Note: Descriptions are shown in the official language in which they were submitted.
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PROTEIN DESIGN AUTOMATION FOR PROTEIN LIBRARIES
FIELD OF THE INVENTION
The invention relates to the use of protein design automation (PDA) to
generate computationally
prescreened secondary libraries of proteins, and to methods and compositions
utilizing the libraries.
BACKGROUND OF THE INVENTION
Directed molecular evolution can be used to create proteins and enzymes with
novel functions and
properties. Starting with a known natural protein, several rounds of
mutagenesis, functional screening,
and propagation of successful sequences are performed. The advantage of this
process is that it can
be used to rapidly evolve any protein without knowledge of its structure.
Several different
mutagenesis strategies exist, including point mutagenesis by error-prone PCR,
cassette mutagenesis,
and DNA shuffling. These techniques have had many successes; however, they are
all handicapped
by their inability to produce more than a tiny fraction of the potential
changes. For example, there are
205°° possible amino acid changes for an average protein
approximately 500 amino acids long.
Clearly, the mutagenesis and functional screening of so many mutants is
impossible; directed
evolution provides a very sparse sampling of the possible sequences and hence
examines only a
small portion of possible improved proteins, typically point mutants or
recombinations of existing
sequences. By sampling randomly from the vast number of possible sequences,
directed evolution is
unbiased and broadly applicable, but inherently inefficient because it ignores
all structural and
biophysical knowledge of proteins.
2 0 In contrast, computational methods can be used to screen enormous sequence
libraries (up to 108° in
a single calculation) overcoming the key limitation of experimental library
screening methods such as
directed molecular evolution. There are a wide variety of methods known for
generating and
evaluating sequences. These include, but are not limited to, sequence
profiling (Bowie and Eisenberg,
Science 253(5016): 164-70, (1991)), rotamer library selections (Dahiyat and
Mayo, Protein Sci 5(5):
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895-903 (1996); Dahiyat and Mayo, Science 278(5335): 82-7 (1997); Desjarlais
and Handel, Protein
Science 4: 2006-2018 (1995); Harbury et al, PNAS USA 92(18): 8408-8412 (1995);
Kono et al.,
Proteins: Structure, Function and Genetics 19: 244-255 (1994); Hellinga and
Richards, PNAS USA 91:
5803-5807 (1994)); and residue pair potentials (Jones, Protein Science 3: 567-
574, (1994)).
In particular, U.S.S.N.s 60/061,097, 601043,464, 60/054,678, 09/127,926 and
PCT US98/07254
describe a method termed "Protein Design Automation", or PDA, that utilizes a
number of scoring
functions to evaluate sequence stability.
It is an object of the present invention to provide computational methods for
prescreening sequence
libraries to generate and select secondary libraries, which can then be made
and evaluated
experimentally.
SUMMARY OF THE INVENTION
In accordance with the objects outlined above, the present invention provides
methods for generating
a secondary library of scaffold protein variants comprising providing a
primary library comprising a
rank-ordered list of scaffold protein primary variant sequences. A list of
primary variant positions in the
primary library is then generated, and a plurality of the primary variant
positions is then combined to
generate a secondary library of secondary sequences.
In an additional aspect, the invention provides methods for generating a
secondary library of scaffold
protein variants comprising providing a primary library comprising a rank-
ordered list of scaffold protein
primary variant sequences, and generating a probability distribution of amino
acid residues in a
2 0 plurality of variant positions. The plurality of the amino acid residues
is combined to generate a
secondary library of secondary sequences. These sequences may then be
optionally synthesized and
tested, in a variety of ways, including multiplexing PCR with pooled
oligonucleotides, error prone PCR,
gene shuffling, etc.
In a further aspect, the invention provides compositions comprising a
plurality of secondary variant
2 5 proteins or nucleic acids encoding the proteins, wherein the plurality
comprises all or a subset of the
secondary library. The invention further provides cells comprising the
library, particularly mammalian
cells.
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In an additional aspect, the invention provides methods for generating a
secondary library of scaffold
protein variants comprising providing a first library rank-ordered list of
scaffold protein primary
variants;
generating a probability distribution of amino acid residues in a plurality of
variant positions; and
synthesizing a plurality of scaffold protein secondary variants comprising a
plurality of the amino acid
residues to form a secondary library. At least one of the secondary variants
is different from the
primary variants.
In an additional embodiment, the present invention provides methods executed
by a computer under
the control of a program, the computer including a memory for storing the
program. The method
comprising the steps of receiving a protein backbone structure with variable
residue positions,
establishing a group of potential rotamers for each of the variable residue
positions, and analyzing the
interaction of each of the rotamers with all or part of the remainder of the
protein backbone structure to
generate a set of optimized protein sequences. The methods further comprise
classifying each
variable residue position as either a core, surface or boundary residue. The
analyzing step may
include a Dead-End Elimination (DEE) computation. Generally, the analyzing
step includes the use of
at least one scoring function selected from the group consisting of a Van der
Waals potential scoring
function, a hydrogen bond potential scoring function, an atomic solvation
scoring function, a secondary
structure propensity scoring function and an electrostatic scoring function.
The methods further
comprise altering the protein backbone prior to the analysis, comprising
altering at least one
2 0 supersecondary structure parameter value. The methods may further comprise
generating a rank
ordered list of additional optimal sequences from the globally optimal protein
sequence. Some or all
of the protein sequences from the ordered list may be tested to produce
potential energy test results.
The methods may further comprise generating a secondary library and/or ranking
a secondary library,
using the techniques outlined herein. Thus devices comprising the computer
code for running the
2 5 programs are provided as well.
BRIEF DESCRIPTION OF THE DRAWINGS
Figure 1 depicts the synthesis of a full-length gene and all possible
mutations by PCR. Overlapping
oligonucleotides corresponding to the full-length gene (black bar, Step 1 )
are synthesized, heated and
annealed. Addition of Pfu DNA polymerase to the annealed oligonucleotides
results in the 5' -3'
3 0 synthesis of DNA (Step 2) to produce longer DNA fragments (Step 3).
Repeated cycles of heating,
annealing (Step 4) results in the production of longer DNA, including some
full-length molecules.
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These can be selected by a second round of PCR using primers (arrowed)
corresponding to the end
of the full-length gene (Step 5).
Figure 2 depicts the reduction of the dimensionality of sequence space by PDA
screening. From left
to right, 1: without PDA; 2: without PDA not counting Cysteine, Proline,
Glycine; 3: with PDA using the
1% criterion, modeling free enzyme; 4: with PDA using the 1% criterion,
modeling enzyme-substrate
complex; 5: with PDA using the 5% criterion modeling free enzyme; 6: with PDA
using the 5%
criterion modeling enzyme-substrate complex.
Figure 3 depicts the active site of 8. circulans xylanase. Those positions
included in the PDA design
are shown by their side chain representation. In red are wild type residues
(their conformation was
allowed to change, but not their amino acid identity). In green are positions
whose conformation and
identity were allowed to change (to any amino acid except proline, cysteine
and glycine).
Figure 4 depicts cefotaxime resistance of E. coli expressing wild type (WT)
and PDA Screened
(3-lactamase; results shown for increasing concentrations of cefotaxime.
DETAILED DESCRIPTION OF THE INVENTION
The present invention is directed to methods of using computational screening
of protein sequence
libraries (that can comprise up to 10°° or more members) to
select smaller secondary libraries of
protein sequences (that can comprise up to 10'3 members), that can then be
actually synthesized and
experimentally tested in the desired assay, for improved function and
properties.
The invention has two broad uses; first, the invention can be used to
prescreen libraries based on
2 0 known scaffold proteins. That is, computational screening for stability
(or other properties) may be
done on either the entire protein or some subset of residues, as desired and
described below. By
using computational methods to generate a threshold or cutoff to eliminate
disfavored sequences, the
percentage of useful variants in a given variant set size can increase, and
the required experimental
outlay is decreased.
2 5 In addition, the present invention finds use in the screening of random
peptide libraries. As is known,
signaling pathways in cells often begin with an effector stimulus that leads
to a phenotypically
describable change in cellular physiology. Despite the key role intracellular
signaling pathways play in
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disease pathogenesis, in most cases, little is understood about a signaling
pathway other than the
initial stimulus and the ultimate cellular response.
Historically, signal transduction has been analyzed by biochemistry or
genetics. The biochemical
approach dissects a pathway in a "stepping-stone" fashion: find a molecule
that acts at, or is involved
in, one end of the pathway, isolate assayable quantities and then try to
determine the next molecule in
the pathway, either upstream or downstream of the isolated one. The genetic
approach is classically a
"shot in the dark": induce or derive mutants in a signaling pathway and map
the locus by genetic
crosses or complement the mutation with a cDNA library. Limitations of
biochemical approaches
include a reliance on a significant amount of pre-existing knowledge about the
constituents under
study and the need to carry such studies out in vitro, post-mortem.
Limitations of purely genetic
approaches include the need to first derive and then characterize the pathway
before proceeding with
identifying and cloning the gene.
Screening molecular libraries of chemical compounds for drugs that regulate
signal systems has led to
important discoveries of great clinical significance. Cyclosporin A (CsA) and
FK506, for examples,
were selected in standard pharmaceutical screens for inhibition of T-cell
activation. It is noteworthy
that while these two drugs bind completely different cellular proteins --
cyclophilin and FK50fi binding
protein (FKBP), respectively, the effect of either drug is virtually the same -
- profound and specific
suppression of T-cell activation, phenotypically observable in T cells as
inhibition of mRNA production
dependent on transcription factors such as NF-AT and NF-KB. Libraries of small
peptides have also
been successfully screened in vitro in assays for bioactivity. The literature
is replete with examples of
small peptides capable of modulating a wide variety of signaling pathways. For
example, a peptide
derived from the HIV-1 envelope protein has been shown to block the action of
cellular calmodulin.
