] Neural networks use hidden layers to learn these nonlinear
feature transformations
automatically. Each hidden layer can be thought of as multiple linear models
with their output
transformed by a nonlinear activation function, such as the sigmoid function
or the more popular
rectified-linear unit (ReLU). Together, these layers compose the input
features into relevant
complex patterns, which facilitates the task of distinguishing two classes.
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[0026] Deep neural networks use many hidden layers, and a layer is
said to be fully-
connected when each neuron receives inputs from all neurons of the preceding
layer. Neural
networks are commonly trained using stochastic gradient descent, an algorithm
suited to training
models on very large data sets. Implementation of neural networks using modern
deep learning
frameworks enables rapid prototyping with different architectures and data
sets. Fully-connected
neural networks can be used for a number of genomics applications, which
include predicting the
percentage of exons spliced in for a given sequence from sequence features
such as the presence
of binding motifs of splice factors or sequence conservation; prioritizing
potential disease-
causing genetic variants; and predicting cis-regulatory elements in a given
genomic region using
features such as chromatin marks, gene expression and evolutionary
conservation.
[0027] Local dependencies in spatial and longitudinal data must be
considered for effective
predictions. For example, shuffling a DNA sequence or the pixels of an image
severely disrupts
informative patterns. These local dependencies set spatial or longitudinal
data apart from tabular
data, for which the ordering of the features is arbitrary. Consider the
problem of classifying
genomic regions as bound versus unbound by a particular transcription factor,
in which bound
regions are defined as high-confidence binding events in chromatin
immunoprecipitation
following by sequencing (ChIP¨seq) data. Transcription factors bind to DNA by
recognizing
sequence motifs. A fully-connected layer based on sequence-derived features,
such as the
number of k-mer instances or the position weight matrix (PWM) matches in the
sequence, can be
used for this task. As k-mer or PWM instance frequencies are robust to
shifting motifs within the
sequence, such models could generalize well to sequences with the same motifs
located at
different positions. However, they would fail to recognize patterns in which
transcription factor
binding depends on a combination of multiple motifs with well-defined spacing.
Furthermore,
the number of possible k-mers increases exponentially with k-mer length, which
poses both
storage and overfilling challenges.
[0028] A convolutional layer is a special form of fully-connected
layer in which the same
fully-connected layer is applied locally, for example, in a 6 bp window, to
all sequence positions.
This approach can also be viewed as scanning the sequence using multiple PWMs,
for example,
for transcription factors GATA1 and TALI. By using the same model parameters
across
positions, the total number of parameters is drastically reduced, and the
network is able to detect
a motif at positions not seen during training. Each convolutional layer scans
the sequence with
several filters by producing a scalar value at every position, which
quantifies the match between
the filter and the sequence. As in fully-connected neural networks, a
nonlinear activation
function (commonly ReLU) is applied at each layer. Next, a pooling operation
is applied, which
aggregates the activations in contiguous bins across the positional axis,
commonly taking the
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maximal or average activation for each channel. Pooling reduces the effective
sequence length
and coarsens the signal. The subsequent convolutional layer composes the
output of the previous
layer and is able to detect whether a GATA1 motif and TALI motif were present
at some
distance range. Finally, the output of the convolutional layers can be used as
input to a fully-
connected neural network to perform the final prediction task. Hence,
different types of neural
network layers (e.g., fully-connected layers and convolutional layers) can be
combined within a
single neural network.
[0029] Convolutional neural networks (CNNs) can predict various
molecular phenotypes on
the basis of DNA sequence alone. Applications include classifying
transcription factor binding
sites and predicting molecular phenotypes such as chromatin features, DNA
contact maps, DNA
methylation, gene expression, translation efficiency, RBP binding, and
microRNA (miRNA)
targets. In addition to predicting molecular phenotypes from the sequence,
convolutional neural
networks can be applied to more technical tasks traditionally addressed by
handcrafted
bioinformatics pipelines. For example, convolutional neural networks can
predict the specificity
of guide RNA, denoise ChIP¨seq, enhance Hi-C data resolution, predict the
laboratory of origin
from DNA sequences and call genetic variants. Convolutional neural networks
have also been
employed to model long-range dependencies in the genome. Although interacting
regulatory
elements may be distantly located on the unfolded linear DNA sequence, these
elements are
often proximal in the actual 3D chromatin conformation. Hence, modelling
molecular
phenotypes from the linear DNA sequence, albeit a crude approximation of the
chromatin, can be
improved by allowing for long-range dependencies and allowing the model to
implicitly learn
aspects of the 3D organization, such as promoter¨enhancer looping. This is
achieved by using
dilated convolutions, which have a receptive field of up to 32 kb. Dilated
convolutions also allow
splice sites to be predicted from sequence using a receptive field of 10 kb,
thereby enabling the
integration of genetic sequence across distances as long as typical human
introns (See
Jaganathan, K. et al. Predicting splicing from primary sequence with deep
learning. Cell 176,
535-548 (2019)).
[0030] Different types of neural network can be characterized by
their parameter-sharing
schemes. For example, fully-connected layers have no parameter sharing,
whereas convolutional
layers impose translational invariance by applying the same filters at every
position of their
input. Recurrent neural networks (RNNs) are an alternative to convolutional
neural networks for
processing sequential data, such as DNA sequences or time series, that
implement a different
parameter-sharing scheme. Recurrent neural networks apply the same operation
to each sequence
element. The operation takes as input the memory of the previous sequence
element and the new
input. It updates the memory and optionally emits an output, which is either
passed on to
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subsequent layers or is directly used as model predictions. By applying the
same model at each
sequence element, recurrent neural networks are invariant to the position
index in the processed
sequence. For example, a recurrent neural network can detect an open reading
frame in a DNA
sequence regardless of the position in the sequence. This task requires the
recognition of a
certain series of inputs, such as the start codon followed by an in-frame stop
codon.
[0031] The main advantage of recurrent neural networks over
convolutional neural networks
is that they are, in theory, able to carry over information through infinitely
long sequences via
memory. Furthermore, recurrent neural networks can naturally process sequences
of widely
varying length, such as mRNA sequences. However, convolutional neural networks
combined
with various tricks (such as dilated convolutions) can reach comparable or
even better
performances than recurrent neural networks on sequence-modelling tasks, such
as audio
synthesis and machine translation. Recurrent neural networks can aggregate the
outputs of
convolutional neural networks for predicting single-cell DNA methylation
states, RBP binding,
transcription factor binding, and DNA accessibility. Moreover, because
recurrent neural
networks apply a sequential operation, they cannot be easily parallelized and
are hence much
slower to compute than convolutional neural networks.
[0032] Each human has a unique genetic code, though a large portion
of the human genetic
code is common for all humans. In some cases, a human genetic code may include
an outlier,
called a genetic variant, that may be common among individuals of a relatively
small group of
the human population. For example, a particular human protein may comprise a
specific
sequence of amino acids, whereas a variant of that protein may differ by one
amino acid in the
otherwise same specific sequence.
[0033] Genetic variants may be pathogenetic, leading to diseases.
Though most of such
genetic variants have been depleted from genomes by natural selection, an
ability to identify
which genetic variants are likely to be pathogenic can help researchers focus
on these genetic
variants to gain an understanding of the corresponding diseases and their
diagnostics, treatments,
or cures. The clinical interpretation of millions of human genetic variants
remains unclear. Some
of the most frequent pathogenic variants are single nucleotide missense
mutations that change
the amino acid of a protein. However, not all missense mutations are
pathogenic.
[0034] Models that can predict molecular phenotypes directly from
biological sequences can
be used as in silico perturbation tools to probe the associations between
genetic variation and
phenotypic variation and have emerged as new methods for quantitative trait
loci identification
and variant prioritization. These approaches are of major importance given
that the majority of
variants identified by genome-wide association studies of complex phenotypes
are non-coding,
which makes it challenging to estimate their effects and contribution to
phenotypes. Moreover,
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linkage disequilibrium results in blocks of variants being co-inherited, which
creates difficulties
in pinpointing individual causal variants. Thus, sequence-based deep learning
models that can be
used as interrogation tools for assessing the impact of such variants offer a
promising approach
to find potential drivers of complex phenotypes. One example includes
predicting the effect of
non-coding single-nucleotide variants and short insertions or deletions
(indels) indirectly from
the difference between two variants in terms of transcription factor binding,
chromatin
accessibility or gene expression predictions. Another example includes
predicting novel splice
site creation from sequence or quantitative effects of genetic variants on
splicing.
[0035] End-to-end deep learning approaches for variant effect
predictions are applied to
predict the pathogenicity of missense variants from protein sequence and
sequence conservation
data (See Sundaram, L. et al. Predicting the clinical impact of human mutation
with deep neural
networks. Nat. Genet. 50, 1161-1170 (2018), referred to herein as "PrimateAr).
PrimateAI uses
deep neural networks trained on variants of known pathogenicity with data
augmentation using
cross-species information. In particular, PrimateAI uses sequences of wild-
type and mutant
proteins to compare the difference and decide the pathogenicity of mutations
using the trained
deep neural networks. Such an approach which utilizes the protein sequences
for pathogenicity
prediction is promising because it can avoid the circularity problem and
overfitting to previous
knowledge. However, compared to the adequate number of data to train the deep
neural networks
effectively, the number of clinical data available in ClinVar is relatively
small. To overcome this
data scarcity, PrimateAI uses common human variants and variants from primates
as benign data
while simulated variants based on trinucleotide context were used as unlabeled
data.
[0036] PrimateAI outperforms prior methods when trained directly
upon sequence
alignments. PrimateAI learns important protein domains, conserved amino acid
positions, and
sequence dependencies directly from the training data consisting of about
120,000 human
samples. PrimateAI substantially exceeds the performance of other variant
pathogenicity
prediction tools in differentiating benign and pathogenic de-novo mutations in
candidate
developmental disorder genes, and in reproducing prior knowledge in ClinVar.
These results
suggest that PrimateAI is an important step forward for variant classification
tools that may
lessen the reliance of clinical reporting on prior knowledge.
[0037] Central to protein biology is the understanding of how
structural elements give rise to
observed function. The surfeit of protein structural data enables development
of computational
methods to systematically derive rules governing structural-functional
relationships. However,
performance of these methods depends critically on the choice of protein
structural
representation.
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[0038] Protein sites are microenvironments within a protein
structure, distinguished by their
structural or functional role. A site can be defined by a three-dimensional
(3D) location and a
local neighborhood around this location in which the structure or function
exists. Central to
rational protein engineering is the understanding of how the structural
arrangement of amino
acids creates functional characteristics within protein sites. Determination
of the structural and
functional roles of individual amino acids within a protein provides
information to help engineer
and alter protein functions. Identifying functionally or structurally
important amino acids allows
focused engineering efforts such as site-directed mutagenesis for altering
targeted protein
functional properties. Alternatively, this knowledge can help avoid
engineering designs that
would abolish a desired function.
[0039] Since it has been established that structure is far more
conserved than sequence, the
increase in protein structural data provides an opportunity to systematically
study the underlying
pattern governing the structural-functional relationships using data-driven
approaches. A
fundamental aspect of any computational protein analysis is how protein
structural information is
represented. The performance of machine learning methods often depends more on
the choice of
data representation than the machine learning algorithm employed. Good
representations
efficiently capture the most critical information while poor representations
create a noisy
distribution with no underlying patterns.
[0040] The surfeit of protein structures and the recent success of
deep learning algorithms
provide an opportunity to develop tools for automatically extracting task
specific representations
of protein structures. Therefore, an opportunity arises to predict variant
pathogenicity using
multi-channel voxelized representations of 3D protein structures as input to
deep neural
networks.
BRIEF DESCRIPTION OF THE DRAWINGS
[0041] The patent or application file contains at least one drawing
executed in color. Copies
of this patent or patent application publication with color drawing(s) will be
provided by the
Office upon request and payment of the necessary fee.
[0042] The color drawings also may be available in PAIR via the
Supplemental Content tab.
In the drawings, like reference characters generally refer to like parts
throughout the different
views Also, the drawings are not necessarily to scale, with an emphasis
instead generally being
placed upon illustrating the principles of the technology disclosed. In the
following description,
various implementations of the technology disclosed are described with
reference to the
following drawings, in which.
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[0043] Figure 1 is a flow diagram that illustrates a process of a
system for determining
pathogenicity of variants, according to various implementations of the
technology disclosed.
[0044] Figure 2 schematically illustrates an example reference
amino acid sequence of a
protein and an alternative amino acid sequence of the protein, in accordance
with one
implementation of the technology disclosed.
[0045] Figure 3 illustrates amino acid-wise classification of atoms
of amino acids in the
reference amino acid sequence of Figure 2, in accordance with one
implementation of the
technology disclosed.
[0046] Figure 4 illustrates amino acid-wise attribution of 3D
atomic coordinates of the alpha-
carbon atoms classified in Figure 3 on an amino acid-basis, in accordance with
one
implementation of the technology disclosed.
[0047] Figure 5 schematically illustrates a process of determining
voxel-wise distance
values, in accordance with one implementation of the technology disclosed.
[0048] Figure 6 shows an example of twenty-one amino acid-wise
distance channels, in
accordance with one implementation of the technology disclosed.
[0049] Figure 7 is a schematic diagram of a distance channel
tensor, in accordance with one
implementation of the technology disclosed.
[0050] Figure 8 shows one-hot encodings of the reference amino acid
and the alternative
amino acid from Figure 2, in accordance with one implementation of the
technology disclosed.
[0051] Figure 9 is a schematic diagram of a voxelized one-hot
encoded reference amino acid
and a voxelized one-hot encoded variant/alternative amino acid, in accordance
with one
implementation of the technology disclosed.
[0052] Figure 10 schematically illustrates a concatenation process
that voxel-wise
concatenates the distance channel tensor of Figure 7 and a reference allele
tensor, in accordance
with one implementation of the technology disclosed.
[0053] Figure 11 schematically illustrates a concatenation process
that voxel-wise
concatenates the distance channel tensor of Figure 7, the reference allele
tensor of Figure 10, and
an alternative allele tensor, in accordance with one implementation of the
technology disclosed.
[0054] Figure 12 is a flow diagram that illustrates a process of a
system for determining and
assigning pan-amino acid conservation frequencies of nearest atoms to voxels
(voxelizing), in
accordance with one implementation of the technology disclosed.
[0055] Figure 13 illustrates voxels-to-nearest amino acids, in
accordance with one
implementation of the technology disclosed.
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[0056] Figure 14 shows an example multi-sequence alignment of the
reference amino acid
sequence across a ninety-nine species, in accordance with one implementation
of the technology
disclosed.
[0057] Figure 15 shows an example of determining a pan-amino acid
conservation
frequencies sequence for a particular voxel, in accordance with one
implementation of the
technology disclosed.
[0058] Figure 16 shows respective pan-amino acid conservation
frequencies determined for
respective voxels using the position frequency logic described in Figure 15,
in accordance with
one implementation of the technology disclosed.
[0059] Figure 17 illustrates voxelized per-voxel evolutionary
profiles, in accordance with
one implementation of the technology disclosed.
[0060] Figure 18 depicts example of an evolutionary profiles
tensor, in accordance with one
implementation of the technology disclosed.
[0061] Figure 19 is a flow diagram that illustrates a process of a
system for determining and
assigning per-amino acid conservation frequencies of nearest atoms to voxels
(voxelizing), in
accordance with one implementation of the technology disclosed.
[0062] Figure 20 shows various examples of voxelized annotation
channels that are
concatenated with the distance channel tensor, in accordance with one
implementation of the
technology disclosed.
[0063] Figure 21 illustrates different combinations and
permutations of input channels that
can be provided as inputs to a pathogenicity classifier for pathogenicity
determination of a target
variant, in accordance with one implementation of the technology disclosed.
[0064] Figure 22 shows different methods of calculating the
disclosed distance channels, in
accordance with various implementations of the technology disclosed.
[0065] Figure 23 shows different examples of the evolutionary
channels, in accordance with
various implementations of the technology disclosed.
[0066] Figure 24 shows different examples of the annotations
channels, in accordance with
various implementations of the technology di sclosed
[0067] Figure 25 shows different examples of the structure
confidence channels, in
accordance with various implementations of the technology disclosed.
[0068] Figure 26 shows an example processing architecture of the
pathogenicity classifier, in
accordance with one implementation of the technology disclosed.
[0069] Figure 27 shows an example processing architecture of the
pathogenicity classifier, in
accordance with one implementation of the technology disclosed.
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[0070] Figures 28, 29, 30, and 31 use PrimateAI as a benchmark
model to demonstrate the
disclosed PrimateAI 3D' s classification superiority over PrimateAI.
[0071] Figures 32A and 32B show the disclosed efficient
voxelization process, in accordance
with various implementations of the technology disclosed.
[0072] Figure 33 depicts how atoms are associated with voxels that
contain the atoms, in
accordance with one implementation of the technology disclosed.
[0073] Figure 34 shows generating voxel-to-atoms mapping from atom-
to-voxels mapping to
identify nearest atoms on a voxel-by-voxel basis, in accordance with one
implementation of the
technology disclosed.
[0074] Figures 35A and 35B illustrate how the disclosed efficient
voxelization has a runtime
complexity of 0(#atoms) versus the runtime complexity of 0(#atoms * #voxels)
without the use
of disclosed efficient voxelization.
[0075] Figure 36 shows an example computer system that can be used
to implement the
technology disclosed.
DETAILED DESCRIPTION
[0076] The following discussion is presented to enable any person
skilled in the art to make
and use the technology disclosed and is provided in the context of a
particular application and its
requirements. Various modifications to the disclosed implementations will be
readily apparent to
those skilled in the art, and the general principles defined herein may be
applied to other
implementations and applications without departing from the spirit and scope
of the technology
disclosed. Thus, the technology disclosed is not intended to be limited to the
implementations
shown but is to be accorded the widest scope consistent with the principles
and features
disclosed herein.
[0077] The detailed description of various implementations will be
better understood when
read in conjunction with the appended drawings. To the extent that the figures
illustrate diagrams
of the functional blocks of the various implementations, the functional blocks
are not necessarily
indicative of the division between hardware circuitry. Thus, for example, one
or more of the
functional blocks (e.g., modules, processors, or memories) may be implemented
in a single piece
of hardware (e.g., a general purpose signal processor or a block of random
access memory, hard
disk, or the like) or multiple pieces of hardware Similarly, the programs may
be stand-alone
programs, may be incorporated as subroutines in an operating system, may be
functions in an
installed software package, and the like. It should be understood that the
various
implementations are not limited to the arrangements and instrumentality shown
in the drawings.
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[0078] The processing engines and databases of the figures,
designated as modules, can be
implemented in hardware or software, and need not be divided up in precisely
the same blocks as
shown in the figures. Some of the modules can also be implemented on different
processors,
computers, or servers, or spread among a number of different processors,
computers, or servers.
In addition, it will be appreciated that some of the modules can be combined,
operated in parallel
or in a different sequence than that shown in the figures without affecting
the functions achieved.
The modules in the figures can also be thought of as flowchart steps in a
method. A module also
need not necessarily have all its code disposed contiguously in memory; some
parts of the code
can be separated from other parts of the code with code from other modules or
other functions
disposed in between.
Protein Structure-Based Pathogenicity Determination
[0079] Figure 1 is a flow diagram that illustrates a process 100 of
a system for determining
pathogenicity of variants. At step 102, a sequence accessor 104 of the system
accesses reference
and alternative amino acid sequences. At 112, a 3D structure generator 114 of
the system
generates 3D protein structures for a reference amino acid sequence. In some
implementations,
the 3D protein structures are homology models of human proteins. In one
implementation, a so-
called SwissModel homology modelling pipeline provides a public repository of
predicted
human protein structures. In another implementation, a so-called HHpred
homology modelling
uses a tool called Modeller to predict the structure of a target protein from
template structures.
[0080] Proteins are represented by a collection of atoms and their
coordinates in 3D space.
An amino acid can have a variety of atoms, such as carbon atoms, oxygen (0)
atoms, nitrogen
(N) atoms, and hydrogen (H) atoms. The atoms can be further classified as side
chain atoms and
backbone atoms. The backbone carbon atoms can include alpha-carbon (Ca) atoms
and beta-
carbon (C13) atoms.
[0081] At step 122, a coordinate classifier 124 of the system
classifies 3D atomic coordinates
of the 3D protein structures on an amino acid-basis. In one implementation,
the amino acid-wise
classification involves attributing the 3D atomic coordinates to the twenty-
one amino acid
categories (including stop or gap amino acid category). In one example, an
amino acid-wise
classification of alpha-carbon atoms can respectively list alpha-carbon atoms
under each of the
twenty-one amino acid categories. In another example, an amino acid-wise
classification of beta-
carbon atoms can respectively list beta-carbon atoms under each of the twenty-
one amino acid
categories.
[0082] In yet another example, an amino acid-wise classification of
oxygen atoms can
respectively list oxygen atoms under each of the twenty-one amino acid
categories. In yet
another example, an amino acid-wise classification of nitrogen atoms can
respectively list
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nitrogen atoms under each of the twenty-one amino acid categories. In yet
another example, an
amino acid-wise classification of hydrogen atoms can respectively list
hydrogen atoms under
each of the twenty-one amino acid categories.
[0083] A person skilled in the art will appreciate that, in various
implementations, the amino
acid-wise classification can include a subset of the twenty-one amino acid
categories and a
subset of the different atomic elements.
[0084] At step 132, a voxel grid generator 134 of the system
instantiates a voxel grid. The
voxel grid can have any resolution, for example, 3x3x3, 5x5x5, 7x7x7, and so
on. Voxels in the
voxel grid can be of any size, for example, one angstrom (A) on each side, two
A on each side,
three A on each side, and so on. One skilled in the art will appreciate that
these example
dimensions refer to cubic dimensions because voxels are cubes. Also, one
skilled in the art will
appreciate that these example dimensions are non-limiting, and the voxels can
have any cubic
dimensions.
[0085] At step 142, a voxel grid centerer 144 of the system centers
the voxel grid at the
reference amino acid experiencing a target variant at the amino acid level. In
one
implementation, the voxel grid is centered at an atomic coordinate of a
particular atom of the
reference amino acid experiencing the target variant, for example, the 3D
atomic coordinate of
the alpha-carbon atom of the reference amino acid experiencing the target
variant.
Distance Channels
[0086] The voxels in the voxel grid can have a plurality of
channels (or features). In one
implementation, the voxels in the voxel grid have a plurality of distance
channels (e.g., twenty-
one distance channels for the twenty-one amino acid categories, respectively
(including stop or
gap amino acid category)). At step 152, a distance channel generator 154 of
the system generates
amino acid-wise distance channels for the voxels in the voxel grid. The
distance channels are
independently generated for each of the twenty-one amino acid categories.
100871 Consider, for example, the Alanine (A) amino acid category.
Further consider, for
example, that the voxel grid is of size 3x3x3 and has twenty-seven voxels.
Then, in one
implementation, an Alanine distance channel includes twenty-seven distance
values for the
twenty-seven voxels in the voxel grid, respectively. The twenty-seven distance
values in the
Alanine distance channel are measured from respective centers of the twenty-
seven voxels in the
voxel grid to respective nearest atoms in the Alanine amino acid category.
[0088] In one example, the Alanine amino acid category includes
only alpha-carbon atoms
and therefore the nearest atoms are those Alanine alpha-carbon atoms that are
most proximate to
the twenty-seven voxels in the voxel grid, respectively. In another example,
the Alanine amino
acid category includes only beta-carbon atoms and therefore the nearest atoms
are those Alanine
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beta-carbon atoms that are most proximate to the twenty-seven voxels in the
voxel grid,
respectively.