Accordingly, generation of random or semi-random sequence libraries of
proteins and peptides allows
for the selection of proteins (including peptides, oligopeptides and
polypeptides) with useful properties.
2 5 The sequences in these experimental libraries can be randomized at
specific sites only, or throughout
the sequence. The number of sequences that can be searched in these libraries
grows expontentially
with the number of positions that are randomized. Generally, only up to 10'z -
10'5 sequences can be
contained in a library because of the physical constraints of laboratories
(the size of the instruments,
the cost of producing large numbers of biopolymers, etc.). Other practical
considerations can often
3 0 limit the size of the libraries to 1 O6 or fewer. These limits are reached
for only 10 amino acid positions.
Therefore, only a sparse sampling of sequences is possible in the search for
improved proteins or
peptides in experimental sequence libraries, lowering the chance of success
and almost certainly
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missing desirable candidates. Because of the randomness of the changes in
these sequences, most
of the candidates in the library are not suitable, resulting in a waste of
most of the effort in producing
the library.
However, using the automated protein design techniques outlined below, virtual
libraries of protein
sequences can be generated that are vastly larger than experimental libraries.
Up to 108° candidate
I sequences can be screened computationally and those that meet design
criteria which favor stable
and functional proteins can be readily selected. An experimental library
consisting of the favorable
candidates found in the virtual library screening can then be generated,
resulting in a much more
efficient use of the experimental library and overcoming the Limitations of
random protein libraries.
Two principle benefits come from the virtual library screening: (1) the
automated protein design
generates a list of sequence candidates that are favored to meek design
criteria; it also shows which
positions in the sequence are readily changed and which positions are unlikely
to change without
disrupting protein stability and function. An experimental random library can
be generated that is only
randomized at the readily changeable, non-disruptive sequence positions. (2)
The diversity of amino
acids at these positions can be limited to those that the automated design
shows are compatible with
these positions. Thus, by limiting the number of randomized positions and the
number of possibilities
at these positions, the number of wasted sequences produced in the
experimental library is reduced,
thereby increasing the probability of success in finding sequences with useful
properties.
In addition, by computationally screening very large libraries of mutants,
greater diversity of protein
sequences can be screened, leading to greater improvements in protein
function. Further, fewer
mutants need to be tested experimentally to screen a given library size,
reducing the cost and difficulty
of protein engineering. By using computational methods to pre-screen a protein
library, the
computational features of speed and efficiency are combined with the ability
of experimental library
screening to create new activities in proteins for which appropriate
computational models and
2 5 structure-function relationships are unclear.
Similarly, novel methods to create secondary libraries derived from very large
computational mutant
Libraries allow the rapid testing of large numbers of computationally designed
sequences.
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In addition, as is more fully outlined below, the libraries may be biased in
any number of ways,
allowing the generation of secondary libraries that vary in their. focus; for
example, domains, subsets of
residues, active or binding sites, surface residues, etc., may all be varied
or kept constant as desired.
Accordingly, the present invention provides methods for generating secondary
Jibraries of scaffold
protein variants. By "protein" herein is meant at least two amino acids linked
together by a peptide
bond. As used herein, protein includes proteins, oligopeptides and peptides.
The peptidyl group may
comprise naturally occurring amino acids and peptide bonds, or synthetic
peptidomimetic structures,
i.e. "analogs", such as peptoids (see Simon et al., PNAS USA 89(20):9367
(192)).. The amino acids
may either be naturally occuring or non-naturally occuring; as will be
appreciated by those in the art,
any structure for which a set of rotamers is known or can be generated can be
used as an amino acid.
The side chains may be in either the (R) or the (S) configuration. In a
preferred embodiment, the
amino acids are in the (S) or L-configuration.
The scaffold protein may be any protein for which a three dimensional
structure is known or can be
generated; that is, for which there are three dimensional coordinates for each
atom of the protein.
Generally this can be determined using X-ray crystallographic techniques, NMR
techniques, de novo
modelling, homology modelling, etc. In general, if X-ray structures are used,
structures at 2A
resolution or better are preferred, but not required.
The scaffold proteins may be from any organism, including prokaryotes and
eukaryotes, with enzymes
from bacteria, fungi, extremeophiles such as the archebacteria, insects, fish,
animals (particularly
mammals and particularly human) and birds all possible.
Thus, by "scaffold protein" herein is meant a protein for which a secondary
library of variants is
desired. As will be appreciated by those in the art, any number of scaffold
proteins find use in the
present invention. Specifically included within the definition of "protein"
are fragments and domains of
known proteins, including functional domains such as enzymatic domains,
binding domains, etc., and
smaller fragments, such as turns, loops, etc. That is, portions of proteins
may be used as well. In
addition, "protein" as used herein includes proteins, oligopeptides and
peptides. In addition, protein
variants, i.e. non-naturally occuring protein analog structures, may be used.
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Suitable proteins include, but are not limited to, industrial and
pharmaceutical proteins, including
ligands, cell surface receptors, antigens, antibodies, cytokines,,hormones,
transcription factors,
signalling modules, cytoskeletal proteins and enzymes. Suitable classes of
enzymes include, but are
not limited to, hydrolases such as proteases, carbohydrases, lipases;
isomerases such as racemases,
epimerases, tautomerases, or mutases; transferases, kinases, oxidoreductases,
and phophatases.
Suitable enzymes are listed in the Swiss-Prot enzyme database. Suitable
protein backbones include,
but are not limited to, all of those found in the protein data base compiled
and serviced by the
Research Collaboratory for Structural Bioinformatics (RCSB, formerly the
Brookhaven National Lab).
Specifically, preferred scaffold proteins include, but are not limited to,
those with known structures
(including variants) including cytokines (IL-Ira (+receptor complex), IL-1
(receptor alone), IL-1a, IL-1b
(including variants and or receptor complex), IL-2, IL-3, IL-4, IL-5, IL-6, IL-
8, IL-10, IFN-(3, INF-y, IFN-
a-2a; 1FN-a-2B, TNF-a; CD40 ligand (chk), Human Obesity Protein Leptin,
Granulocyte Colony-
Stimulating Factor, Bone Morphogenetic Protein-7, Ciliary Neurotrophic Factor,
Granulocyte-
Macrophage Colony-Stimulating Factor, Monocyte Chemoattractant Protein 1,
Macrophage Migration
Inhibitory Factor, Human Glycosylation-Inhibiting Factor, Human Rantes, Human
Macrophage
Inflammatory Protein 1 Beta, human growth hormone, Leukemia Inhibitory Factor,
Human Melanoma
Growth Stimulatory Activity, neutrophil activating peptide-2, Cc-Chemokine Mcp-
3, Platelet Factor M2,
Neutrophil Activating Peptide 2, Eotaxin, Stromal Cell-Derived Factor-1,
Insulin, Insulin-like Growth
Factor I, Insulin-like Growth Factor 11, Transforming Growth Factor B1,
Transforming Growth Factor
2 0 B2, Transforming Growth Factor B3, Transforming Growth Factor A, Vascular
Endothelial growth
factor (VEGF), acidic Fibroblast growth factor, basic Fibroblast growth
factor, Endothelial growth
factor, Nerve growth factor, Brain Derived Neurotrophic Factor, Ciliary
Neurotrophic Factor, Platelet
Derived Growth Factor, Human Hepatocyte Growth Factor, Glial Cell-Derived
Neurotrophic Factor, (as
well as the 55 cytokines in PDB 1112199)); Erythropoietin; other extracellular
signalling moeities,
including, but not limited to, hedgehog Sonic, hedgehog Desert, hedgehog
Indian, hCG; coaguation
factors including, but not limited to, TPA and Factor Vlla; transcription
factors, including but not limited
to, p53, p53 tetramerization domain, Zn fingers (of which more than 12 have
structures),
homeodomains (of which 8 have structures), leucine zippers (of which 4 have
structures); antibodies,
including, but not limited to, cFv; viral proteins, including, but not limited
to, hemagglutinin trimerization
3 0 domain and hiv Gp41 ectodomain (fusion domain); intracellular signalling
modules, including, but not
limited to, SH2 domains (of which 8 structures are known), SH3 domains (of
which 11 have
structures), and Pleckstin Homology Domains; receptors, including, but not
limited to, the extracellular
Region Of Human Tissue Factor Cytokine-Binding Region Of Gp130, G-CSF
receptor, erythropoietin
receptor, Fibroblast Growth Factor receptor, TNF receptor, 1L-1 receptor, IL-1
receptorIIL1ra complex,
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IL-4 receptor, INF-y receptor alpha chain, MHC Class I, MHC Class II , T Cell
Receptor, Insulin
receptor, insulin receptor tyrosine kinase and human growth hormone receptor.
Once a scaffold protein is chosen, a primary library is generated using
computational processing.
Generally speaking, the goal of the computational processing is to determine
a.set of optimized protein
sequences. By "optimized protein sequence" herein is meant a sequence that
best fits the
mathematical equations of the computational process. As will be appreciated by
those in the art, a
global optimized sequence is the one sequence that best fits the equations
(for example, when PDA is
used, the global optimzed sequence is the sequence that best fits Equation 1,
Below); i.e. the
sequence that has the lowest energy of any possible sequence. However, there
are any number of
sequences that are not the global minimum but that have low energies.