[0089] In yet another example, the Alanine amino acid category
includes only oxygen atoms
and therefore the nearest atoms are those Alanine oxygen atoms that are most
proximate to the
twenty-seven voxels in the voxel grid, respectively. In yet another example,
the Alanine amino
acid category includes only nitrogen atoms and therefore the nearest atoms are
those Alanine
nitrogen atoms that are most proximate to the twenty-seven voxels in the voxel
grid,
respectively. In yet another example, the Alanine amino acid category includes
only hydrogen
atoms and therefore the nearest atoms are those Alanine hydrogen atoms that
are most proximate
to the twenty-seven voxels in the voxel grid, respectively.
[0090] Like the Alanine distance channel, the distance channel
generator 154 generates a
distance channel (i.e., a set of voxel-wise distance values) for each of the
remaining amino acid
categories. In other implementations, the distance channel generator 154
generates distance
channels only for a subset of the twenty-one amino acid categories.
[0091] In other implementations, the selection of the nearest atoms
is not confined to a
particular atom type. That is, within a subject amino acid category, the
nearest atom to a
particular voxel is selected, irrespective of the atomic element of the
nearest atom, and the
distance value for the particular voxel calculated for inclusion in the
distance channel for the
subject amino acid category.
[0092] In yet other implementations, the distance channels are
generated on an atomic
element-basis. Instead of or in addition to having the distance channels for
the amino acid
categories, distance values can be generated for atom element categories,
irrespective of the
amino acids to which the atoms belong. Consider, for example, that the atoms
of amino acids in
the reference amino acid sequence span seven atomic elements: carbon, oxygen,
nitrogen,
hydrogen, calcium, iodine, and sulfur. Then, the voxels in the voxel grid are
configured to have
seven distance channels, such that each of the seven distance channels have
twenty-seven voxel
wise distance values that specify distances to nearest atoms only within a
corresponding atomic
element category In other implementations, distance channels for only a subset
of the seven
atomic elements can be generated. In yet other implementations, the atomic
element categories
and the distance channel generation can be further stratified into variations
of a same atomic
element, for example, alpha-carbon (Ca) atoms and beta-carbon (Cr) atoms.
[0093] In yet other implementations, the distance channels can be
generated on an atom type-
basis, for example, distance channels only for side chain atoms and distance
channels only for
backbone atoms.
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[0094] The nearest atoms can be searched within a predefined
maximum scan radius from
the voxel centers (e.g., six angstrom (A)). Also, multiple atoms can be
nearest to a same voxel in
the voxel grid.
[0095] The distances are calculated between 3D coordinates of the
voxel centers and 3D
atomic coordinates of the atoms. Also, the distance channels are generated
with the voxel grid
centered at a same location (e.g., centered at the 3D atomic coordinate of the
alpha-carbon atom
of the reference amino acid experiencing the target variant).
[0096] The distances can be Euclidean distances. Also, the
distances can be parameterized by
atom size (or atom influence) (e.g., by using Lennard-Jones potential and/or
Van der Waals atom
radius of the atom in question). Also, the distance values can be normalized
by the maximum
scan radius, or by a maximum observed distance value of the furthest nearest
atom within a
subject amino acid category or a subject atomic element category or a subject
atom type
category. In some implementations, the distances between the voxels and the
atoms are
calculated based on polar coordinates of the voxels and the atoms. The polar
coordinates are
parameterized by angles between the voxels and the atoms. In one
implementation, this angel
information is used to generate an angle channel for the voxels (i.e.,
independent of the distance
channels). In some implementations, angles between a nearest atom and
neighboring atoms (e.g.,
backbone atoms) can be used as features that are encoded with the voxels.
Reference Allele and Alternative Allele Channels
[0097] The voxels in the voxel grid can also have reference allele
and alternative allele
channels. At step 162, a one-hot encoder 164 of the system generates a
reference one-hot
encoding of a reference amino acid in the reference amino acid sequence and an
alternative one-
hot encoding of an alternative amino acid in an alternative amino acid
sequence. The reference
amino acid experiences the target variant. The alternative amino acid is the
target variant. The
reference amino acid and the alternative amino acid are located at a same
position respectively in
the reference amino acid sequence and the alternative amino acid sequence. The
reference amino
acid sequence and the alternative amino acid sequence have the same position-
wise amino acid
composition with one exception. The exception is the position that has the
reference amino acid
in the reference amino acid sequence and the alternative amino acid in the
alternative amino acid
sequence.
[0098] At step 172, a concatenator 174 of the system concatenates
the amino acid-wise
distance channels and the reference and alternative one-hot encodings. In
another
implementation, the concatenator 174 concatenates the atomic element-wise
distance channels
and the reference and alternative one-hot encodings. In yet another
implementation, the
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concatenator 174 concatenates the atomic type-wise distance channels and the
reference and
alternative one-hot encodings.
[0099] At step 182, runtime logic 184 of the system processes the
concatenated amino acid-
wise/atomic element-wise/atomic type-wise distance channels and the reference
and alternative
one-hot encodings through a pathogenicity classifier (pathogenicity
determination engine) to
determine a pathogenicity of the target variant, which is in turn inferred as
a pathogenicity
determination of the underlying nucleotide variant that creates the target
variant at the amino
acid level. The pathogenicity classifier is trained using labelled datasets of
benign and
pathogenic variants, for example, using the backpropagation algorithm.
Additional details about
the labelled datasets of benign and pathogenic variants and example
architectures and training of
the pathogenicity classifier can be found in commonly owned US Patent
Application Nos.
16/160,903; 16/160,986; 16/160,968; and 16/407,149.
101001 Figure 2 schematically illustrates a reference amino acid
sequence 202 of a protein
200 and an alternative amino acid sequence 212 of the protein 200. The protein
200 comprises N
amino acids. Positions of the amino acids in the protein 200 are labelled 1,
2, 3...N. In the
illustrated example, position 16 is the location that experiences an amino
acid variant 214
(mutation) caused by an underlying nucleotide variant. For example, for the
reference amino acid
sequence 202, position 1 has reference amino acid Phenylalanine (F), position
16 has reference
amino acid Glycine (G) 204, and position N (e.g., the last amino acid of the
sequence 202) has
reference amino acid Leucine (L). Though not illustrated for clarity,
remaining positions in the
reference amino acid sequence 202 contain various amino acids in an order that
is specific to the
protein 200. The alternative amino acid sequence 212 is the same as the
reference amino acid
sequence 202 except for the variant 214 at position 16, which contains the
alternative amino acid
Alanine (A) 214 instead of the reference amino acid Glycine (G) 204.
[0101] Figure 3 illustrates amino acid-wise classification of atoms
of amino acids in the
reference amino acid sequence 202, also referred to herein as "atom
classification 300." Specific
types of amino acids, among the twenty natural amino acids listed in column
302, may repeat in
a protein That is, a particular type of amino acid may occur more than once in
a protein Proteins
may also have some undetermined amino acids that are categorized by a twenty-
first stop or gap
amino acid category. The right column in Figure 3 contains counts of alpha-
carbon (Ca) atoms
from different amino acids.
[0102] Specifically, Figure 3 shows amino acid-wise classification
of alpha-carbon (Ca)
atoms of the amino acids in the reference amino acid sequence 202. Column 308
of Figure 3 lists
the total number of alpha-carbon atoms observed for the reference amino acid
sequence 202 in
each of the twenty-one amino acid categories. For example, column 308 lists
eleven alpha-
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carbon atoms observed for the Alanine (A) amino acid category. Since each
amino acid has only
one alpha-carbon atom, this means that Alanine occurs 11 times in the
reference amino acid
sequence 202. In another example, Arginine (R) occurs thirty-five times in the
reference amino
acid sequence 202. The total number of alpha-carbon atoms across the twenty-
one amino acid
categories is eight hundred and twenty-eight.
101031 Figure 4 illustrates amino acid-wise attribution of 3D
atomic coordinates of the alpha-
carbon atoms of the reference amino acid sequence 202 based on the atom
classification 300 in
Figure 3. This is referred to herein as "atomic coordinates bucketing 400." In
Figure 4, lists 404-
440 tabulate the 3D atomic coordinates of the alpha-carbon atoms bucketed to
each of the
twenty-one amino acid categories.
101041 In the illustrated implementation, the bucketing 400 in
Figure 4 follows the
classification 300 of Figure 3. For example, in Figure 3, the Alanine amino
acid category has
eleven alpha-carbon atoms, and therefore, in Figure 4, the Alanine amino acid
category has
eleven 3D atomic coordinates of the corresponding eleven alpha-carbon atoms
from Figure 3.
This classification-to-bucketing logic flows from Figure 3 to Figure 4 for
other amino acid
categories too. However, this classification-to-bucketing logic is only for
representational
purposes, and, in other implementations, the technology disclosed need not
perform the
classification 300 and the bucketing 400 to locate the voxel-wise nearest
atoms, and may
perform fewer, additional, or different steps. For example, in some
implementations, the
technology disclosed can locate the voxel-wise nearest atoms by using a sort
and search
algorithm that returns the voxel-wise nearest atoms from one or more databases
in response to a
search query configured to accept query parameters like sort criteria (e.g.,
amino acid-wise,
atomic element-wise, atom type-wise), the predefined maximum scan radius, and
the type of
distances (e.g., Euclidean, Mahalanobis, normalized, unnormalized). In various
implementations
of the technology disclosed, a plurality of sort and search algorithms from
the current or future
technical field can be analogous used by a person skilled in the art to locate
the voxel-wise
nearest atoms.
101051 In Figure 4, the 3D atomic coordinates are represented by
cartesi an coordinates x, y,
z, but any type of coordinate system may be used, such as spherical or
cylindrical coordinates,
and claimed subject matter is not limited in this respect. In some
implementations, one or more
databases may include information regarding the 3D atomic coordinates of the
alpha-carbon
atoms and other atoms of amino acids in proteins. Such databases may be
searchable by specific
proteins.
101061 As discussed above, the voxels and the voxel grid are 3D
entities. However, for
clarity's sake, the drawings depict, and the description discusses the voxels
and the voxel grid in
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a two-dimensional (2D) format. For example, a 3x3x3 voxel grid of twenty-seven
voxels is
depicted and described herein as a 3x3 2D pixel grid with nine 2D pixels. A
person skilled in the
art will appreciate that the 2D format is used only for representational
purposes and is intended
to cover the 3D counterparts (i.e., 2D pixels represent 3D voxels and 2D pixel
grid represents 3D
voxel grid). Also, the drawings are also not scale. For example, voxels of
size two angstrom (A)
are depicted using a single pixel.
Voxel-Wise Distance Calculation
101071 Figure 5 schematically illustrates a process of determining
voxel-wise distance
values, also referred to herein as "voxel-wise distance calculation 500." In
the illustrated
example, the voxel-wise distance values are calculated only for the Alanine
(A) distance channel.
However, the same distance calculation logic is executed for each of the
twenty-one amino acid
categories to generate twenty-one amino acid-wise distance channels and can be
further
expanded to other atom types like beta-carbon atoms and other atomic elements
like oxygen,
nitrogen, and hydrogen, as discussed above with respect to Figure 1. In some
implementations,
the atoms are randomly rotated prior to the distance calculation to make the
training of the
pathogenicity classifier invariant to atom orientation.
101081 In Figure 5, a voxel grid 522 has nine voxels 514 identified
with indices (1, 1), (1, 2),
(1, 3), (2, 1), (2, 2), (2, 3), (3, 1), (3, 2), and (3, 3). The voxel grid 522
is centered, for example,
at the 3D atomic coordinate 532 of the alpha-carbon atom of the Glycine (G)
amino acid at
position 16 in the reference amino acid sequence 202 because, in the
alternative amino acid
sequence 212, the position 16 experiences the variant that mutates the Glycine
(G) amino acid to
the Alanine (A) amino acid, as discussed above with respect to Figure 2. Also,
the center of the
voxel grid 522 coincides with the center of voxel (2, 2).
101091 The centered voxel grid 522 is used for the voxel-wise
distance calculation for each
of the twenty-one amino acid-wise distance channels. Starting, for example,
with the Alanine (A)
distance channel, distances between the 3D coordinates of respective centers
of the nine voxels
514 and the 3D atomic coordinates 402 of the eleven Alanine alpha-carbon atoms
are measured
to locate a nearest Alanine alpha-carbon atom for each of the nine voxels 514.
Then, nine
distance values for nine distances between the nine voxels 514 and the
respective nearest Alanine
alpha-carbon atoms are used to construct the Alanine distance channel. The
resulting Alanine
distance channel arranges the nine Alanine distance values in the same order
as the nine voxels
514 in the voxel grid 522.
101101 The above process is executed for each of the twenty-one
amino acid categories. For
example, the centered voxel grid 522 is similarly used to calculate the
Arginine (R) distance
channel, such that distances between the 3D coordinates of respective centers
of the nine voxels
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514 and the 3D atomic coordinates 404 of the thirty-five Arginine alpha-carbon
atoms are
measured to locate a nearest Arginine alpha-carbon atom for each of the nine
voxels 514. Then,
nine distance values for nine distances between the nine voxels 514 and the
respective nearest
Arginine alpha-carbon atoms are used to construct the Arginine distance
channel. The resulting
Arginine distance channel arranges the nine Arginine distance values in the
same order as the
nine voxels 514 in the voxel grid 522. The twenty-one amino acid-wise distance
channels are
voxel-wise encoded to form a distance channel tensor.
[0111] Specifically, in the illustrated example, a distance 512 is
between the center of voxel
(1, 1) of voxel grid 522 and the nearest alpha-carbon (Ca) atom, which is the
Cam atom in list
402. Accordingly, the value assigned to voxel (1, 1) is the distance 512. In
another example, the
Ca" atom is the nearest Ca atom to the center of voxel (1, 2). Accordingly,
the value assigned to
voxel (1, 2) is the distance between the center of voxel (1, 2) and the Ca'
atom. In still another
example, the Cam atom is the nearest Ca atom to the center of voxel (2, 1).
Accordingly, the
value assigned to voxel (2, 1) is the distance between the center of voxel (2,
1) and the CaA6
atom. In still another example, the CaA6 atom is also the nearest Ca atom to
the center of voxels
(3, 2) and (3, 3). Accordingly, the value assigned to voxel (3, 2) is the
distance between the
center of voxel (3, 2) and the CaA6 atom and the value assigned to voxel (3,
3) is the distance
between the center of voxel (3, 3) and the CaA6 atom. In some implementations,
the distance
values assigned to the voxels 514 may be normalized distances. For example,
the distance value
assigned to voxel (1, 1) may be the distance 512 divided by a maximum distance
502 (predefined
maximum scan radius). In some implementations, the nearest-atom distances may
be Euclidean
distances and the nearest-atom distances may be normalized by dividing the
Euclidean distances
with a maximum nearest-atom distance (e.g., such as the maximum distance 502).
101121 As described above, for amino acids having alpha-carbon
atoms, the distances may be
nearest-alpha-carbon atom distances from corresponding voxel centers to
nearest alpha-carbon
atoms of the corresponding amino acids. Additionally, for amino acids having
beta-carbon
atoms, the distances may be nearest-beta-carbon atom distances from
corresponding voxel
centers to nearest beta-carbon atoms of the corresponding amino acids
Similarly, for amino
acids having backbone atoms, the distances may be nearest-backbone atom
distances from
corresponding voxel centers to nearest backbone atoms of the corresponding
amino acids.
Similarly, for amino acids having sidechain atoms, the distances may be
nearest-sidechain atom
distances from corresponding voxel centers to nearest sidechain atoms of the
corresponding
amino acids. In some implementations, the distances additionally/alternatively
can include
distances to second, third, fourth nearest atoms, and so on.
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Amino Acid-Wise Distance Channels
101131 Figure 6 shows an example of twenty-one amino acid-wise
distance channels 600.
Each column in Figure 6 corresponds to a respective one of the twenty-one
amino acid-wise
distance channels 602-642. Each amino acid-wise distance channel comprises a
distance value
for each of the voxels 514 of the voxel grid 522. For example, the amino acid-
wise distance
channel 602 for Alanine (A) comprises distance values for respective ones of
the voxels 514 of
the voxel grid 522. As mentioned above, the voxel grid 522 is 3D grid of
volume 3x3x3 and
comprises twenty-seven voxels. Likewise, though Figure 6 illustrates the
voxels 514 in two
dimensions (e.g., nine voxels of a 3x3 grid), each amino acid-wise distance
channel may
comprise twenty-seven voxel-wise distance values for the 3x3x3 voxel grid.
Directionality Encoding
101141 In some implementations, the technology disclosed uses a
directionality parameter to
specify the directionality of the reference amino acids in the reference amino
acid sequence 202.
In some implementations, the technology disclosed uses the directionality
parameter to specify
the directionality of the alternative amino acids in the alternative amino
acid sequence 212. In
some implementations, the technology disclosed uses the directionality
parameter to specify the
position in the protein 200 that experiences the target variant at the amino
acid level.
101151 As discussed above, all the distance values in the twenty-
one amino acid-wise
distance channels 602-642 are measured from respective nearest atoms to the
voxels 514 in the
voxel grid 522. These nearest atoms originate from one of the reference amino
acids in the
reference amino acid sequence 202. These originating reference amino acids,
which contain the
nearest atoms, can be classified into two categories: (1) those originating
reference amino acids
that precede the variant-experiencing reference amino acid 204 in the
reference amino acid
sequence 202 and (2) those originating reference amino acids that succeed the
variant-
experiencing reference amino acid 204 in the reference amino acid sequence
202. The
originating reference amino acids in the first category can be called
preceding reference amino
acids. The originating reference amino acids in the second category can be
called succeeding
reference amino acids.
101161 The directionality parameter is applied to those distance
values in the twenty-one
amino acid-wise distance channels 602-642 that are measured from those nearest
atoms that
originate from the preceding reference amino acids. In one implementation, the
directionality
parameter is multiplied with such distance values. The directionality
parameter can be any
number, such as -1.
101171 As a result of the application of the directionality
parameter, the twenty-one amino
acid-wise distance channels 600 include some distance values that indicate to
the pathogenicity
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classifier which end of the protein 200 is the start terminal and which end is
the end terminal.
This also allows the pathogenicity classifier to reconstruct a protein
sequence from the 3D
protein structure information supplied by the distance channels and the
reference and allele
channels.
Distance Channel Tensor
101181 Figure 7 is a schematic diagram of a distance channel tensor
700. Distance channel
tensor 700 is a voxelized representation of the amino acid-wise distance
channels 600 from
Figure 6. In the distance channel tensor 700, the twenty-one amino acid-wise
distance channels
602-642 are concatenated voxel-wise, like RGB channels of a color image. The
voxelized
dimensionality of the distance channel tensor 700 is 21x3x3x3 (where 21
denotes the twenty-one
amino acid categories and 3x3x3 denotes the 3D voxel grid with twenty-seven
voxels); although
Figure 7 is a 2D depiction of dimensionality 21x3x3.
One-Hot Encodings
101191 Figure 8 shows one-hot encodings 800 of the reference amino
acid 204 and the
alternative amino acid 214. In Figure 8, left column is a one-hot encoding 802
of the reference
amino acid Glycine (G) 204, with one for the Glycine amino acid category and
zeros for all other
amino acid categories. In Figure 8, right column is a one-hot encoding 804 of
the
variant/alternative amino acid Alanine (A) 214, with one for the Alanine amino
acid category
and zeros for all other amino acid categories.
101201 Figure 9 is a schematic diagram of a voxelized one-hot
encoded reference amino acid
902 and a voxelized one-hot encoded variant/alternative amino acid 912. The
voxelized one-hot
encoded reference amino acid 902 is a voxelized representation of the one-hot
encoding 802 of
the reference amino acid Glycine (G) 204 from Figure 8. The voxelized one-hot
encoded
alternative amino acid 912 is a voxelized representation of the one-hot
encoding 804 of the
variant/alternative amino acid Alanine (A) 214 from Figure 8. The voxelized
dimensionality of
the voxelized one-hot encoded reference amino acid 902 is 21x1x1x1 (where 21
denotes the
twenty-one amino acid categories); although Figure 9 is a 2D depiction of
dimensionality
21x1xl. Similarly, the voxelized dimensionality of the voxelized one-hot
encoded alternative
amino acid 912 is 21x1x1x1 (where 21 denotes the twenty-one amino acid
categories); although
Figure 9 is a 2D depiction of dimensionality 21x lx1
Reference Allele Tensor
101211 Figure 10 schematically illustrates a concatenation process
1000 that voxel-wise
concatenates the distance channel tensor 700 of Figure 7 and a reference
allele tensor 1004. The
reference allele tensor 1004 is a voxel-wise aggregation
(repetition/cloning/replication) of the
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voxelized one-hot encoded reference amino acid 902 from Figure 9. That is,
multiple copies of
the voxelized one-hot encoded reference amino acid 902 are voxel-wise
concatenated according
with each other to the spatial arrangement of the voxels 514 in the voxel grid
522, such that the
reference allele tensor 1004 has a corresponding copy of the voxelized one-hot
encoded
reference amino acid 910 for each of the voxels 514 in the voxel grid 522.
[0122] The concatenation process 1000 produces a concatenated
tensor 1010. The voxelized
dimensionality of the reference allele tensor 1004 is 21x3x3x3 (where 21
denotes the twenty-one
amino acid categories and 3x3x3 denotes the 3D voxel grid with twenty-seven
voxels); although
Figure 10 is a 2D depiction of the reference allele tensor 1004 having
dimensionality 21x3x3.
The voxelized dimensionality of the concatenated tensor 1010 is 42x3x3x3;
although Figure 10
is a 2D depiction of the concatenated tensor 1010 having dimensionality
42x3x3.
Alternative Allele Tensor
[0123] Figure 11 schematically illustrates a concatenation process
1100 that voxel-wise
concatenates the distance channel tensor 700 of Figure 7, the reference allele
tensor 1004 of
Figure 10, and an alternative allele tensor 1104. The alternative allele
tensor 1104 is a voxel-wise
aggregation (repetition/cloning/replication) of the voxelized one-hot encoded
alternative amino
acid 912 from Figure 9. That is, multiple copies of the voxelized one-hot
encoded alternative
amino acid 912 are voxel-wise concatenated with each other according to the
spatial arrangement
of the voxels 514 in the voxel grid 522, such that the alternative allele
tensor 1104 has a
corresponding copy of the voxelized one-hot encoded alternative amino acid 910
for each of the
voxels 514 in the voxel grid 522.
[0124] The concatenation process 1100 produces a concatenated
tensor 1110. The voxelized
dimensionality of the alternative allele tensor 1104 is 21x3x3x3 (where 21
denotes the twenty-
one amino acid categories and 3x3x3 denotes the 3D voxel grid with twenty-
seven voxels);
although Figure 11 is a 2D depiction of the alternative allele tensor 1104
having dimensionality
21x3x3. The voxelized dimensionality of the concatenated tensor 1110 is
63x3x3x3; although
Figure 11 is a 2D depiction of the concatenated tensor 1110 having
dimensionality 63x3x3.
[0125] In some implementations, the runtime logic 184 processes the
concatenated tensor
1110 through the pathogenicity classifier to determine a pathogenicity of the
variant/alternative
amino acid Alanine (A) 214, which is in turn inferred as a pathogenicity
determination of the
underlying nucleotide variant that creates the variant/alternative amino acid
Alanine (A) 214.
Evolutionary Conservation Channels
[0126] Predicting the functional consequences of variants relies at
least in part on the
assumption that crucial amino acids for protein families are conserved through
evolution due to
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negative selection (i.e., amino acid changes at these sites were deleterious
in the past), and that
mutations at these sites have an increased likelihood of being pathogenic
(causing disease) in
humans. In general, homologous sequences of a target protein are collected and
aligned, and a
metric of conservation is computed based on the weighted frequencies of
different amino acids
observed in the target position in the alignment.
101271 Accordingly, the technology disclosed concatenates the
distance channel tensor 700,
the reference allele tensor 1004, and the alternative allele tensor 1104 with
evolutionary
channels. One example of the evolutionary channels is pan-amino acid
conservation frequencies.