Thus, a "primary library" as used herein is a collection of optimized
sequences, generally in the form of
a rank-ordered list. In theory, all possible sequences of a protein may be
ranked; however, currently
10'3 sequences is a practical limit. Thus, in general, some subset of all
possible sequences is used as
the primary library; generally, the top 103 to 10'3 sequerices are chosen as
the primary library. The
cutoff for inclusion in the rank ordered list of the primary library can be
done in a variety of ways. For
example, the cutoff may be just an arbitrary exclusion point: the top 105
sequences may comprise the
primary library. Alternatively, all sequences scoring within a certain limit
of the global optimum can be
used; for example, all sequences with 10 kcal/mol of the global optimum could
be used as the primary
library. This method has the advantage of using a direct measure of fidelity
to a three dimensional
structure to determine inclusion. This approach can be used to insure that
library mutations are not
limited to positions that have the lowest energy gap between different
mutations. Alternatively, the
cutoff may be enforced when a predetermined number of mutations per pos~ion is
reached. As a rank
ordered sequence list is lengthened and the library is enlarged, more
mutations per position are
defined. Alternatively, the total number of sequences defined by the
recombination of all mutations
2 5 can be used as a cutoff criterion for the primary sequence library.
Preferred values for the total
number of sequences range from 100 to 102°, particularly preferred
values range from 1000 to 10'3,
especially preferred values range from 1000 to 10' Alternatively, the first
occurrence in the list of
predefined undesirable residues can be used as a cutoff criterion. For
example, the first hydrophilic
residue occurring in a core position would limit the list. It should also be
noked that while these
3 0 methods are described in conjunction with limiting the size of the primary
library, these same
techniques may be used to formulate the cutoff for inclusion in the secondary
library as well.
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Thus, the present invention provides methods to generate a primary library
comprising a rank ordered
list of sequences, generally in terms of theoretical quantitative stability,
as is more fully described
below. Generating a primary library to optimize the stability of a
conformation can be used to stabilize
the active site conformation of an enzyme, which will improve its activity.
Similarly, stabilizing a ligand-
receptor complex or enzyme-substrate complex wilt improve the binding
affinity.
The primary libraries can be generated in a variety of ways. In essence, any
methods that can result
in the relative ranking of the possible sequences of a protein based on
measurable stability
parameters can be used. As will be appreciated by those in the art, any of the
methods described
herein or known in the art may be used alone, or in combination with other
methods.
ZO In a preferred embodiment, the scaffold protein is an enzyme and highly
accurate electrostatic models
can be used for enzyme active site residue scoring to improve enzyme active
site libraries (see
Warshel, computer Modeling of Chemical Reactions in Enzymes and Solutions,
Wiley & Sons, New
York, (1991), hereby expressly incorporated by reference) These accurate
models can assess the
relative energies of sequences with high precision, but are computationally
intensive.
Similarly, molecular dynamics calculations can be used to computationally
screen sequences by
individually calculating mutant sequence scores and compiling a rank ordered
list.
In a preferred embodiment, residue pair potentials can be used to score
sequences (Miyazawa et al.,
Macromolecules 18(3):534-552 (1985), expressly incorporated by reference)
during computational
screening.
In a preferred embodiment, sequence profile scores (Bowie et al., Science
253(5016):164-70 (1991),
incorporated by reference) andlor potentials of mean force (Hendlich et al.,
J. Mol. Biol. 216(1 ):167-
180 (1990), also incorporated by reference) can also be calculated to score
sequences. These
methods assess the match between a sequence and a 3D protein structure and
hence can act to
screen for fidelity to the protein structure. By using different scoring
functions to rank sequences,
2 5 different regions of sequence space can be sampled in the computational
screen.
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Furthermore, scoring functions can be used to screen for sequences that would
create metal or co-
factor binding sites in the protein (Hellinga, Fold Des. 3(1 ):R1-8 (1998),
hereby expressly incorporated
by reference). Similarly, scoring functions can be used to screen for
sequences that would create
disulfide bonds in the protein. These potentials attempt to specifically
modify a protein structure to
introduce a new structural motif.
In addition, sequence andlor structural alignment programs can be used to
generate primary libraries.
For example, structural alignment of structurally related proteins can be done
to generate sequence
alignments (Orengo et al., Structure 5(8):1093-108 (1997); Holm et al.,
Nucleicpcid Res. 26(1):316-9
(1998), both of which are incorporated by reference). These sequence
alignments can then be
examined to determine the observed sequence variations.
Similarly, sequence homology based alignment methods can be used to create
sequence alignments
of proteins related to the target structure (Aftschul et al., J. Mol. Biol.
215(3):403 (1990), incorporated
by reference). These sequence alignments are then examined to determine the
observed sequence
variations. These sequence variations are tabulated to define a primary
library.
These sequence variations can be tabulated and a secondary library defined
from them as defined
below. Alternatively, the allowed sequence variations can be used to define
the amino acids
considered at each position during the computational screening. Another
variation is to bias the score
for amino acids that occur in the sequence alignment, thereby increasing the
likelihood that they are
found during computational screening but still allowing consideration of other
amino acids. This bias
2 0 would result in a focused primary library but would not eliminate from
consideration amino acids not
found in the alignment.
Similarly, as outlined above, other computational methods are known,
including, but not limited to,
sequence profiling {Bowie and Eisenberg, Science 253(5016): 164-70, (1991)),
rotamer library
selections (Dahiyat and Mayo, Protein Sci 5(5): 895-903 (1996); Dahiyat and
Mayo, Science
278(5335): 82-7 (1997); Desjarlais and Handel, Protein Science 4: 2006-2018
(1995); Harbury et al,
PNAS USA 92(18): 8408-8412 (1995}; Kono et al., Proteins: Structure, Function
and Genetics 19:
244-255 (1994); Hellinga and Richards, PNAS USA 91: 5803-5807 (1994)); and
residue pair potentials
(Jones, Protein Science 3: 567-574, (1994)), all of which are expressly
incorporated by reference.
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In a preferred embodiment, the computational method used to generate the
primary library is Protein
Design Automation (PDA), as is described in U.S.S.N.s 60/061,097, 60/043,464,
60/054,678,
091127,926 and PCT US98/07254, all of which are expressly incorporated herein
by reference.
Briefly, PDA can be described as follows. A known protein structure is used as
the starting point. The
residues to be optimized are then identified, which may be the entire sequence
or subsets) thereof.
The side chains of any positions to be varied are then removed. The resulting
structure consisting of
the protein backbone and the remaining sidechains is called the template. Each
variable residue
position is then preferably classified as a core residue, a surface residue,
or a boundary residue; each
classification defines a subset of possible amino acid residues for the
position (for example, core
residues generally will be selected from the set of hydrophobic residues,
surface residues generally
will be selected from the hydrophilic residues, and boundary residues may be
either). Each amino
acid can be represented by a discrete set of all allowed conformers of each
side chain, called
rotamers. Thus, to arrive at an optimal sequence for a backbone, all possible
sequences of rotamers
must be screened, where each backbone position can be occupied either by each
amino acid in all its
possible rotameric states, or a subset of amino acids, and thus a subset of
rotamers.
Two sets of interactions are then calculated for each rotamer at every
position: the interaction of the
rotamer side chain with all or part of the backbone (the "singles" energy,
also called the
rotamer/template or rotamer/backbone energy), and the interaction of the
rotamer side chain with all
other possible rotamers at every other position or a subset of the other
positions (the "doubles"
2 0 energy, also called the rotamer/rotamer energy). The energy of each of
these interactions is
calculated through the use of a variety of scoring functions, which include
the energy of van der Waal's
forces, the energy of hydrogen bonding, the energy of secondary structure
propensity, the energy of
surface area solvation and the electrostatics. Thus, the total energy of each
rotamer interaction, both
with the backbone and other rotamers, is calculated, and stored in a matrix
form.
2 5 The discrete nature of rotamer sets allows a simple calculation of the
number of rotamer sequences to
be tested. A backbone of length n with m possible rotamers per position will
have m" possible rotamer
sequences, a number which grows exponentially with sequence length and renders
the calculations
either unwieldy or impossible in real time. Accordingly, to solve this
combinatorial search problem, a
"Dead End Elimination" (DEE) calculation is performed. The DEE calculation is
based on the fact that
3 0 if the worst total interaction of a first rotamer is still better than the
best total interaction of a second
rotamer, then the second rotamer cannot be part of the global optimum
solution. Since the energies of
all rotamers have already been calculated, the DEE approach only requires sums
over the sequence
length to test and eliminate rotamers, which speeds up the calculations
considerably. DEE can be
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rerun comparing pairs of rotamers, or combinations of rotamers, which will
eventually result in the
determination of a single sequence which represents the global optimum energy.
Once the global solution has been found, a Monte Carlo search may be done to
generate a rank-
ordered list of sequences in the neighborhood of the DEE solution. Starting at
the DEE solution,
random positions are changed to other rotamers, and the new sequence energy is
calculated. If the
new sequence meets the criteria for acceptance, it is used as a starting point
for another jump. After a
predetermined number of jumps, a rank-ordered list of sequences is generated.
As outlined in U.S.S.N. 09/127,926, the protein backbone (comprising (for a
naturally occuring protein)
the nitrogen, the carbonyl carbon, the a-carbon, and the carbonyl oxygen,
along with the direction of
the vector from the a-carbon to the (3-carbon) may be altered prior to the
computational analysis, by
varying a set of parameters called supersecondary structure parameters.
Once a protein structure backbone is generated (with alterations, as outlined
above) and input into the
computer, explicit hydrogens are added if not included within the structure
{for example, if the structure
was generated by X-ray crystallography, hydrogens must be added). After
hydrogen addition, energy
minimization of the structure is run, to relax the hydrogens as well as the
other atoms, bond angles
and bond lengths. In a preferred embodiment, this is done by doing a number of
steps of conjugate
gradient minimization (Mayo et al., J. Phys. Chem. 94:8897 (1990)) of atomic
coordinate positions to
minimize the Dreiding force field with no electrostatics. Generally from about
10 to about 250 steps is
preferred, with about 50 being most preferred.
2 0 The protein backbone structure contains at least one variable residue
position. As is known in the art,
the residues, or amino acids, of proteins are generally sequentially numbered
starting with the N-
terminus of the protein. Thus a protein having a methionine at it's N-terminus
is said to have a
methionine at residue or amino acid position 1, with the next residues as 2,
3, 4, etc. At each position,
the wild type (i.e. naturally occuring) protein may have one of at least 20
amino acids, in any number
2 5 of rotamers. By "variable residue position" herein is meant an amino acid
position of the protein to be
designed that is not fixed in the design method as a specific residue or
rotamer, generally the wild-type
residue or rotamer.
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In a preferred embodiment, all of the residue positions of the protein are
variable. That is, every amino
acid side chain may be altered in the methods of the present invention. This
is particularly desirable
for smaller proteins, although the present methods allow the design of larger
proteins as well. While
there is no theoretical limit to the length of the protein which may be
designed this way, there is a
practical computational limit.
In an alternate preferred embodiment, only some of the residue positions of
the protein are variable,
and the remainder are "fixed", that is, they are identified in the three
dimensional structure as being in
a set conformation. In some embodiments, a fixed position is left in its
original conformation (which
may or may not correlate to a specific rotamer of the rotamer library being
used). Alternatively,
residues may be fixed as a non-wild type residue; for example, when known site-
directed mutagenesis
techniques have shown that a particular residue is desirable (for example, to
eliminate a proteolytic
site or alter the substrate specificity of an enzyme), the residue may be
fixed as a particular amino
acid. Alternatively, the methods of the present invention may be used to
evaluate mutations de novo,
as is discussed below. In an alternate preferred embodiment, a fixed position
may be "floated"; the
amino acid at that position is fixed, but different rotamers of that amino
acid are tested. In this
embodiment, the variable residues may be at least one, or anywhere from 0.1 %
to 99.9% of the total
number of residues. Thus, for example, it may be possible to change only a few
(or one) residues, or
most of the residues, with all possibilities in between.
In a preferred embodiment, residues which can be fixed include, but are not
limited to, structurally or
biologically functional residues. For example, residues which are known to be
important for biological
activity, such as the residues which form the active site of an enzyme, the
substrate binding site of an
enzyme, the binding site for a binding partner (ligand/receptor,
antigen/antibody, etc.), phosphorylation
or glycosylation sites which are crucial to biological function, or
structurally important residues, such
as disulfide bridges, metal binding sites, critical hydrogen bonding residues,
residues critical for
2 5 backbone conformation such as proline or glycine, residues critical for
packing interactions, etc. may
all be fixed in a conformation or as a single rotamer, or "floated".
Similarly, residues which may be chosen as variable residues may be those that
confer undesirable
biological attributes, such as susceptibility to proteolytic degradation,
dimerization or aggregation sites,
glycosylation sites which may lead to immune responses, unwanted binding
activity, unwanted
3 0 allostery, undesirable enzyme activity but with a preservation of binding,
etc.
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In a preferred embodiment, each variable position is classified as either a
core, surface or boundary
residue position, although in some cases, as explained below, the variable
position may be set to
glycine to minimize backbone strain. Any combination of core, surface and
boundary positions can be
utilized: core, surface and boundary residues; core and surface residues; core
and boundary residues,
and surface and boundary residues, as well as core residues alone, surface
residues alone, or
boundary residues alone.
The classification of residue positions as core, surface or boundary may be
done in several ways, as
will be appreciated by those in the art. In a preferred embodiment, the
classification is done via a
visual scan of the original protein backbone structure, including the side
chains, and assigning a
classification based on a subjective evaluation of one skilled in the art of
protein modelling.
Alternatively, a preferred embodiment utilizes an assessment of the
orientation of the Ca-Cp vectors
relative to a solvent accessible surface computed using only the template Ca
atoms, as outlined in
U.S.S.N.s 601061,097, 601043,464, 601054,678, 09/127,926 and PCT US98/07254.
Once each variable position is classified as either core, surface or boundary,
a set of amino acid side
chains, and thus a set of rotamers, is assigned to each position. That is, the
set of possible amino acid
side chains that the program will allow to be considered at any particular
position is chosen.
Subsequently, once the possible amino acid side chains are chosen, the set of
rotarners that will be
evaluated at a particular position can be determined. Thus, a core residue
will generally be selected
from the group of hydrophobic residues consisting of alanine, valine,
isoleucine, leucine,
2 0 phenylalanine, tyrosine, tryptophan, and methionine (in some embodiments,
when the a scaling factor
of the van der Waals scoring function, described below, is low, methionine is
removed from the set),
and the rotamer set for each core position potentially includes rotamers for
these eight amino acid side
chains (all the rotamers if a backbone independent library is used, and
subsets if a rotamer dependent
backbone is used). Similarly, surface positions are generally selected from
the group of hydrophilic
2 5 residues consisting of alanine, serine, threonine, aspartic acid,
asparagine, glutamine, glutamic acid,
arginine, lysine and histidine. The rotamer set for each surface position thus
includes rotamers for
these ten residues. Finally, boundary positions are generally chosen from
alanine, serine, threonine,
aspartic acid, asparagine, glutamine, glutamic acid, arginine, lysine
histidine, valine, isoleucine,
leucine, phenylalanine, tyrosine, tryptophan, and methionine. The rotamer set
for each boundary
3 0 position thus potentially includes every rotamer for these seventeen
residues (assuming cysteine,
glycine and proline are not used, although they can be). Additionally, in some
preferred embodiments,
a set of 18 naturally occuring amino acids {all except cysteine and proline,
which are known to be
particularly disruptive) are used.
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Thus, as will be appreciated by those in the art, there is a computational
benefit to classifying the
residue positions, as it decreases the number of calculations. It should also
be noted that there may
be situations where the sets of core, boundary and surface residues are
altered from those described
above; for example, under some circumstances, one or more amino acids is
either added or
subtracted from the set of allowed amino acids. For example, some proteins
which dimerize or
multimerize, or have ligand binding sites, may contain hydrophobic surface
residues, etc. In addition,
residues that do not allow helix "capping" or the favorable interaction with
an a-helix dipole may be
subtracted from a set of allowed residues. This modification of amino acid
groups is done on a
residue by residue basis.
In a preferred embodiment, proline, cysteine and glycine are not included in
the list of possible amino
acid side chains, and thus the rotamers for these side chains are not used.
However, in a preferred
embodiment, when the variable residue position has a ~ angle (that is, the
dihedral angle defined by 1)
the carbonyl carbon of the preceding amino acid; 2) the nitrogen atom of the
current residue; 3) the a-
carbon of the current residue; and 4) the carbonyl carbon of the current
residue) greater than 0°, the
position is set to glycine to minimize backbone strain.
Once the group of potential rotarners is assigned for each variable residue
position, processing
proceeds as outlined in U.S.S.N. 09/127,926 and PCT US98/07254. This
processing step entails
analyzing interactions of the rotamers with each other and with the protein
backbone to generate
optimized protein sequences. Simplistically, the processing initially
comprises the use of a number of
2 0 scoring functions to calculate energies of interactions of the rotamers,
either to the backbone itself or
other rotamers. Preferred PDA scoring functions include, but are not limited
to, a Van der Waals
potential scoring function, a hydrogen bond potential scoring function, an
atomic solvation scoring
function, a secondary structure propensity scoring function and an
electrostatic scoring function. As is
further described below, at least one scoring function is used to score each
position, although the
2 5 scoring functions may differ depending on the position classification or
other considerations, like
favorable interaction with an a-helix dipole. As outlined below, the total
energy which is used in the
calculations is the sum of the energy of each scoring function used at a
particular position, as is
generally shown in Equation 1:
Equation 1
3 O Evotat = nE"dw + nEas + nEh_~ing '~ nEss + nEen~
In Equation 1, the total energy is the sum of the energy of the van der Waals
potential (E~dW), the
energy of atomic solvation (Eas), the energy of hydrogen bonding (E,,.~"d;~~),
the energy of secondary
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structure (E55) and the energy of electrostatic interaction (Ee~e~). The term
n is either 0 or 1, depending
on whether the term is to be considered for the particular residue position.
As outlined in U.S.S.N.s 60/061,097, 60/043,464, 60/054,678, 09/127,926 and
PCT US98/07254, any
combination of these scoring functions, either alone or in combination, may be
used. Once the scoring
functions to be used are identified for each variable position, the preferred
first step in the
computational analysis comprises the determination of the interaction of each
possible rotamer with all
or part of the remainder of the protein. That is, the energy of interaction,
as measured by one or more
of the scoring functions, of each possible rotamer at each variable residue
position with either the
backbone or other rotamers, is calculated. In a preferred embodiment, the
interaction of each rotamer
with the entire remainder of the protein, i.e. both the entire template and
all other rotamers, is done.
However, as outlined above, it is possible to only model a portion of a
protein, for example a domain of
a larger protein, and thus in some cases, not all of the protein need be
considered.
In a preferred embodiment, the first step of the computational processing is
done by calculating two
sets of interactions for each rotamer at every position: the interaction of
the rotamer side chain with the
template or backbone (the "singles" energy), and the interaction of the
rotamer side chain with all other
possible rotamers at every other position (the "doubles" energy), whether that
position is varied or
floated. It should be understood that the backbone in this case includes both
the atoms of the protein
structure backbone, as well as the atoms of any fixed residues, wherein the
fixed residues are defined
as a particular conformation of an amino acid.