Another example of the evolutionary channels is per-amino acid conservation
frequencies.
101281 In some implementations, the evolutionary channels are
constructed using position
weight matrices (PWMs). In other implementations, the evolutionary channels
are constructed
using position specific frequency matrices (PSFMs). In yet other
implementations, the
evolutionary channels are constructed using computational tools like SIFT,
PolyPhen, and
PANTHER-PSEC. In yet other implementations, the evolutionary channels are
preservation
channels based on evolutionary preservation. Preservation is related to
conservation, as it also
reflects the effect of negative selection that has acted to prevent
evolutionary change at a given
site in a protein.
Pan-Amino Acid Evolutionary Profiles
101291 Figure 12 is a flow diagram that illustrates a process 1200
of a system for determining
and assigning pan-amino acid conservation frequencies of nearest atoms to
voxels (voxelizing),
in accordance with one implementation of the technology disclosed. Figures 12,
13, 14, 15, 16,
17, and 18 are discussed in tandem.
101301 At step 1202, a similar sequence finder 1204 of the system
retrieves amino acid
sequences that are similar (homologous) to the reference amino acid sequence
202. The similar
amino acid sequences can be selected from multiple species like primates,
mammals, and
vertebrates.
101311 At step 1212, an aligner 1214 of the system position-wise
aligns the reference amino
acid sequence 202 with the similar amino acid sequences, i.e., the aligner
1214 performs a multi-
sequence alignment. Figure 14 shows an example multi-sequence alignment 1400
of the
reference amino acid sequence 202 across a ninety-nine species. In some
implementations, the
multi-sequence alignment 1400 can be partitioned, for example, to generate a
first position
frequency matrix 1402 for primates, a second position frequency matrix 1412
for mammals, and
a third position frequency matrix 1422 for primates. In other implementations,
a single position
frequency matrix is generated across the ninety-nine species.
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101321 At step 1222, a pan-amino acid conservation frequency
calculator 1224 of the system
uses the multi-sequence alignment to determine pan-amino acid conservation
frequencies of the
reference amino acids in the reference amino acid sequence 202.
101331 At step 1232, a nearest atom finder 1234 of the system finds
nearest atoms to the
voxels 514 in the voxel grid 522. In some implementations, the search for the
voxel-wise nearest
atoms may not be confined to any particular amino acid category or atom type.
That is, the
voxel-wise nearest atoms can be selected across the amino acid categories and
the amino acid
types, as long as they are the most proximate atoms to the respective voxel
centers. In other
implementations, the search for the voxel-wise nearest atoms may be confined
to only a
particular atom category, such as only to a particular atomic element like
oxygen, nitrogen, and
hydrogen, or only to alpha-carbon atoms, or only to beta-carbon atoms, or only
to sidechain
atoms, or only to backbone atoms.
101341 At step 1242, an amino acid selector 1244 of the system
selects those reference amino
acids in the reference amino acid sequence 202 that contain the nearest atoms
identified at the
step 1232. Such reference amino acids can be called nearest reference amino
acids. Figure 13
shows an example of locating nearest atoms 1302 to the voxels 514 in the voxel
grid 522 and
respectively mapping nearest reference amino acids 1312 that contain the
nearest atoms 1302 to
the voxels 514 in the voxel grid 522. This is identified in Figure 13 as
"voxels-to-nearest amino
acids mapping 1300."
101351 At step 1252, a voxelizer 1254 of the system voxelizes pan-
amino acid conservation
frequencies of the nearest reference amino acids. Figure 15 shows an example
of determining a
pan-amino acid conservation frequencies sequence for the first voxel (1, 1) in
the voxel grid 522,
also referred to herein as -per-voxel evolutionary profile determination
1500."
101361 Turning to Figure 13, the nearest reference amino acid that
was mapped to the first
voxel (1, 1) is Aspartic acid (D) amino acid at position 15 in the reference
amino acid sequence
202. Then, the multi-sequence alignment of the reference amino acid sequence
202 with, for
example, ninety-nine homologous amino acid sequences of the ninety-nine
species is analyzed at
position 15 Such a position-specific and cross-species analysis reveals how
many instances of
amino acids from each of the twenty-one amino acid categories are found at
position 15 across
the hundred aligned amino acid sequences (i.e., the reference amino acid
sequence 202 plus the
ninety-nine homologous amino acid sequences).
101371 In the example illustrated in Figure 15, the Aspartic acid
(D) amino acid is found at
position 15 in ninety-six out of the hundred aligned amino acid sequences. So,
the Aspartic acid
amino acid category 1504 is assigned a pan-amino acid conservation frequency
of 0.96.
Similarly, in the illustrated example, the Valine (V) acid amino acid is found
at position 15 in
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four out of the hundred aligned amino acid sequences. So, the Valine acid
amino acid category
1514 is assigned a pan-amino acid conservation frequency of 0.04. Since no
instances of amino
acids from other amino acid categories are detected at position 15, the
remaining amino acid
categories are assigned a pan-amino acid conservation frequency of zero. This
way, each of the
twenty-one amino acid categories is assigned a respective pan-amino acid
conservation
frequency, which can be encoded in the pan-amino acid conservation frequencies
sequence 1502
for the first voxel (1, 1).
[0138] Figure 16 shows respective pan-amino acid conservation
frequencies 1612-1692
determined for respective ones of the voxels 514 in the voxel grid 522 using
the position
frequency logic described in Figure 15, also referred to herein as "voxels-to-
evolutionary
profiles mapping 1600."
[0139] Per-voxel evolutionary profiles 1602 are then used by the
voxelizer 1254 to generate
voxelized per-voxel evolutionary profiles 1700, illustrated in Figure 17.
Often, each of the
voxels 514 in the voxel grid 522 has a different pan-amino acid conservation
frequencies
sequence and therefore a different voxelized per-voxel evolutionary profile
because the voxels
are regularly mapped to different nearest atoms and therefore to different
nearest reference
amino acids. Of course, when two or more voxels have a same nearest atom and
thereby a same
nearest reference amino acid, a same pan-amino acid conservation frequencies
sequence and a
same voxelized per-voxel evolutionary profile is assigned to each of the two
or more voxels.
[0140] Figure 18 depicts example of an evolutionary profiles tensor
1800 in which the
voxelized per-voxel evolutionary profiles 1700 are voxel-wise concatenated
with each other
according to the spatial arrangement of the voxels 514 in the voxel grid 522.
The voxelized
dimensionality of the evolutionary profiles tensor 1800 is 21x3x3x3 (where 21
denotes the
twenty-one amino acid categories and 3x3x3 denotes the 3D voxel grid with
twenty-seven
voxels); although Figure 18 is a 2D depiction of the evolutionary profiles
tensor 1800 having
dimensionality 21x3x3.
101411 At step 1262, the concatenator 174 voxel-wise concatenates
the evolutionary profiles
tensor 1800 with the distance channel tensor 700 In some implementations, the
evolutionary
profiles tensor 1800 is voxel-wise concatenated with the concatenator tensor
1110 to generate a
further concatenated tensor of dimensionality 84x3x3x3 (not shown).
[0142] At step 1272, the runtime logic 184 processes the further
concatenated tensor of
dimensionality 84x3x3x3 through the pathogenicity classifier to determine the
pathogenicity of
the target variant, which is in turn inferred as a pathogenicity determination
of the underlying
nucleotide variant that creates the target variant at the amino acid level.
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Per-Amino Acid Evolutionary Profiles
[0143] Figure 19 is a flow diagram that illustrates a process 1900
of a system for determining
and assigning per-amino acid conservation frequencies of nearest atoms to
voxels (voxelizing).
In Figure 19, the steps 1202 and 1212 are the same as Figure 12.
[0144] At step 1922, a per-amino acid conservation frequency
calculator 1924 of the system
uses the multi-sequence alignment to determine per-amino acid conservation
frequencies of the
reference amino acids in the reference amino acid sequence 202.
[0145] At step 1932, a nearest atom finder 1934 of the system
finds, for each of the voxels
514 in the voxel grid 522, twenty-one nearest atoms across each of the twenty-
one amino acid
categories. Each of the twenty-one nearest atoms is different from each other
because they are
selected from different amino acid categories. This leads to the selection of
twenty-one unique
nearest reference amino acids for a particular voxel, which in turn leads to
generation of twenty-
one unique position frequency matrices for the particular voxel, and which in
turn leads to
determination of twenty-one unique per-amino acid conservation frequencies for
the particular
voxel.
[0146] At step 1942, an amino acid selector 1944 of the system
selects, for each of the
voxels 514 in the voxel grid 522, twenty-one reference amino acids in the
reference amino acid
sequence 202 that contain the twenty-one nearest atoms identified at the step
1932. Such
reference amino acids can be called nearest reference amino acids.
[0147] At step 1952, a voxelizer 1954 of the system voxelizes pen-
amino acid conservation
frequencies of the twenty-one nearest reference amino acids identified for the
particular voxel at
the step 1942. The twenty-one nearest reference amino acids are necessarily
located at twenty-
one different positions in the reference amino acid sequence 202 because they
correspond to
different underlying nearest atoms. Accordingly, for the particular voxel,
twenty-one position
frequency matrices can be generated for the twenty-one nearest reference amino
acids. The
twenty-one position frequency matrices can be generated across multiple
species whose
homologous amino acid sequences are position-wise aligned with the reference
amino acid
sequence 202, as discussed above with respect to Figures 12 to 15
[0148] Then, using the twenty-one position frequency matrices,
twenty-one position-specific
conservation scores can be calculated for the twenty-one nearest reference
amino acids identified
for the particular voxel. These twenty-one position-specific conservation
scores form the pen-
amino acid conservation frequencies for the particular voxel, similar to the
pan-amino acid
conservation frequencies sequence 1502 in Figure 12; except the sequence 1502
has many zero
entries, whereas each element (feature) in a per-amino acid conservation
frequencies sequence
has a value (e.g., a floating point number) because the twenty-one nearest
reference amino acids
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across the twenty-one amino acid categories necessarily have different
positions that yield
different position frequency matrices and thereby different per-amino acid
conservation
frequencies.
[0149] The above process is executed for each of the voxels 514 in
the voxel grid 522, and
the resulting voxel-wise per-amino acid conservation frequencies voxelized,
tensorized,
concatenated, and processed for pathogenicity determination similar to the pan-
amino acid
conservation frequencies discussed with respect to Figures 12 to 18.
Annotation Channels
[0150] Figure 20 shows various examples of voxelized annotation
channels 2000 that are
concatenated with the distance channel tensor 700. In some implementations,
the voxelized
annotation channels are one-hot indicators for different protein annotations,
for example whether
an amino acid (residue) is part of a transmembrane region, a signal peptide,
an active site, or any
other binding site, or whether the residue is subject to posttranslational
modifications, PathRatio
(See Pei P, Zhang A: A Topological Measurement for Weighted Protein
Interaction Network.
CSB 2005, 268-278.), etc. Additional examples of the annotation channels can
be found below
in the Particular Implementations section and in the Claims.
[0151] The voxelized annotation channels are arranged voxel-wise
such that the voxels can
have a same annotation sequence like the voxelized reference allele and
alternative allele
sequences (e.g., annotation channels 2002, 2004, 2006), or the voxels can have
respective
annotation sequences like the voxelized per-voxel evolutionary profiles 1700
(e.g., annotation
channels 2012, 2014, 2016 (as indicated by different colors)).
[0152] The annotation channels are voxelized, tensorized,
concatenated, and processed for
pathogenicity determination similar to the pan-amino acid conservation
frequencies discussed
with respect to Figures 12 to 18.
Structural Confidence Channels
[0153] The technology disclosed can also concatenate various
voxelized structural
confidence channels with the distance channel tensor 700. Some examples of the
structure
confidence channels include GMQE score (provided by SwissModel); B-factor;
temperature
factor column of homology models (indicates how well a residue satisfies
(physical) constraints
in the protein structure); normalized number of aligning template proteins for
the residue nearest
to the center of a voxel (alignments provided by Efflpred, e.g., voxel is
nearest to a residue at
which 3 of 6 template structures align, signifying that the feature has value
3/6=0.5; minimum,
maximum, and mean TM-scores; and predicted TM-scores of the template protein
structures that
align to the residue that is nearest to a voxel (continuing the example above,
assume the 3
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template structure has TM-scores 0.5, 0.5 and 1.5, then the minimum is 0.5,
the mean is 2/3, and
the maximum is 1.5). The TM-scores can be provided per protein template by HI-
Ipred.
Additional examples of the structural confidence channels can be found below
in the Particular
Implementations section and in the Claims.
101541 The voxelized structural confidence channels are arranged
voxel-wise such that the
voxels can have a same structural confidence sequence like the voxelized
reference allele and
alternative allele sequences, or the voxels can have respective structural
confidence sequences
like the voxelized per-voxel evolutionary profiles 1700.
101551 The structural confidence channels are voxelized,
tensorized, concatenated, and
processed for pathogenicity determination similar to the pan-amino acid
conservation
frequencies discussed with respect to Figures 12 to 18.
Pathogenicity Classifier
101561 Figure 21 illustrates different combinations and
permutations of input channels that
can be provided as inputs 2102 to a pathogenicity classifier 2108 for a
pathogenicity
determination 2106 of a target variant. One of the inputs 2102 can be distance
channels 2104
generated by a distance channels generator 2272. Figure 22 shows different
methods of
calculating the distance channels 2104. In one implementation, the distance
channels 2104 are
generated based on distances 2202 between voxel centers and atoms across a
plurality of atomic
elements irrespective of amino acids. In some implementations, the distances
2202 are
normalized by a maximum scan radius to generate normalized distances 2202a. In
another
implementation, the distance channels 2104 are generated based on distances
2212 between
voxel centers and alpha-carbon atoms on an amino acid-basis. In some
implementations, the
distances 2212 are normalized by the maximum scan radius to generate
normalized distances
2212a. In yet another implementation, the distance channels 2104 are generated
based on
distances 2222 between voxel centers and beta-carbon atoms on an amino acid-
basis. In some
implementations, the distances 2222 are normalized by the maximum scan radius
to generate
normalized distances 2222a. In yet another implementation, the distance
channels 2104 are
generated based on distances 2232 between voxel centers and side chain atoms
on an amino
acid-basis. In some implementations, the distances 2232 are normalized by the
maximum scan
radius to generate normalized distances 2232a. In yet another implementation,
the distance
channels 2104 are generated based on distances 2242 between voxel centers and
backbone atoms
on an amino acid-basis. In some implementations, the distances 2242 are
normalized by the
maximum scan radius to generate normalized distances 2242a. In yet another
implementation,
the distance channels 2104 are generated based on distances 2252 (one feature)
between voxel
centers and the respective nearest atoms irrespective of atom type and amino
acid type. In yet
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another implementation, the distance channels 2104 are generated based on
distances 2262 (one
feature) between voxel centers and atoms from non-standard amino acids. In
some
implementations, the distances between the voxels and the atoms are calculated
based on polar
coordinates of the voxels and the atoms. The polar coordinates are
parameterized by angles
between the voxels and the atoms. In one implementation, this angel
information is used to
generate an angle channel for the voxels (i.e., independent of the distance
channels). In some
implementations, angles between a nearest atom and neighboring atoms (e.g.,
backbone atoms)
can be used as features that are encoded with the voxels.
[0157] Another one of the inputs 2102 can be a feature 2114
indicating missing atoms within
a specified radius.
[0158] Another one of the inputs 2102 can be one-hot encoding 2124
of the reference amino
acid. Another one of the inputs 2102 can be one-hot encoding 2134 of the
variant/alternative
amino acid.
[0159] Another one of the inputs 2102 can be evolutionary channels
2144 generated by an
evolutionary profiles generator 2372, shown in Figure 23. In one
implementation, the
evolutionary channels 2144 can be generated based on pan-amino acid
conservation frequencies
2302. In another implementation, the evolutionary channels 2144 can be
generated based on pan-
amino acid conservation frequencies 2312.
[0160] Another one of the inputs 2102 can be a feature 2154
indicating missing residue or
missing evolutionary profile.
[0161] Another one of the inputs 2102 can be annotations channels
2164 generated by an
annotations generator 2472, shown in Figure 24. In one implementation, the
annotations
channels 2154 can be generated based on molecular processing annotations 2402.
In another
implementation, the annotations channels 2154 can be generated based on
regions annotations
2412. In yet another implementation, the annotations channels 2154 can be
generated based on
sites annotations 2422. In yet another implementation, the annotations
channels 2154 can be
generated based on Amino acid modifications annotations 2432. In yet another
implementation,
the annotations channels 2154 can be generated based on secondary structure
annotations 2442
In yet another implementation, the annotations channels 2154 can be generated
based on
experimental information annotations 2452.
[0162] Another one of the inputs 2102 can be structure confidence
channels 2174 generated
by a structure confidence generator 2572, shown in Figure 25. In one
implementation, the
structure confidence 2174 can be generated based on global model quality
estimations (GMQEs)
2502. In another implementation, the structure confidence 2174 can be
generated based on
qualitative model energy analysis (QMEAN) scores 2512. In yet another
implementation, the
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structure confidence 2174 can be generated based on temperature factors 2522.
In yet another
implementation, the structure confidence 2174 can be generated based on
template modeling
scores 2542. Examples of the template modeling scores 2542 include minimum
template
modeling scores 2542a, mean template modeling scores 2542b, and maximum
template
modeling scores 2542c.
101631 A person skilled in the art will appreciate that any
permutation and combination of
the input channels can be concatenated into an input for processing through
the pathogenicity
classifier 2108 for the pathogenicity determination 2106 of the target
variant. In some
implementations, only a subset of the input channels may be concatenated. The
input channels
can be concatenated in any order. In one implementation, the input channels
can be concatenated
into a single tensor by a tensor generator (input encoder) 2110. This single
tensor can then be
provided as input to the pathogenicity classifier 2108 for the pathogenicity
determination 2106 of
the target variant.
101641 In one implementation, the pathogenicity classifier 2108
uses convolutional neural
networks (CNNs) with a plurality of convolution layers. In another
implementation, the
pathogenicity classifier 2108 uses recurrent neural networks (RNNs) such as a
long short-term
memory networks (LSTMs), bi-directional LSTMs (Bi-LSTMs), and gated recurrent
units
(GRU)s. In yet another implementation, the pathogenicity classifier 2108 uses
both the CNNs
and the RNNs. In yet another implementation, the pathogenicity classifier 2108
uses graph-
convolutional neural networks that model dependencies in graph-structured
data. In yet another
implementation, the pathogenicity classifier 2108 uses variational
autoencoders (VAEs). In yet
another implementation, the pathogenicity classifier 2108 uses generative
adversarial networks
(GANs). In yet another implementation, the pathogenicity classifier 2108 can
also be a language
model based, for example, on self-attention such as the one implemented by
Transformers and
BERTs.
101651 In yet other implementations, the pathogenicity classifier
2108 can use 1D
convolutions, 2D convolutions, 3D convolutions, 4D convolutions, 5D
convolutions, dilated or
atrous convolutions, transpose convolutions, depthwi se separable
convolutions, poi ntwi se
convolutions, 1 x 1 convolutions, group convolutions, flattened convolutions,
spatial and cross-
channel convolutions, shuffled grouped convolutions, spatial separable
convolutions, and
deconvolutions. It can use one or more loss functions such as logistic
regression/log loss, multi-
class cross-entropy/softmax loss, binary cross-entropy loss, mean-squared
error loss, Li loss, L2
loss, smooth Li loss, and Huber loss. It can use any parallelism, efficiency,
and compression
schemes such TFRecords, compressed encoding (e.g., PNG), sharding, parallel
calls for map
transformation, batching, prefetching, model parallelism, data parallelism,
and
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synchronous/asynchronous stochastic gradient descent (SGD). It can include
upsampling layers,
downsampling layers, recurrent connections, gates and gated memory units (like
an LSTM or
GRU), residual blocks, residual connections, highway connections, skip
connections, peephole
connections, activation functions (e.g., non-linear transformation functions
like rectifying linear
unit (ReLU), leaky ReLU, exponential liner unit (ELU), sigmoid and hyperbolic
tangent (tanh)),
batch normalization layers, regularization layers, dropout, pooling layers
(e.g., max or average
pooling), global average pooling layers, attention mechanisms, and gaussian
error linear unit.
[0166] The pathogenicity classifier 2108 is trained using
backpropagation-based gradient
update techniques. Example gradient descent techniques that can be used for
training the
pathogenicity classifier 2108 include stochastic gradient descent, batch
gradient descent, and
mini-batch gradient descent. Some examples of gradient descent optimization
algorithms that
can be used to train the pathogenicity classifier 2108 are Momentum, Nesterov
accelerated
gradient, Adagrad, Adadelta, RMSprop, Adam, AdaMax, Nadam, and AMSGrad. In
other
implementations, the pathogenicity classifier 2108 can be trained by
unsupervised learning,
semi-supervised learning, self-learning, reinforcement learning, multitask
learning, multimodal
learning, transfer learning, knowledge distillation, and so on.
[0167] Figure 26 shows an example processing architecture 2600 of
the pathogenicity
classifier 2108, in accordance with one implementation of the technology
disclosed. The
processing architecture 2600 includes a cascade of processing modules 2606,
2610, 2614, 2618,
2622, 2626, 2630, 2634, 2638, and 2642 each of which can include 1D
convolutions (1x1x1
CON V), 3D convolutions (3x3x3 CON V), ReLU non-linearity, and batch
normalization (BN).
Other examples of the processing modules include fully-connected (FC) layers,
a dropout layer,
a flattening layer, and a final softmax layer that produces exponentially
normalized scores for the
target variant belonging to a benign class and a pathogenic class. In Figure
26, "64" denotes a
number of convolution filters applied by a particular processing module. In
Figure 26, the size of
an input voxel 2602 is 15x15x15x8. Figure 26 also shows respective volumetric
dimensionalities
of the intermediate inputs 2604, 2608, 2612, 2616, 2620, 2624, 2628, 2632,
2636, and 2640
generated by the processing architecture 2600
[0168] Figure 27 shows an example processing architecture 2700 of
the pathogenicity
classifier 2108, in accordance with one implementation of the technology
disclosed. The
processing architecture 2700 includes a cascade of processing modules 2708,
2714, 2720, 2726,
2732, 2738, 2744, 2750, 2756, 2762, 2768, 2774, and 2780 such as 1D
convolutions (CONV
1D), 3D convolutions (CONV 3D), ReLU non-linearity, and batch normalization
(BN). Other
examples of the processing modules include fully-connected (dense) layers, a
dropout layer, a
flattening layer, and a final softmax layer that produces exponentially
normalized scores for the
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target variant belonging to a benign class and a pathogenic class. In Figure
27, "64- and "32"
denote a number of convolution filters applied by a particular processing
module. In Figure 27,
the size of an input voxel 2704 supplied by an input layer 2702 is 7x7x7x108.
Figure 27 also
shows respective volumetric dimensionalities of the intermediate inputs 2710,
2716, 2722, 2728,
2734, 2740, 2746, 2752, 2758, 2764, 2770, 2776, and 2782 and the resulting
intermediate
outputs 2706, 2712, 2718, 2724, 2730, 2736, 2742, 2748, 2754, 2760, 2766,
2772, 2778, and
2784 generated by the processing architecture 2700.
[0169] A person skilled in the art will appreciate that other
current and future artificial
intelligence, machine learning, and deep learning models, datasets, and
training techniques can
be incorporated in the disclosed variant pathogenicity classifier without
deviating from the spirit
of the technology disclosed.
Performance Results as Objective Indicia of Inventiveness and Non-Obviousness
[0170] The variant pathogenicity classifier disclosed herein makes
pathogenicity predictions
based on 3D protein structures and is referred to as "PrimateAI 3D." "Primate
AI" is a
commonly owned and previously disclosed variant pathogenicity classifier that
makes
pathogenicity predictions based protein sequences. Additional details about
PrimateAI can be
found in commonly owned US Patent Application Nos. 16/160,903; 16/160,986;
16/160,968;
and 16/407,149 and in Sundaram, L. et al. Predicting the clinical impact of
human mutation with
deep neural networks. Nat. Genet. 50, 1161-1170 (2018).