2 0 Thus, "singles" (rotamer/template) energies are calculated for the
interaction of every possible rotamer
at every variable residue posikion with the backbone, using some or all of the
scoring functions. Thus,
for the hydrogen bonding scoring function, every hydrogen bonding atom of the
rotamer and every
hydrogen bonding atom of the backbone is evaluated, and the EHS is calculated
for each possible
rotamer at every variable position. Similarly, for the van der Waals scoring
function, every atom of the
2 5 rotamer is compared to every atom of the template (generally excluding the
backbone atoms of its own
residue), and the E~~,,, is calculated for each possible rotamer at every
variable residue position. In
addition, generally no van der Waals energy is calculated if the atoms are
connected by three bonds
or less. For the atomic solvation scoring function, the surface of the rotamer
is measured against the
surface of the template, and the Eas for each possible rotamer at every
variable residue position is
3 0 calculated. The secondary structure propensity scoring function is also
considered as a singles
energy, and thus the total singles energy may contain an Eg$ term. As will be
appreciated by those in
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the art, many of these energy terms will be close to zero, depending on the
physical distance between
the rotamer and the template position; that is, the farther apart the two
moieties, the lower the energy.
For the calculation of "doubles" energy (rotameNrotamer), the interaction
energy of each possible
rotamer is compared with every possible rotamer at all other variable residue
positions. Thus,
"doubles" energies are calculated for the interaction of every possible
rotamer at every variable
residue position with every possible rotamer at every other variable residue
position, using some or all
of the scoring functions. Thus, for the hydrogen bonding scoring function,
every hydrogen bonding
atom of the first rotamer and every hydrogen bonding atom of every possible
second rotamer is
evaluated, and the EHB is calculated for each possible rotamer pair for any
two variable positions.
Similarly, for the van der Waals scoring function, every atom of the first
rotamer is compared to every
atom of every possible second rotamer, and the E~d"" is calculated for each
possible rotamer pair at
every two variable residue positions. For the atomic solvation scoring
function, the surface of the first
rotamer is measured against the surface of every possible second rotamer, and
the Ees for each
possible rotamer pair at every two variable residue positions is calculated.
The secondary structure
propensity scoring function need not be run as a "doubles" energy, as it is
considered as a component
of the "singles" energy. As will be appreciated by those in the art, many of
these double energy terms
will be close to zero, depending on the physical distance between the first
rotamer and the second
rotamer; that is, the farther apart the two moieties, the lower the energy.
Once the singles and doubles energies are calculated and stored, the next step
of the computational
processing may occur. As outlined in U.S.S.N. 09/127,926 and PCT US98107254,
preferred
embodiments utilize a Dead End Elimination (DEE) step, and preferably a Monte
Carlo step.
The computational processing results in a set of optimized protein sequences.
These optimized
protein sequences are generally, but not always, significantly different from
the wild-type sequence
from which the backbone was taken. That is, each optimized protein sequence
preferably comprises
2 5 at least about 5-10% variant amino acids from the starting or wild-type
sequence, with at least about
15-20% changes being preferred and at least about 30% changes being
particularly preferred.
The cutoff for the primary library is then enforced, resulting in a set of
primary sequences forming the
primary library. As outlined above, this may be done in a variety of ways,
including an arbitrary cutoff,
an energy limitation, or when a certain number of residue positions have been
varied. In general, the
3 0 size of the primary library will vary with the size of the protein, the
number of residues that are
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changing, the computational methods used, the cutoff applied and the
discretion of the user. In
general, it is preferable to have the primary library be large enough to
randomly sample a reasonable
sequence space to allow for robust secondary libraries. Thus, primary
libraries that range from about
50 to about 10'3 are preferred, with from about 1000 to about 10' being
particularly preferred, and from
about 1000 to about 100,000 being especially preferred.
In a preferred embodiment, although this is not required, the primary library
comprises the globally
optimal sequence in its optimal conformation, i.e. the optimum rotamer at each
variable position. That
is, computational processing is run until the simulation program converges on
a single sequence which
is the global optimum. In a preferred embodiment, the primary library
comprises at least two optimized
protein sequences. Thus for example, the computational processing step may
eliminate a number of
disfavored combinations but be stopped prior to convergence, providing a
library of sequences of
which the global optimum is one. In addition, further computational analysis,
for example using a
different method, may be run on the library, to further eliminate sequences or
rank them differently.
Alternatively, as is more fully described in U.S.S.N.s 60/061,097, 60/043,464,
60/054,678, 09/127,926
and PCT US98107254, the global optimum may be reached, and then further
computational
processing may occur, which generates additional optimized sequences in the
neighborhood of the
global optimum.
In addition, in some embodiments, primary library sequences that did not make
the cutoff are included
in the primary library. This may be desirable in some situations to evaluate
the primary library
generation method, to serve as controls or comparisons, or to sample
additional sequence space. For
example, in a preferred embodiment, the wild-type sequence is included.
14
It should also be noted that combining different primary libraries may be
done. For example, positions
in a protein that show a great deal of mutational diversity in computational
screening can be fixed as
outlined below and a different primary library regenerated. A rank ordered
list of the same length as
2 5 the first would now show diversity in previously rarely changing
positions. The variants from the first
primary library can be combined with the variants from the second primary
library to provide a
combined library at lower computational cost than creating a very long rank
ordered list. This
approach can be particularly useful to sample sequence diversity in both low
energy gap, readily
changing surface positions and high energy gap, rarely changing core
positions.
3 0 Thus, the present invention provides primary libraries comprising a rank
ordered list of sequences.
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In one embodiment, all or a portion of the primary library serves as the
secondary library. That is, a
cutoff is applied to the primary sequences and these sequences serve as the
secondary library,
without further manipulation or recombination. The library members can be made
as outlined below,
e.g. by direct synthesis or by constructing the nucleic acids encoding the
library members, expressing
them in a suitable host, optionally followed by screening.
In a preferred embodiment, the primary library of the scaffold protein is used
to generate a secondary
library. As will be appreciated by those in the art, the secondary library can
be either a subset of the
primary library, or contain new library members, i.e. sequences that are not
found in the primary
library. That is, in general, the variant positions andlor amino acid residues
in the variant positions can
be recombined in any number of ways to form a new library that exploits the
sequence variations
found in the primary library. That is, having identified "hot spots" or
important variant positions and/or
residues, these positions can be recombined in novel ways to generate novel
sequences to form a
secondary library. Thus, in a preferred embodiment, the secondary library
comprises at least one
member sequence that is not found in the primary library, and preferably a
plurality of such
sequences.
In a preferred embodiment, the secondary library is generated by tabulating
the amino acid positions
that vary from a reference sequence. The reference sequence can be arbitrarily
selected, or
preferably is chosen either as the wild-type sequence or the global optimum
sequence, with the latter
being preferred. That is, each amino acid position that varies in the primary
library is tabulated. Of
2 0 course, if the original computational analysis fixed some positions, the
variable positions of the
secondary library will comprise either just these original variable positions
or some subset of these
original variable positions. That is, assuming a protein of 100 amino acids,
the original computational
screen can allow all 100 positions to be varied. However, due to the cutoff in
the primary library, only
positions may vary. Alternatively, assuming the same 100 amino acid protein,
the original
25 computational screen could have varied only 25 positions, keeping the other
75 fixed; this could result
in only 12 of the 25 being varied in the cutoff primary library. These primary
library positions can then
be recombined to form a secondary library, wherein all possible combinations
of these variable
positions form the secondary library. It should be noted that the non-variable
positions are set to the
reference sequence positions.
3 0 The formation of the secondary library using this method may be done in
two general ways; either all
variable positions are allowed to be any amino acid, or subsets of amino acids
are allowed for each
position.
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In a preferred embodiment, all amino acid residues are allowed at each
variable position identified in
the primary library. That is, once the variable positions are identified, a
secondary library comprising
every combination of every amino acid at each variable position is made.
In a preferred embodiment, subsets of amino acids are chosen. The subset at
any position may be
either chosen by the user, or may be a collection of the amino acid residues
generated in the primary
screen. That is, assuming core residue 25 is variable and the primary screen
gives 5 different
possible amino acids for this position, the user may chose the set of good
core residues outlined
above (e.g. hydrophobic residues), or the user may build the set by chosing
the 5 different amino acids
generated in the primary screen. Alternatively, combinations of these
techniques may be used,
wherein the set of identified residues is manually expanded. For example, in
some embodiments,
fewer than the number of amino acid residues is chosen; for example, only
three of the five may be
chosen. Alternatively, the set is manually expanded; for example, if the
computation picks two
different hydrophobic residues, additional choices may be added.
In addition, this may be done by analyzing the primary library to determine
which amino acid positions
in the scaffold protein have a high mutational frquency, and which positions
have a low mutation
frequency. The secondary library can be generated by randomizing the amino
acids at the positions
that have high numbers of mutations, while keeping constant the positions that
do not have mutations
above a certain frequency. For example, if the position has less than 20% and
more preferably 10%
mutations, it may be kept constant as the reference sequence position.
In a preferred embodiment, a probability distribution table is generated. In
this embodiment, the
frequency of each amino acid residue at each variable position is identified.
Frequencies can be
thresholded, wherein any variant frequency lower than a cutoff is set to zero.
This cutoff is preferably
1 %, 2%, 5%, 10% or 20%, with 10% being particularly preferred. These
frequencies are then built into
the secondary library. That is, as above, these variable positions are
collected and all possible
2 5 combinations are generated, but the amino acid residues that "fill" the
secondary library are utilized on
a frequency basis. Thus, in a non-frequency based secondary library, a
variable position that has 5
possible residues will have 20% of the proteins comprising that variable
position with the first possible
residue, 20% with the second, etc. However, in a frequency based secondary
library, a variable
position that has 5 possible residues with frequencies of 10%, 15%, 25%, 30%
and 20%, respectively,
3 0 will have 10% of the proteins comprising that variable position with the
first possible residue, 15% of
the proteins with the second residue, 25% with the third, etc. As will be
appreciated by those in the
art, the actual frequency may depend on the method used to actually generate
the proteins; for
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example, exact frequencies may be possible when the proteins are synthesized.