[0171] Figures 28, 29, 30, and 31 use PrimateAI as a benchmark
model to demonstrate
PrimateAI 3D's classification superiority over PrimateAI. The performance
results in Figures 28,
29, 30, and 31 are generated on the classification task of accurately
distinguishing benign
variants from pathogenic variants across a plurality of validation sets.
PrimateAI 3D is trained on
training sets that are different from the plurality of validation sets.
PrimateAI 3D is trained on
common human variants and variants from primates used as benign dataset while
simulated
variants based on trinucleotide context used as unlabeled or pseudo-pathogenic
dataset.
101721 New developmental delay disorder (new DDD) is one example of
a validation set
used to compare the classification accuracy of Primate AT 3D against Primate
AT. The new DDD
validation set labels variants from individuals with DDD as pathogenic and
labels the same
variants from healthy relatives of the individuals with the DDD as benign. A
similar labelling
scheme is used with an autism spectrum disorder (ASD) validation set shown in
Figure 31.
[0173] BRCA1 is another example of a validation set used to compare
the classification
accuracy of Primate AT 3D against Primate AT. The BRCA1 validation set labels
synthetically
generated reference amino acid sequences simulating proteins of the BRCA1 gene
as benign
variants and labels synthetically altered allele amino acid sequences
simulating proteins of the
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BRCA1 gene as pathogenic variants. A similar labelling scheme is used with
different validation
sets of the TP53 gene, TP53S3 gene and its variants, and other genes and their
variants shown in
Figure 31.
101741 Figure 28 identifies performance of the benchmark PrimateAI
model with blue
horizontal bars and performance of the disclosed PrimateAI 3D model with
orange horizontal
bars. Green horizontal bars depict pathogenicity predictions derived by
combining respective
pathogenicity predictions of the disclosed PrimateAI 3D model and the
benchmark PrimateAI
model. In the legend, "ens10" denotes an ensemble of ten PrimateAI 3D models,
each trained
with a different seed training dataset and randomly initialized with different
weights and biases.
Also, "7x7x7x2" depicts the size of the voxel grid used to encode the input
channels during the
training of the ensemble of ten PrimateAI 3D models. For a given variant, the
ensemble of ten
PrimateAI 3D models respectively generates ten pathogenicity predictions,
which are
subsequently combined (e.g., by averaging) to generate a final pathogenicity
prediction for the
given variant. This logic analogous applies to ensembles of different group
sizes.
101751 Also, in Figure 28, the y-axis has the different validation
sets and the x-axis has p-
values. Greater p-values, i.e., longer horizontal bars denote greater accuracy
in differentiating
benign variants from pathogenic variants. As demonstrated by the p-values in
Figure 28,
PrimateAI 3D outperforms PrimateAI across most of the validation sets (only
exception being
the tp53s3 A549 validation set). That is, the orange horizontal bars for
PrimateAI 3D are
consistently longer than the blue horizontal bars for PrimateAI.
101761 Also, in Figure 28, a -mean" category along the y-axis
calculates the mean of the p-
values determined for each of the validation sets. In the mean category as
well, PrimateAI 3D
outperforms PrimateAI.
101771 In Figure 29, PrimateAI is represented by blue horizontal
bars, an ensemble of twenty
PrimateAI 3D models trained with a voxel grid of size 3x3x3 is represented by
red horizontal
bars, an ensemble of ten PrimateAI 3D models trained with a voxel grid of size
7x7x7 is
represented by purple horizontal bars, an ensemble of twenty PrimateAI 3D
models trained with
a voxel grid of size 7x7x7 is represented by brown horizontal bars, and an
ensemble of twenty
PrimateAI 3D models trained with a voxel grid of size 17x17x17 is represented
by purple
horizontal bars.
101781 Also, in Figure 29, the y-axis has the different validation
sets and the x-axis has p-
values. As before, greater p-values, i.e., longer horizontal bars denote
greater accuracy in
differentiating benign variants from pathogenic variants. As demonstrated by
the p-values in
Figure 20, different configurations of PrimateAI 3D outperform PrimateAI
across most of the
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validation sets. That is, the red, purple, brown, and pink horizontal bars for
PrimateAI 3D are
mostly longer than the blue horizontal bars for PrimateAI.
[0179] Also, in Figure 29, a "mean" category along the y-axis
calculates the mean of the p-
values determined for each of the validation sets. In the mean category as
well, the different
configurations of PrimateAI 3D outperform PrimateAI.
[0180] In Figure 30, the red vertical bars represent PrimateAL and
the cyan vertical bars
represent PrimateAI 3D. In Figure 30, the y-axis has p-values, and the x-axis
has the different
validation sets. In Figure 30, without exceptions, PrimateAI 3D consistently
outperforms
PrimateAI across all of the validation sets. That is, the cyan vertical bars
for PrimateAI 3D are
always longer than the red vertical bars for PrimateAI.
[0181] Figure 31 identifies performance of the benchmark PrimateAI
model with blue
vertical bars and performance of the disclosed PrimateAI 3D model with orange
vertical bars.
Green vertical bars depict pathogenicity predictions derived by combining
respective
pathogenicity predictions of the disclosed PrimateAI 3D model and the
benchmark PrimateAI
model. In Figure 31, the y-axis has p-values, and the x-axis has the different
validation sets.
[0182] As demonstrated by the p-values in Figure 31, PrimateAI 3D
outperforms PrimateAI
across most of the validation sets (only exception being the tp53s3
A549_p53NULL Nutlin-3
validation set). That is, the orange vertical bars for PrimateAI 3D are
consistently longer than the
blue vertical bars for PrimateAI.
[0183] Also, in Figure 31, a separate -mean" chart calculates the
mean of the p-values
determined for each of the validation sets. In the mean chart as well,
PrimateAl 3D outperforms
PrimateAI.
[0184] The mean statistics may be biased by outliers. To address
this, a separate -method
ranks" chart is also depicted in Figure 31. Higher rank denotes poorer
classification accuracy. In
the method ranks chart as well, PrimateAI 3D outperforms PrimateAI by having
more counts of
lower ranks 1 and 2 versus Primate AT having all 3s.
101851 In Figures 28 to 31, it is also evident that combining
PrimateAI 3D with PrimateAI
produces superior classification accuracy That is, a protein can be fed as an
amino acid sequence
to PrimateAI to generate a first output, and the same protein can be fed as a
3D, voxelized
protein structure to PrimateAI 3D to generate a second output, and the first
and second outputs
can be combined or analyzed in aggregate to produce a final pathogenicity
prediction for a
variant experienced by the protein.
Efficient Voxelization
[0186] Figure 32 is a flowchart illustrating an efficient
voxelization process 3200 that
efficiently identifies nearest atoms on a voxel-by-voxel basis.
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101871 The discussion now revisits the distance channels. As
discussed above, the reference
amino acid sequence 202 can contain different types of atoms, such as alpha-
carbon atoms, beta-
carbon atoms, oxygen atoms, nitrogen atoms, hydrogen atoms, and so on.
Accordingly, as
discussed above, the distance channels can be arranged by nearest alpha-carbon
atoms, nearest
beta-carbon atoms, nearest oxygen atoms, nearest nitrogen atoms, nearest
hydrogen atoms, and
so on. For example, in Figure 6, each of the nine voxels 514 has twenty-one
amino acid-wise
distance channels for nearest alpha-carbon atoms. Figure 6 can be further
expanded for each of
the nine voxels 514 to also have twenty-one amino acid-wise distance channels
for nearest beta-
carbon atoms, and for each of the nine voxels 514 to also have a nearest
generic atom distance
channel for a nearest atom irrespective of the type of the atom and the type
of the amino acid.
This way, each of the nine voxels 514 can have forty-three distance channels.
101881 The discussion now turns to the number of distance
calculations required to identify
the nearest atoms on a voxel-by-voxel basis for inclusion in the distance
channels. Consider the
example in Figure 3 that depicts a total of eight hundred and twenty-eight
alpha-carbon atoms
distributed across the twenty-one amino acid categories. To calculate the
amino acid-wise
distance channels 602-642 in Figure 6, i.e., to determine the one hundred and
eighty-nine
distance values, distances are measured from each of the nine voxels 514 to
each of the eight
hundred and twenty-eight alpha-carbon atoms, resulting in 9 * 828 = 7, 452
distance
calculations. In the 3D case of twenty-seven voxels, this results in 27 * 828
= 22,356 distance
calculations. When the eight hundred and twenty-eight beta-carbon atoms are
also included, this
number increases to 27 * 1656 = 44, 712 distance calculations.
101891 This means that the runtime complexity of identifying the
nearest atoms on a voxel-
by-voxel basis for a single protein voxelization is 0(#atoms * #voxels), as
illustrated by Figure
35A. Furthermore, the runtime complexity for a single protein voxelization
increases to
0(#atoms * #voxels * #attributes) when the distance channels are calculated
across a variety of
attributes (e.g., different features or channels per voxel like annotation
channels and structural
confidence channels).
101901 Consequently, the distance calculations can become the most
compute-consuming
part of the voxelization process, taking valuable compute resources away from
critical runtime
tasks like model training and model inference. Consider, for example, the case
of model training
with a training dataset of 7,000 proteins. Generating distance channels for a
plurality of voxels
across a plurality of amino acids, atoms, and attributes can involve more than
100 voxelizations
per protein, resulting in about 800,000 voxelizations in a single training
iteration (epoch). A
training run of 20-40 epochs, with rotation of atomic coordinates in each
epoch, can result in as
many as 32 million voxelizations.
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101911 In addition to the high compute cost, the size of the data
for 32 million voxelizations
is too big to fit in main memory (e.g., >20TB for a 15x15x15 voxel grid).
Considering repeated
training runs for parameter optimization and ensemble learning, the memory
footprint of the
voxelization process gets too big to be stored on disk, making the
voxelization process a part of
the model training and not a precomputation step.
101921 The technology disclosed provides an efficient voxelization
process that achieves up
to ¨100x speedup over the runtime complexity of 0(#atoms * #voxels). The
disclosed efficient
voxelization process reduces the runtime complexity for a single protein
voxelization to
0(#atoms). In the case of different features or channels per voxel, the
disclosed efficient
voxelization process reduces the runtime complexity for a single protein
voxelization to
0(#atoms * #attributes). As a result, the voxelization process becomes as fast
as model training,
shifting the computational bottleneck from voxelization back to computing
neural network
weights on processors such as GPUs, ASICs, TPUs, FPGAs, CGRAs, etc.
101931 In some implementations of the disclosed efficient
voxelization process involving
large voxel grids, the runtime complexity for a single protein voxelization is
0(#atoms + voxels)
and 0(#atoms * #attributes + voxels) for the case of different features or
channels per voxel. The
"+ voxels" complexity is observed when the number of atoms is minuscule
compared to the
number of voxels, for example, when there is one atom in a 100x100x100 voxel
grid (i.e., one
million voxels per atom). In such a scenario, the runtime is dominated by the
overhead of the
huge number of voxels, for example, for allocating the memory for one million
voxels,
initialization one million voxels to zero, etc.
101941 The discussion now turns to details of the disclosed
efficient voxelization process.
Figures 32A, 32B, 33, 34, and 35B are discussed in tandem.
101951 Starting with Figure 32A, at step 3202, each atom (e.g.,
each of the 828 alpha-carbon
atoms and each of the 828 beta-carbon atoms) is associated with a voxel that
contains the atom
(e.g., one of the nine voxels 514). The term "contains" refers to the 3D
atomic coordinates of the
atom being located in the voxel. The voxel that contains the atom is also
referred to herein as
"the atom-containing voxel"
101961 Figures 32B and 33 describe how a voxel that contains a
particular atom is selected.
Figure 33 uses 2D atomic coordinates as representative of 3D atomic
coordinates. Note that the
voxel grid 522 is regularly spaced with each of the voxels 514 having a same
step size (e.g., 1
angstrom (A) or 2 A).
101971 Also, in Figure 33, the voxel grid 522 has magenta indices
[0, 1, 2] along a first
dimension (e.g., x-axis) and cyan indices [0, 1, 2] along a second dimension
(e.g., y-axis). Also,
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in Figure 33, the respective voxels 514 in the voxel grid 522 are identified
by green voxel indices
[Voxel 0, Voxel 1, ..., Voxel 81 and by black voxel center indices [(1, 1),
(1, 2), ..., (3, 3)].
[0198] Also, in Figure 33, center coordinates of the voxel centers
along the first dimension,
i.e., first dimension voxel coordinates, are identified in orange. Also, in
Figure 33, center
coordinates of the voxel centers along the second dimension, i.e., second
dimension voxel
coordinates, are identified in red.
[0199] First, at step 3202a (Step 1 in Figure 33), 3D atomic
coordinates (1.7456, 2.14323) of
the particular atom are quantized to generated quantized 3D atomic coordinates
(1.7, 2.1). The
quantization can be achieved by rounding or truncation of bits.
[0200] Then, at step 3202b (Step 2 in Figure 33), voxel coordinates
(or voxel centers or
voxel center coordinates) of the voxels 514 are assigned to the quantized 3D
atomic coordinates
on a dimension-basis. For the first dimension, the quantized atomic coordinate
1.7 is assigned to
Voxel 1 because it covers first dimension voxel coordinates ranging from 1 to
2 and is centered
at 1.5 in the first dimension. Note that Voxel 1 has index 1 along the first
dimension, in contrast
to having index 0 along the second dimension.
[0201] For the second dimension, starting from Voxel 1, the voxel
grid 522 is traversed
along the second dimension. This results in the quantized atomic coordinate
2.5 being assigned
to Voxel 7 because it covers second dimension voxel coordinates ranging from 2
to 3 and is
centered at 2.5 in the second dimension. Note that Voxel 7 has index 2 along
the second
dimension, in contrast to having index 1 along the first dimension.
[0202] Then, at step 3202c (Step 3 in Figure 33), dimension indices
corresponding to the
assigned voxel coordinates are selected. That is, for Voxel 1, index 1 is
selected along the first
dimension, and, for Voxel 7, index 2 is selected along the second dimension. A
person skilled in
the art will appreciate that the above steps can be analogously executed for a
third dimension to
select a dimension index along the third dimension.
[0203] Then, at step 3202d (Step 4 in Figure 33), an accumulated
sum is generated based on
position-wise weighting the selected dimension indices by powers of a radix.
The general idea
behind positional numbering systems is that a numeric value is represented
through increasing
powers of the radix (or base), for example, binary is base two, ternary is
base three, octal is base
eight, and hexadecimal is base sixteen. This is often referred to as a
weighted numbering system
because each position is weighted by a power of the radix. The set of valid
numericals for a
positional numbering system is equal in size to the radix of that system. For
example, there are
ten digits in the decimal system, zero through nine, and three digits in the
ternary system, zero,
one, and two. The largest valid number in a radix system is one smaller than
the radix (so eight is
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not a valid numerical in any radix system smaller than nine). Any decimal
integer can be
expressed exactly in any other integral base system, and vice-versa.
[0204] Returning to the example in Figure 33, the selected
dimension indices 1 and 2 are
converted to a single integer by position-wise multiplying them with
respective powers of base
three and summing the results of the position-wise multiplications. Base three
is selected here
because the 3D atomic coordinates have three dimensions (although Figure 33
shows only 2D
atomic coordinates along two dimensions for simplicity's sake).
[0205] Since index 2 is positioned at the rightmost bit (i.e., the
least significant bit), it is
multiplied by three to the power of zero to yield two. Since index 1 is
positioned at the second
rightmost bit (i.e., the second least significant bit), it is multiplied by
three to the power of one to
yield three. This results in the accumulated sum being five.
[0206] Then, at step 3202e (Step 5 in Figure 33), based on the
accumulated sum, a voxel
index of the voxel containing the particular atom is selected. That is, the
accumulated sum is
interpreted as the voxel index of the voxel containing the particular atom.
[0207] At step 3212, after each atom is associated with the atom-
containing voxel, each atom
is further associated with one or more voxels that are in a neighborhood of
the atom-containing
voxel, also referred to herein as "neighborhood voxels." The neighborhood
voxels can be
selected based on being within a predefined radius of the atom-containing
voxel (e.g., 5
angstrom (A)). In other implementations, the neighborhood voxels can be
selected based on
being contiguously adjacent to the atom-containing voxel (e.g., top, bottom,
right, left adjacent
voxels). The resulting association that associates each atom with the atom-
containing voxel and
the neighborhood voxels is encoded in an atom-to-voxels mapping 3402, also
referred to herein
as element-to-cells mapping. In one example, a first alpha-carbon atom is
associated with a first
subset of voxels 3404 that includes an atom-containing voxel and neighborhood
voxels for the
first alpha-carbon atom. In another example, a second alpha-carbon atom is
associated with a
second subset of voxels 3406 that includes an atom-containing voxel and
neighborhood voxels
for the second alpha-carbon atom.
[0208] Note that no distance calculations are made to determine the
atom-containing voxel
and the neighborhood voxels. The atom-containing voxel is selected by virtue
of the spatial
arrangement of the voxels that allows assignment of quantized 3D atomic
coordinates to
corresponding regularly spaced voxel centers in the voxel grid (without using
any distance
calculations). Also, the neighborhood voxels are selected by virtue of being
spatially contiguous
to the atom-containing voxel in the voxel grid (again without using any
distance calculations).
[0209] At step 3222, each voxel is mapped to atoms to which it was
associated at steps 3202
and 3212. In one implementation, this mapping is encoded in a voxel-to-atoms
mapping 3412,
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which is generated based on the atom-to-voxels mapping 3402 (e.g., by applying
a voxel-based
sorting key on the atom-to-voxels mapping 3402). The voxel-to-atoms mapping
3412 is also
referred to herein as "cell-to-elements mapping." In one example, a first
voxel is mapped to a
first subset of alpha-carbon atoms 3414 that includes alpha-carbon atoms
associated with the first
voxel at steps 3202 and 3212. In another example, a second voxel is mapped to
a second subset
of alpha-carbon atoms 3416 that includes alpha-carbon atoms associated with
the second voxel at
steps 3202 and 3212.
[0210] At step 3232, for each voxel, distances are calculated
between the voxel and atoms
mapped to the voxel at step 3222. Step 3232 has a runtime complexity of
0(#atoms) because
distance to a particular atom is measured only once from a respective voxel to
which the
particular atom is uniquely mapped in the voxel-to-atoms mapping 3412. This is
true when no
neighboring voxels are considered. Without neighbors, the constant factor that
is implied in the
big-0 notation is 1. With neighbors, the big-0 notation is equal to the number
of neighbors + 1
since the number of neighbors is constant for each voxel, and therefore the
runtime complexity
of 0(#atoms) remains true. In contrast, in Figure 35A, distances to a
particular atom are
redundantly measured as many times as the number of voxels (e.g., 27 distances
for a particular
atom due to 27 voxels).
[0211] In Figure 35B, based on the voxel-to-atoms mapping 3412,
each voxel is mapped to a
respective subset of the 828 atoms (not including distance calculations to
neighborhood voxels),
as illustrated by respective ovals for respective voxels. The respective
subsets are largely non-
overlapping, with some exceptions. Insignificant overlap exists due to some
instances when
multiple atoms are mapped to a same voxel, as indicated in Figure 35B by the
prime symbol
and the yellow overlap between the ovals. This minimal overlap has an additive
effect on the
runtime complexity of 0(#atoms) and not a multiplicative effect. This overlap
is a result of
considering neighboring voxels, after determining the voxel that contains the
atom. Without
neighboring voxels, there can be no overlap, because an atom is only
associated with one voxel.
Considering neighbors, however, each neighbor could potentially be associated
with the same
atom (as long as there is no other atom of the same amino acid that is closer)
[0212] At step 3242, for each voxel, based on the distances
calculated at step 3232, a nearest
atom to the voxel is identified. In one implementation, this identification is
encoded in a voxel-
to-nearest atom mapping 3422, also referred to herein as "cell-to-nearest
element mapping." In
one example, the first voxel is mapped to a second alpha-carbon atom as its
nearest alpha-carbon
atom 3424. In another example, the second voxel is mapped to a thirty-first
alpha-carbon atom as
its nearest alpha-carbon atom 3426.
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102131 Furthermore, as the voxel-wise distances are calculated
using the technique discussed
above, the atom-type and amino acid-type categorization of the atoms and the
corresponding
distance values are stored to generate categorized distance channels.
102141 Once the distances to nearest atoms are identified using the
technique discussed
above, these distances can be encoded in the distance channels for
voxelization and subsequent
processing by the pathogenicity classifier 2108.
Computer System
102151 Figure 36 shows an example computer system 3600 that can be
used to implement the
technology disclosed. Computer system 3600 includes at least one central
processing unit (CPU)
3672 that communicates with a number of peripheral devices via bus subsystem
3655. These
peripheral devices can include a storage subsystem 3610 including, for
example, memory
devices and a file storage subsystem 3636, user interface input devices 3638,
user interface
output devices 3676, and a network interface subsystem 3674. The input and
output devices
allow user interaction with computer system 3600. Network interface subsystem
3674 provides
an interface to outside networks, including an interface to corresponding
interface devices in
other computer systems.
102161 In one implementation, the pathogenicity classifier 2108 is
communicably linked to
the storage subsystem 3610 and the user interface input devices 3638.
102171 User interface input devices 3638 can include a keyboard;
pointing devices such as a
mouse, trackball, touchpad, or graphics tablet; a scanner; a touch screen
incorporated into the
display; audio input devices such as voice recognition systems and
microphones; and other types
of input devices. In general, use of the term "input device- is intended to
include all possible
types of devices and ways to input information into computer system 3600.
102181 User interface output devices 3676 can include a display
subsystem, a printer, a fax
machine, or non-visual displays such as audio output devices. The display
subsystem can include
an LED display, a cathode ray tube (CRT), a flat-panel device such as a liquid
crystal display
(LCD), a projection device, or some other mechanism for creating a visible
image. The display
subsystem can also provide a non-visual display such as audio output devices.
In general, use of
the term "output device" is intended to include all possible types of devices
and ways to output
information from computer system 3600 to the user or to another machine or
computer system.
102191 Storage subsystem 3610 stores programming and data
constructs that provide the
functionality of some or all of the modules and methods described herein.
These software
modules are generally executed by processors 3678.
102201 Processors 3678 can be graphics processing units (GPUs),
field-programmable gate
arrays (FPGAs), application-specific integrated circuits (ASICs), and/or
coarse-grained
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reconfigurable architectures (CGRAs). Processors 3678 can be hosted by a deep
learning cloud
platform such as Google Cloud PlatformTM, XilinxTM, and CirrascaleTM. Examples
of processors
3678 include Google's Tensor Processing Unit (TPU)Tm, rackmount solutions like
GX4
Rackmount SeriesTM, GX36 Rackmount SeriesTM, NVIDIA DGX1TM, Microsoft Stratix
V
FPGATM, Graphcore's Intelligent Processor Unit (IPU)TM, Qualcomm's Zeroth
PlatformTM with
Snapdragon processorsTM, NVIDIA' s VoltaTm, NVIDIA' s DRIVE PXTM, NVIDIA' s
JETSON
TX1/TX2 MODULETM, Intel's NirvanaTM, Movidius VPUTM, Fujitsu DPITM, ARM' s
DynamiclQTM, IBM TrueNorthTm, Lambda GPU Server with Testa ViOOsTM, and
others.
[0221] Memory subsystem 3622 used in the storage subsystem 3610 can
include a number of
memories including a main random access memory (RAM) 3632 for storage of
instructions and
data during program execution and a read only memory (ROM) 3634 in which fixed
instructions
are stored. A file storage subsystem 3636 can provide persistent storage for
program and data
files, and can include a hard disk drive, a floppy disk drive along with
associated removable
media, a CD-ROM drive, an optical drive, or removable media cartridges. The
modules
implementing the functionality of certain implementations can be stored by
file storage
subsystem 3636 in the storage subsystem 3610, or in other machines accessible
by the processor.
[0222] Bus subsystem 3655 provides a mechanism for letting the
various components and
subsystems of computer system 3600 communicate with each other as intended.
Although bus
subsystem 3655 is shown schematically as a single bus, alternative
implementations of the bus
subsystem can use multiple busses.
[0223] Computer system 3600 itself can be of varying types
including a personal computer, a
portable computer, a workstation, a computer terminal, a network computer, a
television, a
mainframe, a server farm, a widely-distributed set of loosely networked
computers, or any other
data processing system or user device. Due to the ever-changing nature of
computers and
networks, the description of computer system 3600 depicted in Figure 36 is
intended only as a
specific example for purposes of illustrating the preferred implementations of
the present
invention. Many other configurations of computer system 3600 are possible
having more or less
components than the computer system depicted in Figure 36
Particular Implementations 1
[0224] The following implementations can be practiced as a system,
method, or article of
manufacture. One or more features of an implementation can be combined with
the base
implementation. Implementations that are not mutually exclusive are taught to
be combinable.
One or more features of an implementation can be combined with other
implementations. This
disclosure periodically reminds the user of these options. Omission from some
implementations
of recitations that repeat these options should not be taken as limiting the
combinations taught in
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the preceding sections ¨ these recitations are hereby incorporated forward by
reference into each
of the following implementations.
[0225] Though the technology disclosed uses 3D data as input, in
other implementations, it
can analogously use ID data, 2D data (e.g., pixels and 2D atomic coordinates),
4D data, 5D data,
and so on.
[0226] In some implementations, a system comprises memory storing
amino acid-wise
distance channels for a plurality of amino acids in a protein. Each of the
amino acid-wise
distance channels has voxel-wise distance values for voxels in a plurality of
voxels. The voxel-
wise distance values specify distances from corresponding voxels in the
plurality of voxels to
atoms of corresponding amino acids in the plurality of amino acids. The system
further
comprises a pathogenicity determination engine configured to process a tensor
that includes the
amino acid-wise distance channels and an alternative allele of the protein
expressed by a variant.
The pathogenicity determination engine can also be configured to determine a
pathogenicity of
the variant based at least in part on the tensor.
[0227] In some implementations, the system further comprises a
distance channels generator
that centers a voxel grid of the voxels on an alpha-carbon atom of respective
residues of the
amino acids. The distance channels generator can center the voxel grid on an
alpha-carbon atom
of a residue of a particular amino acid that positioned at a variant amino
acid in the protein.
[0228] The system can be configured to encode, in the tensor, a
directionality of the amino
acids and a position of the particular amino acid by multiplying, with a
directionality parameter,
voxel-wise distance values for those amino acids that precede the particular
amino acid. The
distances can be nearest-atom distances from corresponding voxel centers in
the voxel grid to
nearest atoms of the corresponding amino acids. In some implementations, the
nearest-atom
distances can be Euclidean distances. The nearest-atom distances can be
normalized by dividing
the Euclidean distances with a maximum nearest-atom distance. The amino acids
can have alpha-
carbon atoms and, in some implementations, the distances can be nearest-alpha-
carbon atom
distances from the corresponding voxel centers to nearest alpha-carbon atoms
of the
corresponding amino acids The amino acids can have beta-carbon atoms and, in
some
implementations, the distances can be nearest-beta-carbon atom distances from
the
corresponding voxel centers to nearest beta-carbon atoms of the corresponding
amino acids. The
amino acids can have backbone atoms and, in some implementations, the
distances can be
nearest-backbone atom distances from the corresponding voxel centers to
nearest backbone
atoms of the corresponding amino acids. The amino acids have side chain atoms
and, in some
implementations, the distances can be nearest-sidechain atom distances from
the corresponding
voxel centers to nearest sidechain atoms of the corresponding amino acids.
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102291 The system can further be configured to encode, in the
tensor, a nearest atom channel
that specifies a distance from each voxel to a nearest atom. The nearest atom
can be selected
irrespective of the amino acids and atomic elements of the amino acids. In
some
implementations, the distance is a Euclidean distance. The distance can be
normalized by
dividing the Euclidean distance with a maximum distance. The amino acids can
include non-
standard amino acids. The tensor can include an absentee atom channel that
specifies atoms not
found within a predefined radius of a voxel center, and the absentee atom
channel can be one-hot
encoded. In some implementations, the tensor can further include a one-hot
encoding of the
alternative allele that is voxel-wise encoded to each of the amino acid-wise
distance channels.
The tensor can further include a reference allele of the protein. In some
implementations, the
tensor can further include a one-hot encoding of the reference allele that is
voxel-wise encoded
to each of the amino acid-wise distance channels. The tensor can further
include evolutionary
profiles that specify conservation levels of the amino acids across a
plurality of species.
102301 The system can further comprise an evolutionary profiles
generator that, for each of
the voxels, selects a nearest atom across the amino acids and the atom
categories, selects a pan-
amino acid conservation frequencies sequence for a residue of an amino acid
that includes the
nearest atom, and makes the pan-amino acid conservation frequencies sequence
available as one
of the evolutionary profiles. The pan-amino acid conservation frequencies
sequence can be
configured for a particular position of the residue as observed in the
plurality of species. The
pan-amino acid conservation frequencies sequence can specify whether there is
a missing
conservation frequency for a particular amino acid. In some implementations,
the evolutionary
profiles generator, for each of the voxels, can select respective nearest
atoms in respective ones
of the amino acids, can select respective per-amino acid conservation
frequencies for respective
residues of the amino acids that include the nearest atoms, and can make the
per-amino acid
conservation frequencies available as one of the evolutionary profiles. The
per-amino acid
conservation frequencies can be configured for a particular position of the
residues as observed
in the plurality of species. The per-amino acid conservation frequencies can
specify whether
there is a missing conservation frequency for a particular amino acid
102311 In some implementations of the system, the tensor can
further include annotation
channels for the amino acids. The annotation channels can be one-hot encoded
in the tensor. The
annotation channels can be molecular processing annotations that include
initiator methionine,
signal, transit peptide, propeptide, chain, and peptide. The annotation
channels can be regions
annotations that include topological domain, transmembrane, intramembrane,
domain, repeat,
calcium binding, zinc finger, deoxyribonucleic acid (DNA) binding, nucleotide
binding, region,
coiled coil, motif, and compositional bias. The annotation channels can be
sites annotations that
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include active site, metal binding, binding site, and site. The annotation
channels can be amino
acid modifications annotations that include non-standard residue, modified
residue, lipidation,
glycosylation, disulfide bond, and cross-link. The annotation channels can be
secondary structure
annotations that include helix, turn, and beta strand. The annotation channels
can be
experimental information annotations that include mutagenesis, sequence
uncertainty, sequence
conflict, non-adjacent residues, and non-terminal residue.
[0232] In some implementations of the system, the tensor further
includes structure
confidence channels for the amino acids that specify quality of respective
structures of the amino
acids. The structure confidence channels can be global model quality
estimations (GMQEs). The
structure confidence channels can include qualitative model energy analysis
(QMEAN) scores.
The structure confidence channels can be temperature factors that specify a
degree to which the
residues satisfy physical constraints of respective protein structures. The
structure confidence
channels can be template structures alignments that specify a degree to which
residues of atoms
nearest to the voxels have aligned template structures. The structure
confidence channels can be
template modeling scores of the aligned template structures. The structure
confidence channels
can be a minimum one of the template modeling scores, a mean of the template
modeling scores,
and a maximum one of the template modeling scores.
[0233] In some implementations, the system can further comprise a
tensor generator that
voxel-wise concatenates amino acid-wise distance channels for the alpha-carbon
atoms with the
one-hot encoding of the alternative allele to generate the tensor. The tensor
generator can voxel-
wise concatenate amino acid-wise distance channels for the beta-carbon atoms
with the one-hot
encoding of the alternative allele to generate the tensor. The tensor
generator can voxel-wise
concatenate the amino acid-wise distance channels for the alpha-carbon atoms,
the amino acid-
wise distance channels for the beta-carbon atoms, and the one-hot encoding of
the alternative
allele to generate the tensor. The tensor generator can voxel-wise concatenate
the amino acid-
wise distance channels for the alpha-carbon atoms, the amino acid-wise
distance channels for the
beta-carbon atoms, the one-hot encoding of the alternative allele, and pan-
amino acid
conservation frequencies to generate the tensor The tensor generator can voxel-
wise concatenate
the amino acid-wise distance channels for the alpha-carbon atoms, the amino
acid-wise distance
channels for the beta-carbon atoms, the one-hot encoding of the alternative
allele, the pan-amino
acid conservation frequencies, and the annotation channels to generate the
tensor. The tensor
generator can voxel-wise concatenate the amino acid-wise distance channels for
the alpha-carbon
atoms, the amino acid-wise distance channels for the beta-carbon atoms, the
one-hot encoding of
the alternative allele, the pan-amino acid conservation frequencies, the
annotation channels, and
the structure confidence channels to generate the tensor. The tensor generator
can voxel-wise
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concatenate the amino acid-wise distance channels for the alpha-carbon atoms,
the amino acid-
wise distance channels for the beta-carbon atoms, the one-hot encoding of the
alternative allele,
and per-amino acid conservation frequencies for each of the amino acids to
generate the tensor.
The tensor generator can voxel-wise concatenate the amino acid-wise distance
channels for the
alpha-carbon atoms, the amino acid-wise distance channels for the beta-carbon
atoms, the one-
hot encoding of the alternative allele, per-amino acid conservation
frequencies for each of the
amino acids, and the annotation channels to generate the tensor. The tensor
generator can voxel-
wise concatenate the amino acid-wise distance channels for the alpha-carbon
atoms, the amino
acid-wise distance channels for the beta-carbon atoms, the one-hot encoding of
the alternative
allele, per-amino acid conservation frequencies for each of the amino acids,
the annotation
channels, and the structure confidence channels to generate the tensor. The
tensor generator can
voxel-wise concatenate the amino acid-wise distance channels for the alpha-
carbon atoms, the
amino acid-wise distance channels for the beta-carbon atoms, the one-hot
encoding of the
alternative allele, and the one-hot encoding of the reference allele to
generate the tensor. The
tensor generator can voxel-wise concatenate the amino acid-wise distance
channels for the alpha-
carbon atoms, the amino acid-wise distance channels for the beta-carbon atoms,
the one-hot
encoding of the alternative allele, the one-hot encoding of the reference
allele, and the pan-amino
acid conservation frequencies to generate the tensor. The tensor generator can
voxel-wise
concatenate the amino acid-wise distance channels for the alpha-carbon atoms,
the amino acid-
wise distance channels for the beta-carbon atoms, the one-hot encoding of the
alternative allele,
the one-hot encoding of the reference allele, the pan-amino acid conservation
frequencies, and
the annotation channels to generate the tensor. The tensor generator can voxel-
wise concatenate
the amino acid-wise distance channels for the alpha-carbon atoms, the amino
acid-wise distance
channels for the beta-carbon atoms, the one-hot encoding of the alternative
allele, the one-hot
encoding of the reference allele, the pan-amino acid conservation frequencies,
the annotation
channels, and the structure confidence channels to generate the tensor. The
tensor generator can
voxel-wise concatenate the amino acid-wise distance channels for the alpha-
carbon atoms, the
amino acid-wise distance channels for the beta-carbon atoms, the one-hot
encoding of the
alternative allele, the one-hot encoding of the reference allele, and the per-
amino acid
conservation frequencies for each of the amino acids to generate the tensor.
The tensor generator
can voxel-wise concatenate the amino acid-wise distance channels for the alpha-
carbon atoms,
the amino acid-wise distance channels for the beta-carbon atoms, the one-hot
encoding of the
alternative allele, the one-hot encoding of the reference allele, the per-
amino acid conservation
frequencies for each of the amino acids, and the annotation channels to
generate the tensor. The
tensor generator can voxel-wise concatenate the amino acid-wise distance
channels for the alpha-
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carbon atoms, the amino acid-wise distance channels for the beta-carbon atoms,
the one-hot
encoding of the alternative allele, the one-hot encoding of the reference
allele, the per-amino acid
conservation frequencies for each of the amino acids, the annotation channels,
and the structure
confidence channels to generate the tensor.
102341 In some implementations, the system can further comprise an
atoms rotation engine
that rotates atoms of the amino acids before the amino acid-wise distance
channels are generated.
The pathogenicity determination engine can be a neural network. In particular
implementations,
the pathogenicity determination engine can be a convolutional neural network.
The convolutional
neural network can use 1 x 1 x 1 convolutions, 3 x 3 x 3 convolutions,
rectified linear unit
activation layers, batch normalization layers, a fully-connected layer, a
dropout regularization
layer, and a softmax classification layer. The 1 x 1 x 1 convolutions and the
3 x 3 x 3
convolutions can be three-dimensional convolutions.
102351 In some implementations, a layer of the 1 x 1 x 1
convolutions can process the tensor
and produce an intermediate output that is a convolved representation of the
tensor. A sequence
of layers of the 3 x 3 x 3 convolutions can process the intermediate output
and produce a
flattened output. The fully-connected layer can process the flattened output
and produce
unnormalized outputs. The softmax classification layer can process the
unnormalized outputs
and produce exponentially normalized outputs that identify likelihoods of the
variant being
pathogenic and benign. A sigmoid layer can process the unnormalized outputs
and produce a
normalized output that identifies a likelihood of the variant being
pathogenic. The voxels, the
atoms, and the distances can have three-dimensional coordinates. 'The tensor
can have at least
three dimensions, the intermediate output can have at least three dimensions,
and the flattened
output can have one dimension.
102361 In some implementations, the pathogenicity determination
engine is a recurrent neural
network. In other implementations, the pathogenicity determination engine is
an attention-based
neural network. In still other implementations, the pathogenicity
determination engine is a
gradient-boosted tree. In still other implementations, the pathogenicity
determination engine is a
state vector machine
102371 In other implementations, a system can comprise memory
storing atom category-wise
distance channels for amino acids in a protein. The amino acids can have atoms
for a plurality of
atom categories, and atom categories in the plurality of atom categories can
specify atomic
elements of the amino acids. The atom category-wise distance channels can have
voxel-wise
distance values for voxels in a plurality of voxels. The voxel-wise distance
values can specify
distances from corresponding voxels in the plurality of voxels to atoms in
corresponding atom
categories in the plurality of atom categories. The system can further
comprise a pathogenicity
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determination engine configured to process a tensor that includes the atom
category-wise
distance channels and an alternative allele of the protein expressed by a
variant, and to determine
a pathogenicity of the variant based at least in part on the tensor.
[0238] The system can further comprise a distance channels
generator that centers a voxel
grid of the voxels on respective atoms of respective atom categories in the
plurality of atom
categories. The distance channels generator can center the voxel grid on an
alpha-carbon atom of
a residue of at least one variant amino acid in the protein. The distances can
be nearest-atom
distances from corresponding voxel centers in the voxel grid to nearest atoms
in the
corresponding atom categories. The nearest-atom distances can be Euclidean
distances. The
nearest-atom distances can be normalized by dividing the Euclidean distances
with a maximum
nearest-atom distances. The distances can be nearest-atom distances from the
corresponding
voxel centers in the voxel grid to nearest atoms irrespective of the amino
acids and the atom
categories of the amino acids. The nearest-atom distances can be Euclidean
distances. The
nearest-atom distances can be normalized by dividing the Euclidean distances
with a maximum
nearest-atom distances.
[0239] Other implementations of the method described in this
section can include a non-
transitory computer readable storage medium storing instructions executable by
a processor to
perform any of the methods described above. Yet another implementation of the
method
described in this section can include a system including memory and one or
more processors
operable to execute instructions, stored in the memory, to perform any of the
methods described
above.
Clause Set 1
1. A computer-implemented method, comprising:
storing amino acid-wise distance channels for a plurality of amino acids in a
protein,
wherein each of the amino acid-wise distance channels has voxel-wise distance
values for voxels
in a plurality of voxels, and
wherein the voxel-wise distance values specify distances from corresponding
voxels in the
plurality of voxels to atoms of corresponding amino acids in the plurality of
amino acids;
processing a tensor that includes the amino acid-wise distance channels and an
alternative allele
of the protein expressed by a variant; and
determining a pathogenicity of the variant based at least in part on the
tensor.
2. The computer-implemented method of clause 1, further comprising
centering a voxel grid
of the voxels on an alpha carbon atom of respective residues of the amino
acids.
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3. The computer-implemented method of clause 2, further comprising
centering the voxel
grid on an alpha carbon atom of a residue of a particular amino acid that
corresponds to at least
one variant amino acid in the protein.
4. The computer-implemented method of clause 3, further comprising
encoding, in the
tensor, a directionality of the amino acids and a position of the particular
amino acid by
multiplying, with a directionality parameter, voxel-wise distance values for
those amino acids
that precede the particular amino acid.
5. The computer-implemented method of clause 3, wherein the distances are
nearest-atom
distances from corresponding voxel centers in the voxel grid to nearest atoms
of the
corresponding amino acids.
6. The computer-implemented method of clause 5, wherein the nearest-atom
distances are
Euclidean distances.
7. The computer-implemented method of clause 6, wherein the nearest-atom
distances are
normalized by dividing the Euclidean distances with a maximum nearest-atom
distance.
8. The computer-implemented method of clause 5, wherein the amino acids
have alpha
carbon atoms, and wherein the distances are nearest-alpha carbon atom
distances from the
corresponding voxel centers to nearest alpha carbon atoms of the corresponding
amino acids.
9. The computer-implemented method of clause 5, wherein the amino acids
have beta
carbon atoms and wherein the distances are nearest-beta carbon atom distances
from the
corresponding voxel centers to nearest beta carbon atoms of the corresponding
amino acids.
10. The computer-implemented method of clause 5, wherein the amino acids
have backbone
atoms and wherein the distances are nearest-backbone atom distances from the
coliesponding
voxel centers to nearest backbone atoms of the corresponding amino acids.
11. The computer-implemented method of clause 5, wherein the amino acids
have sidechain
atom and wherein the distances are nearest-sidechain atom distances from the
corresponding
voxel centers to nearest sidechain atoms of the corresponding amino acids.
12. The computer-implemented method of clause 3, further comprising
encoding, in the
tensor, a nearest atom channel that specifies a distance from each voxel to a
nearest atom,
wherein the nearest atom is selected irrespective of the amino acids and
atomic elements of the
amino acids.
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13. The computer-implemented method of clause 12, wherein the distance is a
Euclidean
distance.
14. The computer-implemented method of clause 13, wherein the distance is
normalized by
dividing the Euclidean distance with a maximum distance.
15. The computer-implemented method of clause 12, wherein the amino acids
include non-
standard amino acids.
16. The computer-implemented method of clause 1, wherein the tensor further
includes an
absentee atom channel that specifies atoms not found within a predefined
radius of a voxel
center, and wherein the absentee atom channel is one-hot encoded.
17. The computer-implemented method of clause 1, wherein the tensor further
includes a
one-hot encoding of the alternative allele that is voxel-wise encoded to each
of the amino acid-
wise distance channels.
18. The computer-implemented method of clause 1, wherein the tensor further
includes a
reference allele of the protein.
19. The computer-implemented method of clause 18, wherein the tensor
further includes a
one-hot encoding of the reference allele that is voxel-wise encoded to each of
the amino acid-
wise distance channels.
20. The computer-implemented method of clause 1, wherein the tensor further
includes
evolutionary profiles that specify conservation levels of the amino acids
across a plurality of
species.
21. The computer-implemented method of clause 20, further comprising, for
each of the
voxels,
selecting a nearest atom across the amino acids and the atom categories,
selecting a pan-amino acid conservation frequencies sequence for a residue of
an amino acid that
includes the nearest atom, and
making the pan-amino acid conservation frequencies sequence available as one
of the
evolutionary profiles.
22. The computer-implemented method of clause 21, wherein the pan-amino
acid
conservation frequencies sequence is configured for a particular position of
the residue as
observed in the plurality of species.
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23. The computer-implemented method of clause 21, wherein the pan-amino
acid
conservation frequencies sequence specifies whether there is a missing
conservation frequency
for a particular amino acid.
24. The computer-implemented method of clause 21, further comprising, for
each of the
voxels,
selecting respective nearest atoms in respective ones of the amino acids,
selecting respective per-amino acid conservation frequencies for respective
residues of the amino
acids that include the nearest atoms, and
making the per-amino acid conservation frequencies available as one of the
evolutionary
profiles.
25. The computer-implemented method of clause 24, wherein the per-amino
acid
conservation frequencies are configured for a particular position of the
residues as observed in
the plurality of species.
26. The computer-implemented method of clause 24, wherein the per-amino
acid
conservation frequencies specify whether there is a missing conservation
frequency for a
particular amino acid
27. The computer-implemented method of clause 1, wherein the tensor further
includes
annotation channels for the amino acids, wherein the annotation channels are
one-hot encoded in
the tensor.
28. The computer-implemented method of clause 27, wherein the annotation
channels are
molecular processing annotations that include initiator methionine, signal,
transit peptide,
propeptide, chain, and peptide.
29. The computer-implemented method of clause 27, wherein the annotation
channels are
regions annotations that include topological domain, transmembrane,
intramembrane, domain,
repeat, calcium binding, zinc finger, deoxyribonucleic acid (DNA) binding,
nucleotide binding,
region, coiled coil, motif, and compositional bias.
30. The computer-implemented method of clause 27, wherein the annotation
channels are
sites annotations that include active site, metal binding, binding site, and
site.
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31. The computer-implemented method of clause 27, wherein the annotation
channels are
amino acid modifications annotations that include non-standard residue,
modified residue,
lipidation, glycosylation, disulfide bond, and cross-link.
32. The computer-implemented method of clause 27, wherein the annotation
channels are
secondary structure annotations that include helix, turn, and beta strand.
33. The computer-implemented method of clause 27, wherein the annotation
channels are
experimental information annotations that include mutagenesis, sequence
uncertainty, sequence
conflict, non-adjacent residues, and non-terminal residue.
34. The computer-implemented method of clause 1, wherein the tensor further
includes
structure confidence channels for the amino acids that specify quality of
respective structures of
the amino acids.
35. The computer-implemented method of clause 34, wherein the structure
confidence
channels are global model quality estimations (GMQEs).
36. The computer-implemented method of clause 34, wherein the structure
confidence
channels include qualitative model energy analysis (QMEAN) scores.
37. The computer-implemented method of clause 34, wherein the structure
confidence
channels are temperature factors that specify a degree to which the residues
satisfy physical
constraints of respective protein structures.
38. The computer-implemented method of clause 34, wherein the structure
confidence
channels are template structures alignments that specify a degree to which
residues of atoms
nearest to the voxels have aligned template structures.
39. The computer-implemented method of clause 38, wherein the structure
confidence
channels are template modeling scores of the aligned template structures.
40. The computer-implemented method of clause 39, wherein the structure
confidence
channels are a minimum one of the template modeling scores, a mean of the
template modeling
scores, and a maximum one of the template modeling scores.
41. The computer-implemented method of clause 1, further comprising voxel-
wise
concatenating amino acid-wise distance channels for the alpha carbon atoms
with the one-hot
encoding of the alternative allele to generate the tensor.
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42. The computer-implemented method of clause 41, further comprising voxel-
wise
concatenating amino acid-wise distance channels for the beta carbon atoms with
the one-hot
encoding of the alternative allele to generate the tensor.
43. The computer-implemented method of clause 42, further comprising voxel-
wise
concatenating the amino acid-wise distance channels for the alpha carbon
atoms, the amino acid-
wise distance channels for the beta carbon atoms, and the one-hot encoding of
the alternative
allele to generate the tensor.