However, when the
frequency-based primer system outlined below is used, the actual frequencies
at each position will
vary, as outlined below.
As will be appreciated, a secondary library created by recombining variable
positions andlor residues
at the variable position may not be in a rank-ordered list. In some
embodiments, the entire list may
just be made and tested. Alternatively, in a preferred embodiment, the
secondary library is also in the
form of a rank ordered list. This may be done for several reasons, including
the size of the secondary
library is still too big to generate experimentally, or for predictive
purposes. This may be done in
several ways. In one embodiment, the secondary library is ranked using the
scoring functions of PDA
to rank the library members. Alternatively, statistical methods could be used.
For example, the
secondary library may be ranked by frequency score; that is, proteins
containing the most of high
frequency residues could be ranked higher, etc. This may be done by adding or
multiplying the
frequency at each variable position to generate a numerical score. Similarly,
the secondary library
different positions could be weighted and then the proteins scored; for
example, those containing
certain residues could be artbitrarily ranked.
As outlined herein, secondary libraries can be generated in two general ways.
The first is
computationally, as above, wherein the primary library is further
computationally manipulated, for
example by recombining the possible variant positions and/or amino acid
residues at each variant
position. It may be ranked, as outlined above. This computationally-derived
secondary library can
2 0 then be experimentally generated by synthesizing the library members or
nucleic acids encoding them,
as is more fully outlined below. Alternatively, the secondary library is made
experimentally; that is,
nucleic acid recombination techniques are used to experimentally generate the
combinations. This
can be done in a variety of ways, as outlined below.
In a preferred embodiment, the different protein members of the secondary
library may be chemically
synthesized. This is particularly useful when the designed proteins are short,
preferably less than 750
amino acids in length, with less than 100 amino acids being preferred, and
less than 50 amino acids
being particularly preferred, although as is known in the art, longer proteins
can be made chemically or
enzymatically. See for example Wilken et al, Curr. Opin. Biotechnol. 9:412-26
(1998), hereby
expressly incorporated by reference.
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In a preferred embodiment, particularly for longer proteins or proteins for
which large samples are
desired, the secondary library sequences are used to create nucleic acids such
as DNA which encode
the member sequences and which can then be cloned into host cells, expressed
and assayed, if
desired. Thus, nucleic acids, and particularly DNA, can be made which encodes
each member protein
sequence. This is done using well known procedures. The choice of codons,
suitable expression
vectors and suitable host cells will vary depending on a number of factors,
and 'can be easily optimized
as needed.
In a preferred embodiment, multiple PCR reactions with pooled oligonucleotides
is done, as is
generally depicted in Figure 1. In this embodiment, overlapping
oligonucleotides are synthesized
which correspond to the full length gene. Again, these oligonucleotides may
represent all of the
different amino acids at each variant position or subsets.
In a preferred embodiment, these oligonucleotides are pooled in equal
proportions and multiple PCR
reactions are performed to create full length sequences containing the
combinations of mutations
defined by the secondary library.
In a preferred embodiment, the different oligonucleotides are added in
relative amounts corresponding
to the probability distribution table. The multiple PCR reactions thus result
in full length sequences
with the desired combinations of mutaions in the desired proportions.
The total number of oligonucleotides needed is a function of the number of
positions being mutated
and the number of mutations being considered at these positions:
2 0 (number of oligos for constant positions) + M1 + M2 + M3 +... Mn = (total
number of oligos required),
where Mn is the number of mutations considered at position n in the sequence.
In a preferred embodiment, each overlapping oligonucleotide comprises only one
position to be varied;
in alternate embodiments, the variant positions are too close together to
allow this and multiple
2 5 variants per oligonucleotide are used to allow complete recombination of
all the possibilities. That is,
each oligo can contain the codon for a single position being mutated, or for
more than one position
being mutated. The multiple positions being mutated must be close in sequence
to prevent the oligo
length from being impractical. For multiple mutating positions on an
oligonucleotide, particular
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combinations of mutations can be included or excluded in the library by
including or excluding the
oligonucleotide encoding that combination. The total number of
oligonucleotides required increases
when multiple mutable positions are encoded by a single ofigonucleotide. The
annealed regions are
the ones that remain constant, i.e. have the sequence of the reference
sequence.
Oligonucleotides with insertions or deletions of codons can be used to create
a library expressing
different length proteins. In particular computational sequence screening for
insertions or deletions
can result in secondary libraries defining different length proteins, which
can be expressed by a library
of pooled oligonucleotide of different lengths.
In a preferred embodiment, error-prone PCR is done to generate the secondary
library. See U.S.
Patent Nos. 5,605,793, 5,811,238, and 5,830,721, all of which are hereby
incorporated by reference.
This can be done on the optimal sequence or on top members of the library. In
this embodiment, the
gene for the optimal sequence found in the computational screen of the primary
library can be
synthesized. Error prone PCR is then performed on the optimal sequence gene in
the presence of
oligonucleotides that code for the mutations at the variant positions of the
secondary library (bias
oligonucleotides). The addition of the oligonucieotides will create a bias
favoring the incorporation of
the mutations in the secondary library. Alternatively, only oligonucleotides
for certain mutations may
be used to bias the library.
In a preferred embodiment, gene shuffling with error prone PCR can be
performed on the gene for the
2 0 optimal sequence, in the presence of bias oligonucleotides, to create a
DNA sequence library that
reflects the proportion of the mutations found in the secondary library. The
choice of the bias
oligonucleotides can be done in a variety of ways; they can chosen on the
basis of their frequency, i.e.
oligonucleotides encoding high mutational frequency positions can be used;
alternatively,
oligonucleotides containing the most variable positions can be used, such that
the diversity is
2 5 increased; if the secondary library is ranked, same number of top scoring
positions can be used to
generate bias oligonucleotides; random positions may be chosen; a few top
scoring and a few low
scoring ones may be chosen; etc. What is important is to generate new
sequences based on
preferred variable positions and sequences.
In a preferred embodiment, a secondary library may be computationally
remanipulated to form an
3 0 additional secondary library. For example, any of the secondary library
sequences may be chosen for
a second round of PDA, by freezing or fixing some or all of the changed
positions in the first secondary
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library. Alternatively, only changes seen in the last probability distribution
table are allowed.
Alternatively, the stringency of the probability table may be altered, either
by increasing or decreasing
the cutoff for inclusion. Similarly, the secondary library may be recombined
experimentally after the
first round; for example, the best gene/genes from the first screen may be
taken and gene assembly
redone (using techniques outlined below, multiple PCR, error prone PCR,
shuffling, etc.).
Alternatively, the fragments from one or more good genes) to change
probabilities at some positions.
This biases the search to an area of sequence space found in the first round
of computational and
experimental screening.
As outlined herein, any number of protein attributes may be altered in these
methods, including, but
not limited to, enzyme activity, stability, solubility, aggregation, binding
affinity, binding specificity,
substrate specificity, structural integrity, immunogenicity, toxicity,
generate peptide and
peptidomimmetic libraries, create new antibody CDR's, generate new DNA, RNA
bindings, etc.
It should be noted that therapeutic proteins utilized in these methods will
preferentially have residues
in the hydrophobic cores screened, to prevent changes in the molecular surface
of the protein that
might induce immunogenic responses. Therapeutic proteins cna also be designed
in the region
surrounding their binding sites to their receptors. Such a region can be
defined, for example, by
including in the design all residues within a certain distance, for example
4.5 h of the binding site
residues. This range can vary from 4 to 6 A. This designe will serve to
improve enzyme activity and
specificity.
2 0 In a preferred embodiment, the methods of the invention are used not on
known scaffold proteins, but
on random peptides, to search a virtual library for those sequences likely to
adapt a stable
conformation. As discussed above, there is a current benefit and focus on
screening random peptide
libraries to find novel binding/modulators. However, the sequences in these
experimental libraries can
be randomized at specific sites only, or throughout the sequence. The number
of sequences that can
2 5 be searched in these libraries grows expontentially with the number of
positions that are randomized.
Generally, only up to 10'2 - 10'5 sequences can be contained in a library
because of the physical
constraints of laboratories (the size of the instruments, the cost of
producing large numbers of
biopolymers, etc.). Other practical considerations can often limit the size of
the libraries to 106 or
fewer. These limits are reached for only 10 amino acid positions. Therefore,
only a sparse sampling
3 0 of sequences is possible in the search for improved proteins or peptides
in experimental sequence
libraries, lowering the chance of success and almost certainly missing
desirable candidates. Because
of the randomness of the changes in these sequences, most of the candidates in
the library are not
suitable, resulting in a waste of most of the effort in producing the library.
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However, using the automated protein design techniques outlined herein,
virtual libraries of protein
sequences can be generated that are vastly larger than experimental libraries.
Up to 10'5 candidate
sequences can be screened computationally and those that meet design criteria
which favor stable
and functional proteins can be readily selected. An experimental library
consisting of the favorable
candidates found in the virtual library screening can then be generated,
resulting in a much more
efficient use of the experimental library and overcoming the limitations of
random protein libraries.
Thus, the methods of the invention allow the virtual screening of a set of
random peptides for peptides
likely to take on a particular structure, and thus eliminating the large
number of unpreferred or
unallowed conformations without having to make and test the peptides.
As mentioned above, two principle benefits come from the virtual library
screening: (1 ) the automated
protein design generates a list of sequence candidates that are favored to
meet design criteria; it also
shows which positions in the sequence are readily changed and which positions
are unlikely to change
without disrupting protein stability and function. An experimental random
library can be generated that
is only randomized at the readily changeable, non-disruptive sequence
positions. (2) The diversity of
amino acids at these positions can be limited to those that the automated
design shows are
compatible with these positions. Thus, by limiting the number of randomized
positions and the number
of possibilities at these positions, the number of wasted sequences produced
in the experimental
library is reduced, thereby increasing the probability of success in finding
sequences with useful
properties.
2 0 For example, the table below lists the 10 favored sequences candidates
from the virtual screening of
12 positions in a protein. It shows that positions 9, 10 and 12 are most
likely to have changes that do
not disrupt the function of the protein, suggesting that a random experimental
library that randomizes
positions 9, 10 and 12 will have a higher fraction of desirable sequences.
Also, the virtual library
suggests that position 10 is most compatible with lle or Phe residues, further
limiting the size of the
library and allowing a more complete screening of good sequences.
1 2 3 4 5 6 7 8 9 10 11 12
1 LEU LEU ILE ILE ALA LEU LEU LEU LEU PHE ALA LEU
2 LEU LEU ILE ILE ALA LEU LEU LEU . LEU ILE ALA LEU
3 LEU LEU ILE ILE ALA LEU LEU LEU LEU ILE ALA LEU
4 LEU LEU ILE ILE ALA LEU LEU LEU LEU PHE ALA ILE
3 0 5 LEU LEU ILE ILE ALA LEU LEU LEU LEU PHE ALA ILE
6 LEU LEU ILE ILE ALA LEU LEU LEU LEU ILE ALA ILE
7 LEU LEU ILE ILE ALA LEU LEU LEU ILE PHE ALA LEU
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8 LEU LEU ILE ILE ALA LEU LEU LEU LEU ILE ALA ILE
9 LEU LEU ILE ILE ALA LEU LEU LEU ILE PHE ALA LEU
LEU LEU ILE ILE ALA LEU LEU LEU LEU LEU ALA LEU
The automated design method uses physical chemical criteria to screen
sequences, resulting in
5 sequences that are likely to be stable, structured, and that preserve
function, if needed. Different
design criteria can be used to produce candidate sets that are biased for
properties such as charged,
solubility, or active site characteristics (polarity, size), or are biased to
have certain amino acids at
certain positions. That is, The candidate bioactive agents and candidate
nucleic acids are
randomized, either fully randomized or they are biased in their randomization,
e.g. in
10 nucleotidelresidue frequency generally or per position. By "randomized" or
grammatical equivalents
herein is meant that each nucleic acid and peptide consists of essentially
random nucleotides and
amino acids, respectively. Thus, any amino acid residue may be incorporated at
any position. The
synthetic process can be designed to generate randomized peptides and/or
nucleic acids, to allow the
formation of all or most of the possible combinations over the length of the
nucleic acid, thus forming a
library of randomized candidate nucleic acids.
In one embodiment, the library is fully randomized, with no sequence
preferences or constants at any
position. In a preferred embodiment, the library is biased. That is, some
positions within the
sequence are either held constant, or are selected from a limited number of
possibilities. For example,
in a preferred embodiment, the nucleotides or amino acid residues are
randomized within a defined
2 0 class, for example, of hydrophobic amino acids, hydrophilic residues,
sterically biased (either small or
large) residues, towards the creation of cysteines, for cross-linking,
prolines for SH-3 domains,
serines, threonines, tyrosines or histidines for phosphorylation sites, etc.,
or to purines, etc.
In a preferred embodiment, the bias is towards peptides or nucleic acids that
interact with known
classes of molecules. For example, it is known that much of intracellular
signaling is carried out via
short regions of polypeptides interacting with other polypeptides through
small peptide domains. For
instance, a short region from the HIV-1 envelope cytoplasmic domain has been
previously shown to
block the action of cellular calmodulin. Regions of the Fas cytoplasmic
domain, which shows
homology to the mastoparan toxin from Wasps, can be limited to a short peptide
region with death-
inducing apoptotic or G protein inducing functions. Magainin, a natural
peptide derived from Xenopus,
3 0 can have potent anti-tumour and anti-microbial activity. Short peptide
fragments of a protein kinase C
isozyme (f3PKC), have been shown to block nuclear translocation of f3PKC in
Xenopus oocytes
following stimulation. And, short SH-3 target peptides have been used as
psuedosubstrates for
specific binding to SH-3 proteins. This is of course a short list of available
peptides with biological
activity, as the literature is dense in this area. Thus, there is much
precedent for the potential of small
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peptides to have activity on intracellular signaling cascades. In addition,
agonists and antagonists of
any number of molecules may be used as the basis of biased randomization of
candidate bioactive
agents as well.
In general, the generation of a prescreened random peptide libraries may be
described as follows.
Any structure, whether a known structure, for example a portion of a known
protein, a known peptide,
etc., or a synthetic structure, can be used as the backbone for PDA. For
example, structures from X-
ray crystallographic techniques, NMR techniques, de novo modelling, homology
modelling, etc. may
all be used to pick a backbone for which sequences are desired. Similarly, a
number of molecules or
protein domains are suitable as starting points for the generation of biased
randomized candidate
bioactive agents. A large number of small molecule domains are known, that
confer a common
function, structure or affinity. In addition, as is appreciated in the art,
areas of weak amino acid
homology may have strong structural homology. A number of these molecules,
domains, and/or
corresponding consensus sequences, are known, including, but are not limited
to, SH-2 domains, SH-
3 domains, Pleckstrin, death domains, protease cleavage/recognition sites,
enzyme inhibitors, enzyme
substrates, Traf, etc. Similarly, there are a number of known nucleic acid
binding proteins containing
domains suitable for use in the invention. For example, leucine zipper
consensus sequences are
known.
Thus, in general, known peptide ligands can be used as the starting backbone
for the generation of
the primary library.
In addition, structures known to take on certain conformations may be used to
create a backbone, and
then sequences screened for those that are likely to take on that
conformation. For example, there
are a wide variety of "ministructures" known, sometimes referred to as
"presentation structures", that
can confer conformational stability or give a random sequence a
conformationally restricted form.
Proteins interact with each other largely through conformationally constrained
domains. Although
2 5 small peptides with freely rotating amino and carboxyl termini can have
potent functions as is known in
the art, the conversion of such peptide structures into pharmacologic agents
is difficult due to the
inability to predict side-chain positions for peptidomimetic synthesis.
Therefore the presentation of
peptides in conformationally constrained structures will benefit both the
later generation of
pharmaceuticals and will also likely lead to higher affinity interactions of
the peptide with the target
3 0 protein. This fact has been recognized in the combinatorial library
generation systems using
biologically generated short peptides in bacterial phage systems. A number of
workers have
constructed small domain molecules in which one might present randomized
peptide structures.
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Thus, synthetic presentation structures, i.e. artificial polypeptides, are
capable of presenting a
randomized peptide as a conformationally-restricted domain. Preferred
presentation structures
maximize accessibility to the peptide by presenting it on an exterior loop.
Accordingly, suitable
presentation structures include, but are not limited to, minibody structures,
loops on beta-sheet turns
and coiled-coil stem structures in which residues not critical to structure
are randomized, zinc-finger
domains, cysteine-linked (disulfide) structures, transglutaminase linked
structures, cyclic peptides, B-
loop structures, helical barrels or bundles, leucine zipper motifs, etc.
In a preferred embodiment, the presentation structure is a coiled-coil
structure, allowing the
presentation of the randomized peptide on an exterior loop. See, for example,
~Myszka et al.,
Biochem. 33:2362-2373 (1994), hereby incorporated by reference, and Figure 3).
Using this system
investigators have isolated peptides capable of high affinity interaction with
the appropriate target. In
general, coiled-coil structures allow for between 6 to 20 randomized
positions; (see Martin et al.,
EMBO J. 13(22):5303-5309 (1994), incorporated by reference).
In a preferred embodiment, the presentation structure is a minibody structure.
A "minibody" is
essentially composed of a minimal antibody complementarity region. The
minibody presentation
structure generally provides two randomizing regions that in the folded
protein are presented along a
single face of the tertiary structure. See for example Bianchi et al., J. Mol.
Biol. 236(2):649-59 (1994),
and references cited therein, all of which are incorporated by reference).
Investigators have shown
this minimal domain is stable in solution and have used phage selection
systems in combinatorial
2 0 libraries to select minibodies with peptide regions exhibiting high
affinity, Kd = 10-', for the pro-
inflammatory cytokine Il--6.
Once the backbone is chosen and the primary library of the random peptides
generated as outlined
above, the secondary library generation and creation proceeds as for the known
scaffold protein,
including recombination of variant positions andlor amino acid residues,
either computationally or
experimentally. Again, libraries of DNA expressing the protein sequences
defined by the automated
protein design methods can be produced. Codons can be randomized at only the
nucleotide
sequence triplets that define the residue positions specified by the automated
design method. Also,
mixtures of base triplets that code for particular amino acids could be
introduced into the DNA
synthesis reaction to attach a full triplet defining an amino acid in one
reaction step. Also, a library of
3 0 random DNA oligomers could be designed that biases the desired positions
toward certain amino
acids, or that restricts those positions to certain amino acids. The amino
acids biased for would be
those specified in the virtual screening, or a subset of those.
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Multiple DNA libraries are synthesized that code for different subsets of
amino acids at certain
positions, allowing generation of the amio acid diversity desired without
having to fully randomize the
codon and thereby waste sequences in the library on stop codons, frameshifts,
undesired amino
acids, etc. This can be done by creating a library that at each position to be
randomized is only
randomized at one or two of the positions of the triplet, where the
position{s) left constant are those
that the amino acids to be considered at this position have in common.
Multiple DNA libraries would
be created to insure that all amino acids desired at each position exist in
the aggregate library.