44. The computer-implemented method of clause 43, further comprising voxel-
wise
concatenating the amino acid-wise distance channels for the alpha carbon
atoms, the amino acid-
wise distance channels for the beta carbon atoms, the one-hot encoding of the
alternative allele,
and pan-amino acid conservation frequencies sequences to generate the tensor.
45. The computer-implemented method of clause 44, further comprising voxel-
wise
concatenating the amino acid-wise distance channels for the alpha carbon
atoms, the amino acid-
wise distance channels for the beta carbon atoms, the one-hot encoding of the
alternative allele,
the pan-amino acid conservation frequencies sequences, and the annotation
channels to generate
the tensor.
46. The computer-implemented method of clause 45, further comprising voxel-
wise
concatenating the amino acid-wise distance channels for the alpha carbon
atoms, the amino acid-
wise distance channels for the beta carbon atoms, the one-hot encoding of the
alternative allele,
the pan-amino acid conservation frequencies sequences, the annotation
channels, and the
structure confidence channels to generate the tensor.
47. The computer-implemented method of clause 46, further comprising voxel-
wise
concatenating the amino acid-wise distance channels for the alpha carbon
atoms, the amino acid-
wise distance channels for the beta carbon atoms, the one-hot encoding of the
alternative allele,
and per-amino acid conservation frequencies for each of the amino acids to
generate the tensor.
48. The computer-implemented method of clause 47, further comprising voxel-
wise
concatenating the amino acid-wise distance channels for the alpha carbon
atoms, the amino acid-
wise distance channels for the beta carbon atoms, the one-hot encoding of the
alternative allele,
per-amino acid conservation frequencies for each of the amino acids, and the
annotation channels
to generate the tensor.
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49. The computer-implemented method of clause 48, further comprising voxel-
wise
concatenating the amino acid-wise distance channels for the alpha carbon
atoms, the amino acid-
wise distance channels for the beta carbon atoms, the one-hot encoding of the
alternative allele,
per-amino acid conservation frequencies for each of the amino acids, the
annotation channels,
and the structure confidence channels to generate the tensor.
50. The computer-implemented method of clause 49, further comprising voxel-
wise
concatenating the amino acid-wise distance channels for the alpha carbon
atoms, the amino acid-
wise distance channels for the beta carbon atoms, the one-hot encoding of the
alternative allele,
and the one-hot encoding of the reference allele to generate the tensor.
51. The computer-implemented method of clause 50, further comprising voxel-
wise
concatenating the amino acid-wise distance channels for the alpha carbon
atoms, the amino acid-
wise distance channels for the beta carbon atoms, the one-hot encoding of the
alternative allele,
the one-hot encoding of the reference allele, and the pan-amino acid
conservation frequencies
sequences to generate the tensor.
52. The computer-implemented method of clause 51, further comprising voxel-
wise
concatenating the amino acid-wise distance channels for the alpha carbon
atoms, the amino acid-
wise distance channels for the beta carbon atoms, the one-hot encoding of the
alternative allele,
the one-hot encoding of the reference allele, the pan-amino acid conservation
frequencies
sequences, and the annotation channels to generate the tensor.
53. The computer-implemented method of clause 52, further comprising voxel-
wise
concatenating the amino acid-wise distance channels for the alpha carbon
atoms, the amino acid-
wise distance channels for the beta carbon atoms, the one-hot encoding of the
alternative allele,
the one-hot encoding of the reference allele, the pan-amino acid conservation
frequencies
sequences, the annotation channels, and the structure confidence channels to
generate the tensor.
54. The computer-implemented method of clause 53, further comprising voxel-
wise
concatenating the amino acid-wise distance channels for the alpha carbon
atoms, the amino acid-
wise distance channels for the beta carbon atoms, the one-hot encoding of the
alternative allele,
the one-hot encoding of the reference allele, and the per-amino acid
conservation frequencies for
each of the amino acids to generate the tensor.
55. The computer-implemented method of clause 54, further comprising voxel-
wise
concatenating the amino acid-wise distance channels for the alpha carbon
atoms, the amino acid-
wise distance channels for the beta carbon atoms, the one-hot encoding of the
alternative allele,
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the one-hot encoding of the reference allele, the per-amino acid conservation
frequencies for
each of the amino acids, and the annotation channels to generate the tensor.
56. The computer-implemented method of clause 55, further comprising voxel-
wise
concatenating the amino acid-wise distance channels for the alpha carbon
atoms, the amino acid-
wise distance channels for the beta carbon atoms, the one-hot encoding of the
alternative allele,
the one-hot encoding of the reference allele, the per-amino acid conservation
frequencies for
each of the amino acids, the annotation channels, and the structure confidence
channels to
generate the tensor.
57. The computer-implemented method of clause 1, further comprising
rotating atoms of the
amino acids before the amino acid-wise distance channels are generated.
58. The computer-implemented method of clause 1, further comprising using 1
x 1 x 1
convolutions, 3 x 3 x 3 convolutions, rectified linear unit activation layers,
batch normalization
layers, a fully-connected layer, a dropout regularization layer, and a softmax
classification layer
in a convolutional neural network.
59. The computer-implemented method of clause 58, wherein the 1 x 1 x 1
convolutions and
the 3 x 3 x 3 convolutions are three-dimensional convolutions.
60. The computer-implemented method of clause 58, wherein a layer of the 1
x 1 x 1
convolutions processes the tensor and produces an intermediate output that is
a convolved
representation of the tensor, wherein a sequence of layers of the 3 x 3 x 3
convolutions processes
the intermediate output and produces a flattened output, wherein the fully-
connected layer
processes the flattened output and produces unnormalized outputs, and wherein
the softmax
classification layer processes the unnormalized outputs and produces
exponentially normalized
outputs that identify likelihoods of the variant being pathogenic and benign.
61. The computer-implemented method of clause 60, wherein a sigmoid layer
processes the
unnormalized outputs and produces a normalized output that identifies a
likelihood of the variant
being pathogenic.
62. The computer-implemented method of clause 60, wherein the voxels, the
atoms, and the
distances have three-dimensional coordinates, wherein the tensor has at least
three dimensions,
wherein the intermediate output has at least three dimensions, and wherein the
flattened output
has one dimension.
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63. A computer-implemented method, comprising:
storing atom category-wise distance channels for amino acids in a protein,
wherein the amino acids have atoms for a plurality of atom categories,
wherein atom categories in the plurality of atom categories specify atomic
elements of the amino
acids,
wherein each of the atom category-wise distance channels has voxel-wise
distance values for
voxels in a plurality of voxels, and
wherein the voxel-wise distance values specify distances from corresponding
voxels in the
plurality of voxels to atoms in corresponding atom categories in the plurality
of atom
categories;
processing a tensor that includes the atom category-wise distance channels and
an alternative
allele of the protein expressed by a variant; and
determining a pathogenicity of the variant based at least in part on the
tensor.
64. The computer-implemented method of clause 63, further comprising
centering a voxel
grid of the voxels on respective atoms of respective atom categories in the
plurality of atom
categories.
65. The computer-implemented method of clause 64, further comprising
centering the voxel
grid on an alpha carbon atom of a residue of at least one variant amino acid
in the protein.
66. The computer-implemented method of clause 65, wherein the distances are
nearest-atom
distances from corresponding voxel centers in the voxel grid to nearest atoms
in the
corresponding atom categories.
67. The computer-implemented method of clause 66, wherein the nearest-atom
distances are
Euclidean distances.
68. The computer-implemented method of clause 67, wherein the nearest-atom
distances are
normalized by dividing the Euclidean distances with a maximum nearest-atom
distances.
69. The computer-implemented method of clause 68, wherein the distances are
nearest-atom
distances from the corresponding voxel centers in the voxel grid to nearest
atoms irrespective of
the amino acids and the atom categories of the amino acids.
70. The computer-implemented method of clause 69, wherein the nearest-atom
distances are
Euclidean distances.
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71. The computer-implemented method of clause 70, wherein the
nearest-atom distances are
normalized by dividing the Euclidean distances with a maximum nearest-atom
distances.
Clause Set 2
1. One or more computer-readable media storing computer-executable
instructions that,
when executed on one or more processors, configure a computer to perform
operations
comprising:
storing amino acid-wise distance channels for a plurality of amino acids in a
protein,
wherein each of the amino acid-wise distance channels has voxel-wise distance
values for voxels
in a plurality of voxels, and
wherein the voxel-wise distance values specify distances from corresponding
voxels in the
plurality of voxels to atoms of corresponding amino acids in the plurality of
amino acids;
processing a tensor that includes the amino acid-wise distance channels and an
alternative allele
of the protein expressed by a variant; and
determining a pathogenicity of the variant based at least in part on the
tensor.
2. The computer-readable media of clause 1, the operations further
comprising centering a
voxel grid of the voxels on an alpha carbon atom of respective residues of the
amino acids.
3. The computer-readable media of clause 2, the operations further
comprising centering the
voxel grid on an alpha carbon atom of a residue of a particular amino acid
that corresponds to at
least one variant amino acid in the protein.
4. The computer-readable media of clause 3, the operations further
comprising encoding, in
the tensor, a directionality of the amino acids and a position of the
particular amino acid by
multiplying, with a directionality parameter, voxel-wise distance values for
those amino acids
that precede the particular amino acid.
5. The computer-readable media of clause 3, wherein the distances are
nearest-atom
distances from corresponding voxel centers in the voxel grid to nearest atoms
of the
corresponding amino acids.
6. The computer-readable media of clause 5, wherein the nearest-atom
distances are
Euclidean distances.
7. The computer-readable media of clause 6, wherein the nearest-atom
distances are
normalized by dividing the Euclidean distances with a maximum nearest-atom
distance.
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8. The computer-readable media of clause 5, wherein the amino acids have
alpha carbon
atoms, and wherein the distances are nearest-alpha carbon atom distances from
the
corresponding voxel centers to nearest alpha carbon atoms of the corresponding
amino acids.
9. The computer-readable media of clause 5, wherein the amino acids have
beta carbon
atoms and wherein the distances are nearest-beta carbon atom distances from
the corresponding
voxel centers to nearest beta carbon atoms of the corresponding amino acids.
10. The computer-readable media of clause 5, wherein the amino acids have
backbone atoms
and wherein the distances are nearest-backbone atom distances from the
corresponding voxel
centers to nearest backbone atoms of the corresponding amino acids.
11. The computer-readable media of clause 5, wherein the amino acids have
sidechain atom
and wherein the distances are nearest-sidechain atom distances from the
corresponding voxel
centers to nearest sidechain atoms of the corresponding amino acids.
12. The computer-readable media of clause 3, the operations further
comprising encoding, in
the tensor, a nearest atom channel that specifies a distance from each voxel
to a nearest atom,
wherein the nearest atom is selected irrespective of the amino acids and
atomic elements of the
amino acids.
13. The computer-readable media of clause 12, wherein the distance is a
Euclidean distance.
14. The computer-readable media of clause 13, wherein the distance is
normalized by
dividing the Euclidean distance with a maximum distance.
15. The computer-readable media of clause 12, wherein the amino acids
include non-standard
amino acids.
16. The computer-readable media of clause 1, wherein the tensor further
includes an absentee
atom channel that specifies atoms not found within a predefined radius of a
voxel center, and
wherein the absentee atom channel is one-hot encoded.
17. The computer-readable media of clause 1, wherein the tensor further
includes a one-hot
encoding of the alternative allele that is voxel-wise encoded to each of the
amino acid-wise
distance channels.
18. The computer-readable media of clause 1, wherein the tensor further
includes a reference
allele of the protein.
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19. The computer-readable media of clause 18, wherein the tensor further
includes a one-hot
encoding of the reference allele that is voxel-wise encoded to each of the
amino acid-wise
distance channels.
20. The computer-readable media of clause 1, wherein the tensor further
includes
evolutionary profiles that specify conservation levels of the amino acids
across a plurality of
species.
21. The computer-readable media of clause 20, the operations further
comprising, for each of
the voxels,
selecting a nearest atom across the amino acids and the atom categories,
selecting a pan-amino acid conservation frequencies sequence for a residue of
an amino acid that
includes the nearest atom, and
making the pan-amino acid conservation frequencies sequence available as one
of the
evolutionary profiles
22. The computer-readable media of clause 21, wherein the pan-amino acid
conservation
frequencies sequence is configured for a particular position of the residue as
observed in the
plurality of species
23. The computer-readable media of clause 21, wherein the pan-amino acid
conservation
frequencies sequence specifies whether there is a missing conservation
frequency for a particular
amino acid.
24. The computer-readable media of clause 21, the operations further
comprising, for each of
the voxels,
selecting respective nearest atoms in respective ones of the amino acids,
selecting respective per-amino acid conservation frequencies for respective
residues of the amino
acids that include the nearest atoms, and
making the per-amino acid conservation frequencies available as one of the
evolutionary
profiles.
25. The computer-readable media of clause 24, wherein the per-amino acid
conservation
frequencies are configured for a particular position of the residues as
observed in the plurality of
species.
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26. The computer-readable media of clause 24, wherein the per-amino acid
conservation
frequencies specify whether there is a missing conservation frequency for a
particular amino
acid.
27. The computer-readable media of clause 1, wherein the tensor further
includes annotation
channels for the amino acids, wherein the annotation channels are one-hot
encoded in the tensor.
28. The computer-readable media of clause 27, wherein the annotation
channels are
molecular processing annotations that include initiator methionine, signal,
transit peptide,
propeptide, chain, and peptide.
29. The computer-readable media of clause 27, wherein the annotation
channels are regions
annotations that include topological domain, transmembrane, intramembrane,
domain, repeat,
calcium binding, zinc finger, deoxyribonucleic acid (DNA) binding, nucleotide
binding, region,
coiled coil, motif, and compositional bias.
30. The computer-readable media of clause 27, wherein the annotation
channels are sites
annotations that include active site, metal binding, binding site, and site.
31. The computer-readable media of clause 27, wherein the annotation
channels are amino
acid modifications annotations that include non-standard residue, modified
residue, lipidation,
glycosylation, disulfide bond, and cross-link.
32. The computer-readable media of clause 27, wherein the annotation
channels are
secondary structure annotations that include helix, turn, and beta strand.
33. The computer-readable media of clause 27, wherein the annotation
channels are
experimental information annotations that include mutagenesis, sequence
uncertainty, sequence
conflict, non-adjacent residues, and non-terminal residue.
34. The computer-readable media of clause 1, wherein the tensor further
includes structure
confidence channels for the amino acids that specify quality of respective
structures of the amino
acids.
35. The computer-readable media of clause 34, wherein the structure
confidence channels are
global model quality estimations (GMQEs).
36. The computer-readable media of clause 34, wherein the structure
confidence channels
include qualitative model energy analysis (QMEAN) scores.
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37. The computer-readable media of clause 34, wherein the structure
confidence channels are
temperature factors that specify a degree to which the residues satisfy
physical constraints of
respective protein structures.
38. The computer-readable media of clause 34, wherein the structure
confidence channels are
template structures alignments that specify a degree to which residues of
atoms nearest to the
voxels have aligned template structures.
39. The computer-readable media of clause 38, wherein the structure
confidence channels are
template modeling scores of the aligned template structures.
40. The computer-readable media of clause 39, wherein the structure
confidence channels are
a minimum one of the template modeling scores, a mean of the template modeling
scores, and a
maximum one of the template modeling scores.
41. The computer-readable media of clause 1, the operations further
comprising voxel-wise
concatenating amino acid-wise distance channels for the alpha carbon atoms
with the one-hot
encoding of the alternative allele to generate the tensor.
42. The computer-readable media of clause 41, the operations further
comprising voxel-wise
concatenating amino acid-wise distance channels for the beta carbon atoms with
the one-hot
encoding of the alternative allele to generate the tensor.
43. The computer-readable media of clause 42, the operations further
comprising voxel-wise
concatenating the amino acid-wise distance channels for the alpha carbon
atoms, the amino acid-
wise distance channels for the beta carbon atoms, and the one-hot encoding of
the alternative
allele to generate the tensor.
44. The computer-readable media of clause 43, the operations further
comprising voxel-wise
concatenating the amino acid-wise distance channels for the alpha carbon
atoms, the amino acid-
wise distance channels for the beta carbon atoms, the one-hot encoding of the
alternative allele,
and pan-amino acid conservation frequencies sequences to generate the tensor.
45. The computer-readable media of clause 44, the operations further
comprising voxel-wise
concatenating the amino acid-wise distance channels for the alpha carbon
atoms, the amino acid-
wise distance channels for the beta carbon atoms, the one-hot encoding of the
alternative allele,
the pan-amino acid conservation frequencies sequences, and the annotation
channels to generate
the tensor.
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46. The computer-readable media of clause 45, the operations further
comprising voxel-wise
concatenating the amino acid-wise distance channels for the alpha carbon
atoms, the amino acid-
wise distance channels for the beta carbon atoms, the one-hot encoding of the
alternative allele,
the pan-amino acid conservation frequencies sequences, the annotation
channels, and the
structure confidence channels to generate the tensor.
47. The computer-readable media of clause 46, the operations further
comprising voxel-wise
concatenating the amino acid-wise distance channels for the alpha carbon
atoms, the amino acid-
wise distance channels for the beta carbon atoms, the one-hot encoding of the
alternative allele,
and per-amino acid conservation frequencies for each of the amino acids to
generate the tensor.
48. The computer-readable media of clause 47, the operations further
comprising voxel-wise
concatenating the amino acid-wise distance channels for the alpha carbon
atoms, the amino acid-
wise distance channels for the beta carbon atoms, the one-hot encoding of the
alternative allele,
per-amino acid conservation frequencies for each of the amino acids, and the
annotation channels
to generate the tensor.
49. The computer-readable media of clause 48, the operations further
comprising voxel-wise
concatenating the amino acid-wise distance channels for the alpha carbon
atoms, the amino acid-
wise distance channels for the beta carbon atoms, the one-hot encoding of the
alternative allele,
per-amino acid conservation frequencies for each of the amino acids, the
annotation channels,
and the structure confidence channels to generate the tensor.
50. The computer-readable media of clause 49, the operations further
comprising voxel-wise
concatenating the amino acid-wise distance channels for the alpha carbon
atoms, the amino acid-
wise distance channels for the beta carbon atoms, the one-hot encoding of the
alternative allele,
and the one-hot encoding of the reference allele to generate the tensor.
51. The computer-readable media of clause 50, the operations further
comprising voxel-wise
concatenating the amino acid-wise distance channels for the alpha carbon
atoms, the amino acid-
wise distance channels for the beta carbon atoms, the one-hot encoding of the
alternative allele,
the one-hot encoding of the reference allele, and the pan-amino acid
conservation frequencies
sequences to generate the tensor.
52. The computer-readable media of clause 51, the operations further
comprising voxel-wise
concatenating the amino acid-wise distance channels for the alpha carbon
atoms, the amino acid-
wise distance channels for the beta carbon atoms, the one-hot encoding of the
alternative allele,
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the one-hot encoding of the reference allele, the pan-amino acid conservation
frequencies
sequences, and the annotation channels to generate the tensor.
53. The computer-readable media of clause 52, the operations further
comprising voxel-wise
concatenating the amino acid-wise distance channels for the alpha carbon
atoms, the amino acid-
wise distance channels for the beta carbon atoms, the one-hot encoding of the
alternative allele,
the one-hot encoding of the reference allele, the pan-amino acid conservation
frequencies
sequences, the annotation channels, and the structure confidence channels to
generate the tensor.
54. The computer-readable media of clause 53, the operations further
comprising voxel-wise
concatenating the amino acid-wise distance channels for the alpha carbon
atoms, the amino acid-
wise distance channels for the beta carbon atoms, the one-hot encoding of the
alternative allele,
the one-hot encoding of the reference allele, and the per-amino acid
conservation frequencies for
each of the amino acids to generate the tensor.
55. The computer-readable media of clause 54, the operations further
comprising voxel-wise
concatenating the amino acid-wise distance channels for the alpha carbon
atoms, the amino acid-
wise distance channels for the beta carbon atoms, the one-hot encoding of the
alternative allele,
the one-hot encoding of the reference allele, the per-amino acid conservation
frequencies for
each of the amino acids, and the annotation channels to generate the tensor.
56. The computer-readable media of clause 55, the operations further
comprising voxel-wise
concatenating the amino acid-wise distance channels for the alpha carbon
atoms, the amino acid-
wise distance channels for the beta carbon atoms, the one-hot encoding of the
alternative allele,
the one-hot encoding of the reference allele, the per-amino acid conservation
frequencies for
each of the amino acids, the annotation channels, and the structure confidence
channels to
generate the tensor.
57. The computer-readable media of clause 1, the operations further
comprising rotating
atoms of the amino acids before the amino acid-wise distance channels are
generated.
58. The computer-readable media of clause 1, the operations further
comprising using 1 x 1 x
1 convolutions, 3 x 3 x 3 convolutions, rectified linear unit activation
layers, batch normalization
layers, a fully-connected layer, a dropout regularization layer, and a softmax
classification layer
in a convolutional neural network.
59. The computer-readable media of clause 58, wherein the 1 x 1 x 1
convolutions and the 3
x 3 x 3 convolutions are three-dimensional convolutions.
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60. The computer-readable media of clause 58, wherein a layer of the 1 x 1
x 1 convolutions
processes the tensor and produces an intermediate output that is a convolved
representation of
the tensor, wherein a sequence of layers of the 3 x 3 x 3 convolutions
processes the intermediate
output and produces a flattened output, wherein the fully-connected layer
processes the flattened
output and produces unnormalized outputs, and wherein the softmax
classification layer
processes the unnormalized outputs and produces exponentially normalized
outputs that identify
likelihoods of the variant being pathogenic and benign.
61. The computer-readable media of clause 60, wherein a sigmoid layer
processes the
unnormalized outputs and produces a normalized output that identifies a
likelihood of the variant
being pathogenic.
62. The computer-readable media of clause 60, wherein the voxels, the
atoms, and the
distances have three-dimensional coordinates, wherein the tensor has at least
three dimensions,
wherein the intermediate output has at least three dimensions, and wherein the
flattened output
has one dimension.
63. One or more computer-readable media storing computer-executable
instructions that,
when executed on one or more processors, configure a computer to perform
operations
comprising:
storing atom category-wise distance channels for amino acids in a protein,
wherein the amino acids have atoms for a plurality of atom categories,
wherein atom categories in the plurality of atom categories specify atomic
elements of the amino
acids,
wherein each of the atom category-wise distance channels has voxel-wise
distance values for
voxels in a plurality of voxels, and
wherein the voxel-wise distance values specify distances from corresponding
voxels in the
plurality of voxels to atoms in corresponding atom categories in the plurality
of atom
categories;
processing a tensor that includes the atom category-wise distance channels and
an alternative
allele of the protein expressed by a variant; and
determining a pathogenicity of the variant based at least in part on the
tensor.
64. The computer-readable media of clause 63, the operations further
comprising centering a
voxel grid of the voxels on respective atoms of respective atom categories in
the plurality of
atom categories.
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65. The computer-readable media of clause 64, the operations further
comprising centering
the voxel grid on an alpha carbon atom of a residue of at least one variant
amino acid in the
protein.
66. The computer-readable media of clause 65, wherein the distances are
nearest-atom
distances from corresponding voxel centers in the voxel grid to nearest atoms
in the
corresponding atom categories.
67. The computer-readable media of clause 66, wherein the nearest-atom
distances are
Euclidean distances.
68. The computer-readable media of clause 67, wherein the nearest-atom
distances are
normalized by dividing the Euclidean distances with a maximum nearest-atom
distances.
69. The computer-readable media of clause 68, wherein the distances are
nearest-atom
distances from the corresponding voxel centers in the voxel grid to nearest
atoms irrespective of
the amino acids and the atom categories of the amino acids.
70. The computer-readable media of clause 69, wherein the nearest-atom
distances are
Euclidean distances.
71. The computer-readable media of clause 70, wherein the nearest-atom
distances are
normalized by dividing the Euclidean distances with a maximum nearest-atom
distances.
Particular Implementations 2
102401 In some implementations, a system comprises a voxelizer that
accesses a three-
dimensional structure of a reference amino acid sequence of a protein and fits
a three-
dimensional grid of voxels on atoms in the three-dimensional structure on an
amino acid-basis to
generate amino acid-wise distance channels. Each of the amino acid-wise
distance channels has a
three-dimensional distance value for each voxel in the three-dimensional grid
of voxels. The
three-dimensional distance value specifies a distance from a corresponding
voxel in the three-
dimensional grid of voxels to atoms of a corresponding reference amino acid in
the reference
amino acid sequence. The system further comprises an alternative allele
encoder that encodes an
alternative allele amino acid to each voxel in the three-dimensional grid of
voxels The
alternative allele amino acid is a three-dimensional representation of a one-
hot encoding of a
variant amino acid expressed by a variant nucleotide. The system further
comprises an
evolutionary conservation encoder that encodes an evolutionary conservation
sequence to each
voxel in the three-dimensional grid of voxels. The evolutionary conservation
sequence can be a
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three-dimensional representation of amino acid-specific conservation
frequencies across a
plurality of species. The amino acid-specific conservation frequencies can be
selected in
dependence upon amino acid proximity to the corresponding voxel. The system
further
comprises a convolutional neural network configured to apply three-dimensional
convolutions to
a tensor that includes the amino acid-wise distance channels encoded with the
alternative allele
amino acid and respective evolutionary conservation sequences. The
convolutional neural
network can be also configured to determine a pathogenicity of the variant
nucleotide based at
least in part on the tensor.
102411 The voxelizer can center the three-dimensional grid of
voxels on an alpha-carbon
atom of respective residues of reference amino acids in the reference amino
acid sequence. The
voxelizer can center the three-dimensional grid of voxels on an alpha-carbon
atom of a residue of
a particular reference amino acid positioned at the variant amino acid.
102421 In some implementations, the system can be further
configured to encode, in the
tensor, a directionality of the reference amino acids in the reference amino
acid sequence and a
position of the particular reference amino acid by multiplying, with a
directionality parameter,
three-dimensional distance values for those reference amino acids that precede
the particular
reference amino acid. The distances can be nearest-atom distances from
corresponding voxel
centers in the three-dimensional grid of voxels to nearest atoms of the
corresponding reference
amino acids. The nearest-atom distances can be Euclidean distances and can be
normalized by
dividing the Euclidean distances with a maximum nearest-atom distance.
102431 In some implementations, the reference amino acids can have
alpha-carbon atoms and
the distances can be nearest-alpha-carbon atom distances from the
corresponding voxel centers to
nearest alpha-carbon atoms of the corresponding reference amino acids. In some
implementations, the reference amino acids can have beta-carbon atoms and the
distances can be
nearest-beta-carbon atom distances from the corresponding voxel centers to
nearest beta-carbon
atoms of the corresponding reference amino acids. In some implementations, the
reference amino
acids can have backbone atoms and the distances can be nearest-backbone atom
distances from
the corresponding voxel centers to nearest backbone atoms of the corresponding
reference amino
acids. In some implementations, the amino acids can have sidechain atoms and
the distances can
be nearest-sidechain atom distances from the corresponding voxel centers to
nearest sidechain
atoms of the corresponding reference amino acids.
102441 In some implementations, the system can be further
configured to encode, in the
tensor, a nearest atom channel that specifies a distance from each voxel to a
nearest atom. The
nearest atom can be selected irrespective of the amino acids and atomic
elements of the amino
acids. The distance can be a Euclidean distance and can be normalized by
dividing the Euclidean
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distance with a maximum distance. The amino acids can include non-standard
amino acids. The
tensor can further include an absentee atom channel that specifies atoms not
found within a
predefined radius of a voxel center. The absentee atom channel can be one-hot
encoded.
[0245] In some implementations, the system can further comprise a
reference allele encoder
that voxel-wise encodes a reference allele amino acid to each three-
dimensional distance value
on the amino acid position-basis. The reference allele amino acid can be a
three-dimensional
representation of a one-hot encoding of the reference amino acid sequence. The
amino acid-
specific conservation frequencies can specify conservation levels of
respective amino acids
across the plurality of species.
[0246] In some implementations, the evolutionary conservation
encoder can select a nearest
atom to the corresponding voxel across the reference amino acids and the atom
categories, can
select pan-amino acid conservation frequencies for a residue of a reference
amino acid that
includes the nearest atom, and can use a three-dimensional representation of
the pan-amino acid
conservation frequencies as the evolutionary conservation sequence. The pan-
amino acid
conservation frequencies can be configured for a particular position of the
residue as observed in
the plurality of species. The pan-amino acid conservation frequencies can
specify whether there
is a missing conservation frequency for a particular reference amino acid.
[0247] In some implementations, the evolutionary conservation
encoder can select respective
nearest atoms to the corresponding voxel in respective ones of the reference
amino acids, can
select respective per-amino acid conservation frequencies for respective
residues of the reference
amino acids that include the nearest atoms, and can use a three-dimensional
representation of the
per-amino acid conservation frequencies as the evolutionary conservation
sequence. The per-
amino acid conservation frequencies can be configured for a particular
position of the residues as
observed in the plurality of species. The per-amino acid conservation
frequencies can specify
whether there is a missing conservation frequency for a particular reference
amino acid.
[0248] In some implementations, the system can further comprise an
annotations encoder
that voxel-wise encodes one or more annotation channels to each three-
dimensional distance
value The annotation channels can be three-dimensional representations of a
one-hot encoding
of residue annotations and can be molecular processing annotations that
include initiator
methionine, signal, transit peptide, propeptide, chain, and peptide. In some
implementations, the
annotation channels can be regions annotations that include topological
domain, transmembrane,
intramembrane, domain, repeat, calcium binding, zinc finger, deoxyribonucleic
acid (DNA)
binding, nucleotide binding, region, coiled coil, motif, and compositional
bias or can be sites
annotations that include active site, metal binding, binding site, and site.
In some
implementations, the annotation channels can be amino acid modifications
annotations that
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include non-standard residue, modified residue, lipidation, glycosylation,
disulfide bond, and
cross-link or can be secondary structure annotations that include helix, turn,
and beta strand. The
annotation channels can be experimental information annotations that include
mutagenesis,
sequence uncertainty, sequence conflict, non-adjacent residues, and non-
terminal residue.
[0249] In some implementations, the system can further comprise a
structure confidence
encoder that voxel-wise encodes one or more structure confidence channels to
each three-
dimensional distance value. The structure confidence channels can be three-
dimensional
representations of confidence scores that specify quality of respective
residue structures. The
structure confidence channels can be global model quality estimations (GMQEs),
can be
qualitative model energy analysis (QMEAN) scores, can be temperature factors
that specify a
degree to which the residues satisfy physical constraints of respective
protein structures, can be
template structures alignments that specify a degree to which residues of
atoms nearest to the
voxels have aligned template structures, can be template modeling scores of
the aligned template
structures, or can be a minimum one of the template modeling scores, a mean of
the template
modeling scores, and a maximum one of the template modeling scores.
[0250] In some implementations, the system can further comprise an
atoms rotation engine
that rotates the atoms before the amino acid-wise distance channels are
generated.
[0251] The convolutional neural network can use 1 x 1 x 1
convolutions, 3 x 3 x 3
convolutions, rectified linear unit activation layers, batch normalization
layers, a fully-connected
layer, a dropout regularization layer, and a softmax classification layer. The
1 x 1 x 1
convolutions and the 3 x 3 x 3 convolutions can be the three-dimensional
convolutions. In some
implementations, a layer of the 1 x 1 x 1 convolutions can process the tensor
and produce an
intermediate output that is a convolved representation of the tensor. A
sequence of layers of the 3
x 3 x 3 convolutions can process the intermediate output and produce a
flattened output. The
fully-connected layer can process the flattened output and produce
unnormalized outputs. The
softmax classification layer can process the unnormalized outputs and produce
exponentially
normalized outputs that identify likelihoods of the variant nucleotide being
pathogenic and
benign
[0252] In some implementations, a sigmoid layer can process the
unnormalized outputs and
produce a normalized output that identifies a likelihood of the variant
nucleotide being
pathogenic. The convolutional neural network can be an attention-based neural
network. The
tensor can include the amino acid-wise distance channels further encoded with
the reference
allele amino acid, can include the amino acid-wise distance channels further
encoded with the
annotation channels, or can include the amino acid-wise distance channels
further encoded with
the structure confidence channels.
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102531 In some implementations, a system can comprise a voxelizer
that accesses a three-
dimensional structure of a reference amino acid sequence of a protein and fits
a three-
dimensional grid of voxels on atoms in the three-dimensional structure on an
amino acid-basis to
generate atom category-wise distance channels. The atoms span a plurality of
atom categories,
which specify atomic elements of the amino acids. Each of the atom category-
wise distance
channels has a three-dimensional distance value for each voxel in the three-
dimensional grid of
voxels. The three-dimensional distance value specifies a distance from a
corresponding voxel in
the three-dimensional grid of voxels to atoms of corresponding atom categories
in the plurality
of atom categories. The system further comprises an alternative allele encoder
that encodes an
alternative allele amino acid to each voxel in the three-dimensional grid of
voxels. The
alternative allele amino acid is a three-dimensional representation of a one-
hot encoding of a
variant amino acid expressed by a variant nucleotide. The system further
comprises an
evolutionary conservation encoder that encodes an evolutionary conservation
sequence to each
voxel in the three-dimensional grid of voxels. The evolutionary conservation
sequence can be a
three-dimensional representation of amino acid-specific conservation
frequencies across a
plurality of species. The amino acid-specific conservation frequencies can be
selected in
dependence upon amino acid proximity to the corresponding voxel. The system
further
comprises a convolutional neural network configured to apply three-dimensional
convolutions to
a tensor that includes the atom category-wise distance channels encoded with
the alternative
allele amino acid and respective evolutionary conservation sequences, and to
determine a
pathogenicity of the variant nucleotide based at least in part on the tensor.
102541 In some implementations, a system comprises a voxelizer that
accesses a three-
dimensional structure of a reference amino acid sequence of a protein and fits
a three-
dimensional grid of voxels on atoms in the three-dimensional structure on an
amino acid-basis to
generate amino acid-wise distance channels. Each of the amino acid-wise
distance channels can
have a three-dimensional distance value for each voxel in the three-
dimensional grid of voxels.
The three-dimensional distance value can specify a distance from a
corresponding voxel in the
three-dimensional grid of voxels to atoms of a corresponding reference amino
acid in the
reference amino acid sequence. The system further comprises an alternative
allele encoder that
encodes an alternative allele amino acid to each voxel in the three-
dimensional grid of voxels.
The alternative allele amino acid is a three-dimensional representation of a
one-hot encoding of a
variant amino acid expressed by a variant nucleotide. The system further
comprises an
evolutionary conservation encoder that encodes an evolutionary conservation
sequence to each
voxel in the three-dimensional grid of voxels. The evolutionary conservation
sequence can be a
three-dimensional representation of amino acid-specific conservation
frequencies across a
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plurality of species. The amino acid-specific conservation frequencies can be
selected in
dependence upon amino acid proximity to the corresponding voxel. The system
further
comprises a tensor generator configured to generate a tensor that includes the
amino acid-wise
distance channels encoded with the alternative allele amino acid and
respective evolutionary
conservation sequences.
102551 In some implementations, a system comprises a voxelizer that
accesses a three-
dimensional structure of a reference amino acid sequence of a protein and fits
a three-
dimensional grid of voxels on atoms in the three-dimensional structure on an
amino acid-basis to
generate atom category-wise distance channels. The atoms can span a plurality
of atom
categories, which specify atomic elements of the amino acids. Each of the atom
category-wise
distance channels can have a three-dimensional distance value for each voxel
in the three-
dimensional grid of voxels. The three-dimensional distance value can specify a
distance from a
corresponding voxel in the three-dimensional grid of voxels to atoms of
corresponding atom
categories in the plurality of atom categories. The system further comprises
an alternative allele
encoder that encodes an alternative allele amino acid to each voxel in the
three-dimensional grid
of voxels. The alternative allele amino acid is a three-dimensional
representation of a one-hot
encoding of a variant amino acid expressed by a variant nucleotide. The system
further
comprises an evolutionary conservation encoder that encodes an evolutionary
conservation
sequence to each voxel in the three-dimensional grid of voxels. The
evolutionary conservation
sequence can be a three-dimensional representation of amino acid-specific
conservation
frequencies across a plurality of species. the amino acid-specific
conservation frequencies can
be selected in dependence upon amino acid proximity to the corresponding
voxel. The system
further comprises a tensor generator configured to generate a tensor that
includes the atom
category-wise distance channels encoded with the alternative allele amino acid
and respective
evolutionary conservation sequences.
Clause Set 1
1. A computer-implemented method, comprising:
accessing a three-dimensional structure of a reference amino acid sequence of
a protein, and
fitting a three-dimensional grid of voxels on atoms in the three-dimensional
structure on an
amino acid-basis to generate amino acid-wise distance channels,
wherein each of the amino acid-wise distance channels has a three-dimensional
distance
value for each voxel in the three-dimensional grid of voxels, and
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wherein the three-dimensional distance value specifies a distance from a
corresponding voxel
in the three-dimensional grid of voxels to atoms of a corresponding reference
amino acid
in the reference amino acid sequence;
encoding an alternative allele channel to each voxel in the three-dimensional
grid of voxels,
wherein the alternative allele channel is a three-dimensional representation
of a one-hot
encoding of a variant amino acid expressed by a variant nucleotide;
encoding an evolutionary conservation channel to each sequence of three-
dimensional distance
values across the amino acid-wise distance channels on a voxel position-basis,
wherein the evolutionary conservation channel is a three-dimensional
representation of
amino acid-specific conservation frequencies across a plurality of species,
and
wherein the amino acid-specific conservation frequencies are selected in
dependence upon
amino acid proximity to the corresponding voxel;
applying three-dimensional convolutions to a tensor that includes the amino
acid-wise distance
channels encoded with the alternative allele channel and respective
evolutionary conservation
channels; and
determining a pathogenicity of the variant nucleotide based at least in part
on the tensor.
2. The computer-implemented method of clause 1, further comprising
centering the three-
dimensional grid of voxels on an alpha carbon atom of respective residues of
reference amino
acids in the reference amino acid sequence.
3. The computer-implemented method of clause 2, further comprising
centering the three-
dimensional grid of voxels on an alpha carbon atom of a residue of a
particular reference amino
acid that corresponds to the variant amino acid.
4. The computer-implemented method of clause 3, further comprising
encoding, in the
tensor, a directionality of the reference amino acids in the reference amino
acid sequence and a
position of the particular reference amino acid by multiplying, with a
directionality parameter,
three-dimensional distance values for those reference amino acids that precede
the particular
reference amino acid.
5. The computer-implemented method of clause 4, wherein the distances are
nearest-atom
distances from corresponding voxel centers in the three-dimensional grid of
voxels to nearest
atoms of the corresponding reference amino acids
6. The computer-implemented method of clause 5, wherein the nearest-atom
distances are
Euclidean distances
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7. The computer-implemented method of clause 6, wherein the nearest-atom
distances are
normalized by dividing the Euclidean distances with a maximum nearest-atom
distance.
8. The computer-implemented method of clause 5, wherein the reference amino
acids have
alpha carbon atoms and wherein the distances are nearest-alpha carbon atom
distances from the
corresponding voxel centers to nearest alpha carbon atoms of the corresponding
reference amino
acids.
9. The computer-implemented method of clause 5, wherein the reference amino
acids have
beta carbon atoms and wherein the distances are nearest-beta carbon atom
distances from the
corresponding voxel centers to nearest beta carbon atoms of the corresponding
reference amino
acids.
10. The computer-implemented method of clause 5, wherein the reference
amino acids have
backbone atoms and wherein the distances are nearest-backbone atom distances
from the
corresponding voxel centers to nearest backbone atoms of the corresponding
reference amino
acids.
11. The computer-implemented method of clause 5, wherein the amino acids
have sidechain
atoms and wherein the distances are nearest-sidechain atom distances from the
corresponding
voxel centers to nearest sidechain atoms of the corresponding reference amino
acids.
12. The computer-implemented method of clause 3, further comprising
encoding, in the
tensor, a nearest atom channel that specifies a distance from each voxel to a
nearest atom,
wherein the nearest atom is selected irrespective of the amino acids and
atomic elements of the
amino acids.
13. The computer-implemented method of clause 12, wherein the distance is a
Euclidean
distance.
14. The computer-implemented method of clause 13, wherein the distance is
normalized by
dividing the Euclidean distance with a maximum distance.
15. The computer-implemented method of clause 12, wherein the amino acids
include non-
standard amino acids.
16. The computer-implemented method of clause 1, wherein the tensor further
includes an
absentee atom channel that specifies atoms not found within a predefined
radius of a voxel
center.
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17. The computer-implemented method of clause 16, wherein the absentee atom
channel is
one-hot encoded.
18. The computer-implemented method of clause 1, further comprising voxel-
wise encoding
a reference allele channel to each voxel in the three-dimensional grid of
voxels.
19. The computer-implemented method of clause 18, the reference allele
amino acid is a
three-dimensional representation of a one-hot encoding of a reference amino
acid that
experiences the variant amino acid.
20. The computer-implemented method of clause 1, wherein the amino acid-
specific
conservation frequencies specify conservation levels of respective amino acids
across the
plurality of species.
21. The computer-implemented method of clause 20, further comprising:
selecting a nearest atom to the corresponding voxel across the reference amino
acids and the
atom categories,
selecting pan-amino acid conservation frequencies for a residue of a reference
amino acid that
includes the nearest atom, and
using a three-dimensional representation of the pan-amino acid conservation
frequencies as the
evolutionary conservation channel.
22. The computer-implemented method of clause 21, wherein the pan-amino
acid
conservation frequencies are configured for a particular position of the
residue as observed in the
plurality of species.
23. The computer-implemented method of clause 21, wherein the pan-amino
acid
conservation frequencies specify whether there is a missing conservation
frequency for a
particular reference amino acid.
24. The computer-implemented method of clause 21, further comprising:
selecting respective nearest atoms to the corresponding voxel in respective
ones of the reference
amino acids,
selecting respective per-amino acid conservation frequencies for respective
residues of the
reference amino acids that include the nearest atoms, and
using a three-dimensional representation of the per-amino acid conservation
frequencies as the
evolutionary conservation channel.
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25. The computer-implemented method of clause 24, wherein the per-amino
acid
conservation frequencies are configured for a particular position of the
residues as observed in
the plurality of species.
26. The computer-implemented method of clause 24, wherein the per-amino
acid
conservation frequencies specify whether there is a missing conservation
frequency for a
particular reference amino acid.
27. The computer-implemented method of clause 1, further comprising voxel-
wise encoding
one or more annotation channels to each voxel in the three-dimensional grid of
voxels, wherein
the annotation channels are three-dimensional representations of a one-hot
encoding of residue
annotations.
28. The computer-implemented method of clause 27, wherein the annotation
channels are
molecular processing annotations that include initiator methionine, signal,
transit peptide,
propeptide, chain, and peptide.
29. The computer-implemented method of clause 27, wherein the annotation
channels are
regions annotations that include topological domain, transmembrane,
intramembrane, domain,
repeat, calcium binding, zinc finger, deoxyribonucleic acid (DNA) binding,
nucleotide binding,
region, coiled coil, motif, and compositional bias.
30. The computer-implemented method of clause 27, wherein the annotation
channels are
sites annotations that include active site, metal binding, binding site, and
site.
31. The computer-implemented method of clause 27, wherein the annotation
channels are
amino acid modifications annotations that include non-standard residue,
modified residue,
lipidation, glycosylation, disulfide bond, and cross-link.
32. The computer-implemented method of clause 27, wherein the annotation
channels are
secondary structure annotations that include helix, turn, and beta strand.
33. The computer-implemented method of clause 27, wherein the annotation
channels are
experimental information annotations that include mutagenesis, sequence
uncertainty, sequence
conflict, non-adjacent residues, and non-terminal residue.
34. The computer-implemented method of clause 1, further comprising voxel-
wise encoding
one or more structure confidence channels to each voxel in the three-
dimensional grid of voxels,
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wherein the structure confidence channels are three-dimensional
representations of confidence
scores that specify quality of respective residue structures.
35. The computer-implemented method of clause 34, wherein the structure
confidence
channels are global model quality estimations (GMQEs).
36. The computer-implemented method of clause 34, wherein the structure
confidence
channels are qualitative model energy analysis (Q1VIEAN) scores.
37. The computer-implemented method of clause 34, wherein the structure
confidence
channels are temperature factors that specify a degree to which the residues
satisfy physical
constraints of respective protein structures.
38. The computer-implemented method of clause 34, wherein the structure
confidence
channels are template structures alignments that specify a degree to which
residues of atoms
nearest to the voxels have aligned template structures.
39. The computer-implemented method of clause 38, wherein the structure
confidence
channels are template modeling scores of the aligned template structures.
40. The computer-implemented method of clause 39, wherein the structure
confidence
channels are a minimum one of the template modeling scores, a mean of the
template modeling
scores, and a maximum one of the template modeling scores.
41. The computer-implemented method of clause 1, further comprising
rotating the atoms
before the amino acid-wise distance channels are generated.
42. The computer-implemented method of clause 1, further comprising using 1
x 1 x 1
convolutions, 3 x 3 x 3 convolutions, rectified linear unit activation layers,
batch normalization
layers, a fully-connected layer, a dropout regularization layer, and a softmax
classification layer
in a convolutional neural network.
43. The computer-implemented method of clause 42, wherein the 1 x 1 x 1
convolutions and
the 3 x 3 x 3 convolutions are the three-dimensional convolutions.
44. The computer-implemented method of clause 42, wherein a layer of the 1
x 1 x 1
convolutions processes the tensor and produces an intermediate output that is
a convolved
representation of the tensor, wherein a sequence of layers of the 3 x 3 x 3
convolutions processes
the intermediate output and produces a flattened output, wherein the fully-
connected layer
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processes the flattened output and produces unnormalized outputs, and wherein
the softmax
classification layer processes the unnormalized outputs and produces
exponentially normalized
outputs that identify likelihoods of the variant nucleotide being pathogenic
and benign.
45. The computer-implemented method of clause 44, wherein a sigmoid layer
processes the
unnormalized outputs and produces a normalized output that identifies a
likelihood of the variant
nucleotide being pathogenic.
46. The computer-implemented method of clause 1, wherein the convolutional
neural
network is an attention-based neural network.
47. The computer-implemented method of clause 1, wherein the tensor
includes the amino
acid-wise distance channels further encoded with the reference allele channel.
48. The computer-implemented method of clause 1, wherein the tensor
includes the amino
acid-wise distance channels further encoded with the annotation channels.
49. The computer-implemented method of clause 1, wherein the tensor
includes the amino
acid-wise distance channels further encoded with the structure confidence
channels.
50. A computer-implemented method, comprising:
accessing a three-dimensional structure of a reference amino acid sequence of
a protein, and
fitting a three-dimensional grid of voxels on atoms in the three-dimensional
structure on an
amino acid-basis to generate atom category-wise distance channels,
wherein the atoms span a plurality of atom categories,
wherein atom categories in the plurality of atom categories specify atomic
elements of the amino
acids,
wherein each of the atom category-wise distance channels has a three-
dimensional distance
value for each voxel in the three-dimensional grid of voxels, and
wherein the three-dimensional distance value specifies a distance from a
corresponding voxel
in the three-dimensional grid of voxels to atoms of corresponding atom
categories in the
plurality of atom categories;
encoding an alternative allele channel to each voxel in the three-dimensional
grid of voxels,
wherein the alternative allele channel is a three-dimensional representation
of a one-hot
encoding of a variant amino acid expressed by a variant nucleotide;
encoding an evolutionary conservation channel to each sequence of three-
dimensional distance
values across the atom category-wise distance channels on a voxel position-
basis,
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wherein the evolutionary conservation channel is a three-dimensional
representation of
amino acid-specific conservation frequencies across a plurality of species,
and
wherein the amino acid-specific conservation frequencies are selected in
dependence upon
amino acid proximity to the corresponding voxel,
applying three-dimensional convolutions to a tensor that includes the atom
category-wise
distance channels encoded with the alternative allele channel and respective
evolutionary
conservation channels; and
determining a pathogenicity of the variant nucleotide based at least in part
on the tensor.
51. A computer-implemented method, comprising:
accessing a three-dimensional structure of a reference amino acid sequence of
a protein, and
fitting a three-dimensional grid of voxels on atoms in the three-dimensional
structure on an
amino acid-basis to generate amino acid-wise distance channels,
wherein each of the amino acid-wise distance channels has a three-dimensional
distance
value for each voxel in the three-dimensional grid of voxels, and
wherein the three-dimensional distance value specifies a distance from a
corresponding voxel
in the three-dimensional grid of voxels to atoms of a corresponding reference
amino acid
in the reference amino acid sequence,
encoding an alternative allele channel to each voxel in the three-dimensional
grid of voxels,
wherein the alternative allele channel is a three-dimensional representation
of a one-hot
encoding of a valiant amino acid expressed by a valiant nucleotide,
encoding an evolutionary conservation channel to each sequence of three-
dimensional distance
values across the amino acid-wise distance channels on a voxel position-basis,
wherein the evolutionary conservation channel is a three-dimensional
representation of
amino acid-specific conservation frequencies across a plurality of species,
and
wherein the amino acid-specific conservation frequencies are selected in
dependence upon
amino acid proximity to the corresponding voxel; and
generating a tensor that includes the amino acid-wise distance channels
encoded with the
alternative allele channel and respective evolutionary conservation channels
52. A computer-implemented method, comprising:
accessing a three-dimensional structure of a reference amino acid sequence of
a protein, and
fitting a three-dimensional grid of voxels on atoms in the three-dimensional
structure on an
amino acid-basis to generate atom category-wise distance channels,
wherein the atoms span a plurality of atom categories,
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wherein atom categories in the plurality of atom categories specify atomic
elements of the amino
acids,
wherein each of the atom category-wise distance channels has a three-
dimensional distance
value for each voxel in the three-dimensional grid of voxels, and
wherein the three-dimensional distance value specifies a distance from a
corresponding voxel
in the three-dimensional grid of voxels to atoms of corresponding atom
categories in the
plurality of atom categories;
encoding an alternative allele channel to each voxel in the three-dimensional
grid of voxels,
wherein the alternative allele channel is a three-dimensional representation
of a one-hot
encoding of a variant amino acid expressed by a variant nucleotide;
encoding an evolutionary conservation channel to each sequence of three-
dimensional distance
values across the atom category-wise distance channels on a voxel position-
basis,
wherein the evolutionary conservation channel is a three-dimensional
representation of
amino acid-specific conservation frequencies across a plurality of species,
and
wherein the amino acid-specific conservation frequencies are selected in
dependence upon
amino acid proximity to the corresponding voxel; and
generating a tensor that includes the atom category-wise distance channels
encoded with the
alternative allele channel and respective evolutionary conservation channels.
Clause Set 2
1. One or more computer-readable media storing computer-executable
instructions that,
when executed on one or more processors, configure a computer to perform
operations
comprising:
accessing a three-dimensional structure of a reference amino acid sequence of
a protein, and
fitting a three-dimensional grid of voxels on atoms in the three-dimensional
structure on an
amino acid-basis to generate amino acid-wise distance channels,
wherein each of the amino acid-wise distance channels has a three-dimensional
distance
value for each voxel in the three-dimensional grid of voxels, and
wherein the three-dimensional distance value specifies a distance from a
corresponding voxel
in the three-dimensional grid of voxels to atoms of a corresponding reference
amino acid
in the reference amino acid sequence;
encoding an alternative allele channel to each voxel in the three-dimensional
grid of voxels,
wherein the alternative allele channel is a three-dimensional representation
of a one-hot
encoding of a variant amino acid expressed by a variant nucleotide;
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encoding an evolutionary conservation channel to each sequence of three-
dimensional distance
values across the amino acid-wise distance channels on a voxel position-basis,
wherein the evolutionary conservation channel is a three-dimensional
representation of
amino acid-specific conservation frequencies across a plurality of species,
and
wherein the amino acid-specific conservation frequencies are selected in
dependence upon
amino acid proximity to the corresponding voxel;
applying three-dimensional convolutions to a tensor that includes the amino
acid-wise distance
channels encoded with the alternative allele channel and respective
evolutionary conservation
channels; and
determining a pathogenicity of the variant nucleotide based at least in part
on the tensor.
2. The computer-readable media of clause 1, the operations further
comprising centering the
three-dimensional grid of voxels on an alpha carbon atom of respective
residues of reference
amino acids in the reference amino acid sequence.
3. The computer-readable media of clause 2, the operations further
comprising centering the
three-dimensional grid of voxels on an alpha carbon atom of a residue of a
particular reference
amino acid that corresponds to the variant amino acid.
4. The computer-readable media of clause 3, the operations further
comprising encoding, in
the tensor, a directionality of the reference amino acids in the reference
amino acid sequence and
a position of the particular reference amino acid by multiplying, with a
directionality parameter,
three-dimensional distance values for those reference amino acids that precede
the particular
reference amino acid.
5. The computer-readable media of clause 4, wherein the distances are
nearest-atom
distances from corresponding voxel centers in the three-dimensional grid of
voxels to nearest
atoms of the corresponding reference amino acids.
6. The computer-readable media of clause 5, wherein the nearest-atom
distances are
Euclidean distances.
7. The computer-readable media of clause 6, wherein the nearest-atom
distances are
normalized by dividing the Euclidean distances with a maximum nearest-atom
distance.
8. The computer-readable media of clause 5, wherein the reference amino
acids have alpha
carbon atoms and wherein the distances are nearest-alpha carbon atom distances
from the
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corresponding voxel centers to nearest alpha carbon atoms of the corresponding
reference amino
acids.
9. The computer-readable media of clause 5, wherein the reference amino
acids have beta
carbon atoms and wherein the distances are nearest-beta carbon atom distances
from the
corresponding voxel centers to nearest beta carbon atoms of the corresponding
reference amino
acids.
10. The computer-readable media of clause 5, wherein the reference amino
acids have
backbone atoms and wherein the distances are nearest-backbone atom distances
from the
corresponding voxel centers to nearest backbone atoms of the corresponding
reference amino
acids.
11. The computer-readable media of clause 5, wherein the amino acids have
sidechain atoms
and wherein the distances are nearest-sidechain atom distances from the
corresponding voxel
centers to nearest sidechain atoms of the corresponding reference amino acids.
12. The computer-readable media of clause 3, the operations further
comprising encoding, in
the tensor, a nearest atom channel that specifies a distance from each voxel
to a nearest atom,
wherein the nearest atom is selected irrespective of the amino acids and
atomic elements of the
amino acids.
13. The computer-readable media of clause 12, wherein the distance is a
Euclidean distance.
14. The computer-readable media of clause 13, wherein the distance is
normalized by
dividing the Euclidean distance with a maximum distance.
15. The computer-readable media of clause 12, wherein the amino acids
include non-standard
amino acids.
16. The computer-readable media of clause 1, wherein the tensor further
includes an absentee
atom channel that specifies atoms not found within a predefined radius of a
voxel center.
17. The computer-readable media of clause 16, wherein the absentee atom
channel is one-hot
encoded.
18. The computer-readable media of clause 1, the operations further
comprising voxel-wise
encoding a reference allele channel to each voxel in the three-dimensional
grid of voxels.
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19. The computer-readable media of clause 18, the reference allele amino
acid is a three-
dimensional representation of a one-hot encoding of a reference amino acid
that experiences the
variant amino acid.
20. The computer-readable media of clause 1, wherein the amino acid-
specific conservation
frequencies specify conservation levels of respective amino acids across the
plurality of species.
21. The computer-readable media of clause 20, the operations further
comprising:
selecting a nearest atom to the corresponding voxel across the reference amino
acids and the
atom categories,
selecting pan-amino acid conservation frequencies for a residue of a reference
amino acid that
includes the nearest atom, and
using a three-dimensional representation of the pan-amino acid conservation
frequencies as the
evolutionary conservation channel.
22. The computer-readable media of clause 21, wherein the pan-amino acid
conservation
frequencies are configured for a particular position of the residue as
observed in the plurality of
species.
23. The computer-readable media of clause 21, wherein the pan-amino acid
conservation
frequencies specify whether there is a missing conservation frequency for a
particular reference
amino acid.
24. The computer-readable media of clause 21, the operations further
comprising:
selecting respective nearest atoms to the corresponding voxel in respective
ones of the reference
amino acids,
selecting respective per-amino acid conservation frequencies for respective
residues of the
reference amino acids that include the nearest atoms, and
using a three-dimensional representation of the per-amino acid conservation
frequencies as the
evolutionary conservation channel.
25. The computer-readable media of clause 24, wherein the per-amino acid
conservation
frequencies are configured for a particular position of the residues as
observed in the plurality of
species.
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26. The computer-readable media of clause 24, wherein the per-amino acid
conservation
frequencies specify whether there is a missing conservation frequency for a
particular reference
amino acid.
27. The computer-readable media of clause 1, the operations further
comprising voxel-wise
encoding one or more annotation channels to each voxel in the three-
dimensional grid of voxels,
wherein the annotation channels are three-dimensional representations of a one-
hot encoding of
residue annotations.
28. The computer-readable media of clause 27, wherein the annotation
channels are
molecular processing annotations that include initiator methionine, signal,
transit peptide,
propeptide, chain, and peptide.
29. The computer-readable media of clause 27, wherein the annotation
channels are regions
annotations that include topological domain, transmembrane, intramembrane,
domain, repeat,
calcium binding, zinc finger, deoxyribonucleic acid (DNA) binding, nucleotide
binding, region,
coiled coil, motif, and compositional bias.
30. The computer-readable media of clause 27, wherein the annotation
channels are sites
annotations that include active site, metal binding, binding site, and site.
31. The computer-readable media of clause 27, wherein the annotation
channels are amino
acid modifications annotations that include non-standard residue, modified
residue, lipidation,
glycosylation, disulfide bond, and cross-link.
32. The computer-readable media of clause 27, wherein the annotation
channels are
secondary structure annotations that include helix, turn, and beta strand.
33. The computer-readable media of clause 27, wherein the annotation
channels are
experimental information annotations that include mutagenesis, sequence
uncertainty, sequence
conflict, non-adjacent residues, and non-terminal residue.
34. The computer-readable media of clause 1, the operations further
comprising voxel-wise
encoding one or more structure confidence channels to each voxel in the three-
dimensional grid
of voxels, wherein the structure confidence channels are three-dimensional
representations of
confidence scores that specify quality of respective residue structures.
35. The computer-readable media of clause 34, wherein the structure
confidence channels are
global model quality estimations (GMQEs).
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36. The computer-readable media of clause 34, wherein the structure
confidence channels are
qualitative model energy analysis (QMEAN) scores.
37. The computer-readable media of clause 34, wherein the structure
confidence channels are
temperature factors that specify a degree to which the residues satisfy
physical constraints of
respective protein structures.
38. The computer-readable media of clause 34, wherein the structure
confidence channels are
template structures alignments that specify a degree to which residues of
atoms nearest to the
voxels have aligned template structures.
39. The computer-readable media of clause 38, wherein the structure
confidence channels are
template modeling scores of the aligned template structures.
40. The computer-readable media of clause 39, wherein the structure
confidence channels are
a minimum one of the template modeling scores, a mean of the template modeling
scores, and a
maximum one of the template modeling scores.
41. The computer-readable media of clause 1, the operations further
comprising rotating the
atoms before the amino acid-wise distance channels are generated.
42. The computer-readable media of clause 1, the operations further
comprising using 1 x 1 x
1 convolutions, 3 x 3 x 3 convolutions, rectified linear unit activation
layers, batch normalization
layers, a fully-connected layer, a dropout regularization layer, and a softmax
classification layer
in a convolutional neural network.
43. The computer-readable media of clause 42, wherein the 1 x 1 x 1
convolutions and the 3
x 3 x 3 convolutions are the three-dimensional convolutions.
44. The computer-readable media of clause 42, wherein a layer of the 1 x 1
x 1 convolutions
processes the tensor and produces an intermediate output that is a convolved
representation of
the tensor, wherein a sequence of layers of the 3 x 3 x 3 convolutions
processes the intermediate
output and produces a flattened output, wherein the fully-connected layer
processes the flattened
output and produces unnormalized outputs, and wherein the softmax
classification layer
processes the unnormalized outputs and produces exponentially normalized
outputs that identify
likelihoods of the variant nucleotide being pathogenic and benign.
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45. The computer-readable media of clause 44, wherein a sigmoid layer
processes the
unnormalized outputs and produces a normalized output that identifies a
likelihood of the variant
nucleotide being pathogenic.
46. The computer-readable media of clause 1, wherein the convolutional
neural network is an
attention-based neural network.
47. The computer-readable media of clause 1, wherein the tensor includes
the amino acid-
wise distance channels further encoded with the reference allele channel.
48. The computer-readable media of clause 1, wherein the tensor includes
the amino acid-
wise distance channels further encoded with the annotation channels.
49. The computer-readable media of clause 1, wherein the tensor includes
the amino acid-
wise distance channels further encoded with the structure confidence channels.
50. One or more computer-readable media storing computer-executable
instructions that,
when executed on one or more processors, configure a computer to perform
operations
comprising:
accessing a three-dimensional structure of a reference amino acid sequence of
a protein, and
fitting a three-dimensional grid of voxels on atoms in the three-dimensional
structure on an
amino acid-basis to generate atom category-wise distance channels,
wherein the atoms span a plurality of atom categories,
wherein atom categories in the plurality of atom categories specify atomic
elements of the amino
acids,
wherein each of the atom category-wise distance channels has a three-
dimensional distance
value for each voxel in the three-dimensional grid of voxels, and
wherein the three-dimensional distance value specifies a distance from a
corresponding voxel
in the three-dimensional grid of voxels to atoms of corresponding atom
categories in the
plurality of atom categories;
encoding an alternative allele channel to each voxel in the three-dimensional
grid of voxels,
wherein the alternative allele channel is a three-dimensional representation
of a one-hot
encoding of a variant amino acid expressed by a variant nucleotide;
encoding an evolutionary conservation channel to each sequence of three-
dimensional distance
values across the atom category-wise distance channels on a voxel position-
basis,
wherein the evolutionary conservation channel is a three-dimensional
representation of
amino acid-specific conservation frequencies across a plurality of species,
and
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wherein the amino acid-specific conservation frequencies are selected in
dependence upon
amino acid proximity to the corresponding voxel;
applying three-dimensional convolutions to a tensor that includes the atom
category-wise
distance channels encoded with the alternative allele channel and respective
evolutionary
conservation channels; and
determining a pathogenicity of the variant nucleotide based at least in part
on the tensor.
L One or more computer-readable media storing computer-executable
instructions that,
when executed on one or more processors, configure a computer to perform
operations
comprising:
accessing a three-dimensional structure of a reference amino acid sequence of
a protein, and
fitting a three-dimensional grid of voxels on atoms in the three-dimensional
structure on an
amino acid-basis to generate amino acid-wise distance channels,
wherein each of the amino acid-wise distance channels has a three-dimensional
distance
value for each voxel in the three-dimensional grid of voxels, and
wherein the three-dimensional distance value specifies a distance from a
corresponding voxel
in the three-dimensional grid of voxels to atoms of a corresponding reference
amino acid
in the reference amino acid sequence,
encoding an alternative allele channel to each three-dimensional distance
value in each of the
amino acid-wise distance channels on an amino acid position-basis,
wherein the alternative allele channel is a three-dimensional representation
of a one-hot
encoding of a variant amino acid expressed by a variant nucleotide,
encoding an evolutionary conservation channel to each sequence of three-
dimensional distance
values across the amino acid-wise distance channels on a voxel position-basis,
wherein the evolutionary conservation channel is a three-dimensional
representation of
amino acid-specific conservation frequencies across a plurality of species,
and
wherein the amino acid-specific conservation frequencies are selected in
dependence upon
amino acid proximity to the corresponding voxel; and
generating a tensor that includes the amino acid-wise distance channels
encoded with the
alternative allele channel and respective evolutionary conservation channels.
52 One or more computer-readable media storing computer-executable
instructions that,
when executed on one or more processors, configure a computer to perform
operations
comprising:
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accessing a three-dimensional structure of a reference amino acid sequence of
a protein, and
fitting a three-dimensional grid of voxels on atoms in the three-dimensional
structure on an
amino acid-basis to generate atom category-wise distance channels,
wherein the atoms span a plurality of atom categories,
wherein atom categories in the plurality of atom categories specify atomic
elements of the amino
acids,
wherein each of the atom category-wise distance channels has a three-
dimensional distance
value for each voxel in the three-dimensional grid of voxels, and
wherein the three-dimensional distance value specifies a distance from a
corresponding voxel
in the three-dimensional grid of voxels to atoms of corresponding atom
categories in the
plurality of atom categories;
encoding an alternative allele channel to each voxel in the three-dimensional
grid of voxels,
wherein the alternative allele channel is a three-dimensional representation
of a one-hot
encoding of a variant amino acid expressed by a variant nucleotide;
encoding an evolutionary conservation channel to each sequence of three-
dimensional distance
values across the atom category-wise distance channels on a voxel position-
basis,
wherein the evolutionary conservation channel is a three-dimensional
representation of
amino acid-specific conservation frequencies across a plurality of species,
and
wherein the amino acid-specific conservation frequencies are selected in
dependence upon
amino acid proximity to the corresponding voxel; and
generating a tensor that includes the atom category-wise distance channels
encoded with the
alternative allele channel and respective evolutionary conservation channels.
102561 Other implementations of the method described in this
section can include a non-
transitory computer readable storage medium storing instructions executable by
a processor to
perform any of the methods described above. Yet another implementation of the
method
described in this section can include a system including memory and one or
more processors
operable to execute instructions, stored in the memory, to perform any of the
methods described
above.
Particular Implementations 3
Clause Set 1
1. A computer-implemented method of efficiently determining which
elements of a
sequence are nearest to uniformly spaced cells in a grid, wherein the elements
have element
coordinates, and the cells have dimension-wise cell indices and cell
coordinates, including:
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generating an element-to-cells mapping that maps, to each of the elements, a
subset of the cells,
wherein the subset of the cells mapped to a particular element in the sequence
includes a
nearest cell in the grid and one or more neighborhood cells in the grid,
wherein the nearest cell is selected based on matching element coordinates of
the
particular element to the cell coordinates, and
wherein the neighborhood cells are contiguously adjacent to the nearest cell
and selected
based on being within a distance proximity range from the particular element;
generating a cell-to-elements mapping that maps, to each of the cells, a
subset of the elements,
wherein the subset of the elements mapped to a particular cell in the grid
includes those
elements in the sequence that are mapped to the particular cell by the element-
to-cells
mapping; and
using the cell-to-elements mapping to determine, for each of the cells, a
nearest element in the
sequence,
wherein the nearest element to the particular cell is determined based on
distances between
the particular cell and the elements in the subset of the elements.
2. The computer-implemented method of clause 1, wherein the matching the
element
coordinates of the particular element to the cell coordinates further includes
truncating a decimal
portion of the element coordinates to generate truncated element coordinates.
3. The computer-implemented method of clause 2, wherein the matching the
element
coordinates of the particular element to the cell coordinates further
includes:
for a first dimension, matching a first truncated element coordinate in the
truncated element
coordinates to a first cell coordinate of a first cell in the grid, and
selecting a first dimension
index of the first cell,
for a second dimension, matching a second truncated element coordinate in the
truncated element
coordinates to a second cell coordinate of a second cell in the grid, and
selecting a second
dimension index of the second cell,
for a third dimension, matching a third truncated element coordinate in the
truncated element
coordinates to a third cell coordinate of a third cell in the grid, and
selecting a third
dimension index of the third cell;
using the selected first, second, and third dimension indices to generate an
accumulated sum
based on position-wise weighting the selected first, second, and third
dimension indices by
powers of a radix; and
using the accumulated sum as a cell index for selection of the nearest cell.
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4. The computer-implemented method of clause 1, wherein the distances are
calculated
between cell coordinates of the particular cell and element coordinates of the
elements in the
subset of the elements.
5. The computer-implemented method of clause 1, wherein the sequence is a
protein
sequence of amino acids.
6. The computer-implemented method of clause 5, wherein the elements are
atoms of the
amino acids.
7. The computer-implemented method of clause 6, wherein the steps of
generating the
element-to-cells mapping, generating the cell-to-elements mapping, and using
the cell-to-
elements mapping to determine, for each of the cells, the nearest element have
a runtime
complexity of 0(a * f + v), wherein
a is a number of the atoms,
f is a number of the amino acids,
v is a number of the cells, and
* is a multiplication operation.
8. The computer-implemented method of clause 7, wherein the atoms include
alpha carbon
atoms.
9. The computer-implemented method of clause 7, wherein the atoms include
beta carbon
atoms.
10. The computer-implemented method of clause 7, wherein the atoms include
non-carbon
atoms.
1 1 . The computer-implemented method of clause 1, wherein the cells
are three-dimensional
voxels.
12. The computer-implemented method of clause 11, wherein the cell
coordinates are three-
dimensional coordinates.
13. The computer-implemented method of clause 12, wherein the element
coordinates are
three-dimensional coordinates.
14. The computer-implemented method of clause 1, wherein the neighborhood
cells are
selected based on being within an index adjacency range from the nearest cell.
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15. The computer-implemented method of clause 1, wherein the neighborhood
cells are
selected based on being within a cell neighborhood in the grid that includes
the nearest cell.
16. The computer-implemented method of clause 1, wherein the sequence
includes M
elements, wherein the subset of the elements includes N elements, and wherein
M > > N.
17. A computer-implemented method of efficiently determining which atoms in
a protein are
nearest to voxels in a grid, wherein the atoms have three-dimensional (3D)
atom coordinates, and
the voxels have 3D voxel coordinates, including:
generating an atom-to-voxels mapping that maps, to each of the atoms, a
containing voxel
selected based on matching 3D atom coordinates of a particular atom of the
protein to the 3D
voxel coordinates in the grid;
generating a voxel-to-atoms mapping that maps, to each of the voxels, a subset
of the atoms,
wherein the subset of the atoms mapped to a particular voxel in the grid
includes those atoms
in the protein that are mapped to the particular voxel by the atom-to-voxels
mapping; and
using the voxel-to-atoms mapping to determine, for each of the voxels, a
nearest atom in the
protein.
1 S. The computer-implemented method of clause 17, wherein the steps
of clause 17 have a
runtime complexity of 0(number of atoms).
[0257] Other implementations of the method described in this
section can include a non-
transitory computer readable storage medium storing instructions executable by
a processor to
perform any of the methods described above. Yet another implementation of the
method
described in this section can include a system including memory and one or
more processors
operable to execute instructions, stored in the memory, to perform any of the
methods described
above.
[0258] While the present invention is disclosed by reference to the
preferred
implementations and examples detailed above, it is to be understood that these
examples are
intended in an illustrative rather than in a limiting sense. It is
contemplated that modifications
and combinations will readily occur to those skilled in the art, which
modifications and
combinations will be within the spirit of the invention and the scope of the
following claims.
[0259] What is claimed is:
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