Alternatively, the random peptide libraries may be done using the frequency
tabulation and
experimental generation methods including multiplexed PCR, shuffling, etc.
The present invention provides computer readable memories, central processing
units, associated
circuitry, and other associated compositions to implement the invention. The
apparatus of the
invention may include a central processing unit which communicates with a
memory and a set of
input/output devices (e.g., keyboard, mouse, monitor, printer, etc.) 26
through a bus. The general
interaction between a central processing unit, a memory, inputloutput devices,
and a bus is known in
the art. The present invention is directed toward the automated protein design
program and
secondary library generator stored in the memory.
The automated protein design program andlor the secondary library generator
may be implemented
with a side chain module. As discussed in detail in the associated
applications, the side chain module
establishes a group of potential rotamers for a selected protein backbone
structure. The protein
design program may also be implemented with a ranking module. As discussed in
detail below, the
ranking module analyzes the interaction of rotamers with the protein backbone
structure to generate
optimized protein sequences. The protein design program may also include a
search module to
execute a search, for example a Monte Carlo search as described below, in
relation to the optimized
protein sequences. Finally, an assessment module may also be used to assess
physical parameters
associated with the derived proteins, as discussed further below.
The memory also stores a protein backbone structure, which is downloaded by a
user through the
input/output devices. The memory also stores information on potential rotamers
derived by the side
chain module. In addition, the memory stores protein sequences generated by
the ranking module.
The protein sequences may be passed as output to the input/output devices.
3 0 Using the nucleic acids of the present invention which encode library
members, a variety of expression
vectors are made. The expression vectors may be either self-replicating
extrachromosomal vectors or
vectors which integrate into a host genome. Generally, these expression
vectors include
CA 02347214 2001-05-31
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transcriptional and translational regulatory nucleic acid operably linked to
the nucleic acid encoding the
library protein. The term "control sequences" refers to DNA sequences
necessary for the expression
of an operably linked coding sequence in a particular host organism. The
control sequences that are
suitable for prokaryotes, for example, include a promoter, optionally an
operator sequence, and a
ribosome binding site. Eukaryotic cells are known to utilize promoters,
polyadenylation signals, and
enhancers.
Nucleic acid is "operably linked" when it is placed into a functional
relationship with another nucleic
acid sequence. For example, DNA for a presequence or secretory leader is
operably linked to DNA
for a polypeptide if it is expressed as a preprotein that participates in the
secretion of the polypeptide;
a promoter or enhancer is operably linked to a coding sequence if it affects
the transcription of the
sequence; or a ribosome binding site is operably linked to a coding sequence
if it is positioned so as to
facilitate translation. Generally, "operably linked" means that the DNA
sequences being linked are
contiguous, and, in the case of a secretory leader, contiguous and in reading
phase. However,
enhancers do not have to be contiguous. Linking is accomplished by ligation at
convenient restriction
sites. If such sites do not exist, the synthetic oligonucleotide adaptors or
linkers are used in
accordance with conventional practice. The transcriptional and translational
regulatory nucleic acid
will generally be appropriate to the host cell used to express the library
protein, as will be appreciated
by those in the art; for example, transcriptional and translational regulatory
nucleic acid sequences
2 0 from Bacillus are preferably used to express the library protein in
Bacillus. Numerous types of
appropriate expression vectors, and suitable regulatory sequences are known in
the art for a variety of
host cells.
In general, the transcriptional and translational regulatory sequences may
include, but are not limited
to, promoter sequences, ribosomal binding sites, transcriptional start and
stop sequences,
translational start and stop sequences, and enhancer or activator sequences.
In a preferred
embodiment, the regulatory sequences include a promoter and transcriptional
start and stop
sequences.
Promoter sequences include constitutive and inducible promoter sequences. The
promoters may be
either naturally occurring promoters, hybrid or synthetic promoters. Hybrid
promoters, which combine
3 0 elements of more than one promoter, are also known in the art, and are
useful in the present-
invention.
In addition, the expression vector may comprise additional elements. For
example, the expression
vector may have two replication systems, thus allowing it to be maintained in
two organisms, for
example in mammalian or insect cells for expression and in a prokaryotic host
for cloning and
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amplification. Furthermore, for integrating expression vectors, the expression
vector contains at least
one sequence homologous to the host cell genome, and preferably two homologous
sequences which
flank the expression construct. The integrating vector may be directed to a
specific locus in the host
cell by selecting the appropriate homologous sequence for inclusion in the
vector. Constructs for
integrating vectors and appropriate selection and screening protocols are well
known in the art and are
described in e.g., Mansour et al., Cell, 51:503 (1988) and Murray, Gene
Transfer and Expression
Protocols, Methods in Molecular Biology, Vol. 7 (Clifton: Humana Press, 1991
).
In addition, in a preferred embodiment, the expression vector contains a
selection gene to allow the
selection of transformed host cells containing the expression vector, and
particularly in the case of
mammalian cells, ensures the stability of the vector, since cells which do not
contain the vector will
generally die. Selection genes are well known in the art and will vary with
the host cell used. By
"selection gene" herein is meant any gene which encodes a gene product that
confers resistance to a
selection agent. Suitable selection agents include, but are not limited to,
neomycin (or its analog
G418), blasticidin S, histinidol D, bieomycin, puromycin, hygromycin B, and
other drugs.
In a preferred embodiment, the expression vector contains a RNA splicing
sequence upstream or
downstream of the gene to be expressed in order to increase the level of gene
expression. See Barret
et al., Nucleic Acids Res. 1991; Groos et al., Mol. Cell. Biol. 1987; and
Budiman et al., Mol. Cell. Biol.
1988.
A preferred expression vector system is a retroviral vector system such as is
generally described in
Mann et al., Cell, 33:153-9 (1993); Pear et al., Proc. Natl. Acad. Sci.
U.S.A., 90(18):8392-6 (1993);
Kitamura et al., Proc. Natl. Acad. Sci. U.S.A., 92:9146-50 (1995); Kinsella et
al., Human Gene
Therapy, 7:1405-13; Hofmann et aL,Proc. Natl. Acad. Sci. U.S.A., 93:5185-90;
Choate et al., Human
Gene Therapy, 7:2247 (1996); PCTIUS97101019 and PCT/US97/01048, and references
cited therein,
all of which are hereby expressly incorporated by reference.
2 5 The library proteins of the present invention are produced by culturing a
host cell transformed with
nucleic acid, preferably an expression vector, containing nucleic acid
encoding an library protein,
under the appropriate conditions to induce or cause expression of the library
protein. The conditions
appropriate for library protein expression will vary with the choice of the
expression vector and the
host cell, and will be easily ascertained by one skilled in the art through
routine experimentation. For
3 0 example, the use of constitutive promoters in the expression vector will
require optimizing the growth
and proliferation of the host cell, while the use of an inducible promoter
requires the appropriate
growth conditions for induction. In addition, in some embodiments, the timing
of the harvest is
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important. For example, the baculoviral systems used in insect cell expression
are lytic viruses, and
thus harvest time selection can be crucial for product yield.
As will be appreciated by those in the art, the type of cells used in the
present invention can vary
widely. Basically, a wide variety of appropriate host cells can be used,
including yeast, bacteria,
archaebacteria, fungi, and insect and animal cells, including mammalian cells.
'Of particular interest
are Drosophila melanogasfer cells, Saccharomyces cerevisiae and other yeasts,
E. coli, Bacillus
subtilis, SF9 cells, C129 cells, 293 cells, Neurospora, BHK, CHO, COS, and
HeLa cells, fibroblasts,
Schwanoma cell lines, immortalized mammalian myeloid and lymphoid cell lines,
Jurkat cells, mast
cells and other endocrine and exocrine cells, and neuronal cells. See the ATCe
cell line catalog,
hereby expressly incorporated by reference. In addition, the expression of the
secondary libraries in
phage display systems, such as are well known in the art, are particularly
preferred, especially when
the secondary library comprises random peptides. In one embodiment, the cells
may be genetically
engineered, that is, contain exogeneous nucleic acid, for example, to contain
target molecules.
In a preferred embodiment, the library proteins are expressed in mammalian
cells. Any mammalian
cells may be used, with mouse, rat, primate and human cells being particularly
preferred, although as
will be appreciated by those in the art, modifications of the system by
pseudotyping allows all
eukaryotic cells to be used, preferably higher eukaryotes. As is more fully
described below, a screen
will be set up such that the cells exhibit a selectable phenotype in the
presence of a random library
member. As is more fully described below, cell types implicated in a wide
variety of disease conditions
2 0 are particularly useful, so long as a suitable screen may be designed to
allow the selection of cells that
exhibit an altered phenotype as a consequence of the presence of a library
member within the cell.
Accordingly, suitable mammalian cell types include, but are not limited to,
tumor cells of all types
(particularly melanoma, myeloid leukemia, carcinomas of the lung, breast,
ovaries, colon, kidney,
prostate, pancreas and testes), cardiomyocytes, endothelial cells, epithelial
cells, lymphocytes (T-cell
and B cell) , mast cells, eosinophils, vascular intimal cells, hepatocytes,
leukocytes including
mononuclear leukocytes, stem cells such as haemopoetic, neural, skin, lung,
kidney, liver and
myocyte stem cells (for use in screening for differentiation and de-
differentiation factors), osteoclasts,
chondrocytes and other connective tissue cells, keratinocytes, melanocytes,
liver cells, kidney cells,
and adipocytes. Suitable cells also include known research cells, including,
but not limited to, Jurkat T
3 0 cells, NIH3T3 cells, CHO, Cos, etc. See the ATCC cell line catalog, hereby
expressly incorporated by
reference.
Mammalian expression systems are also known in the art, and include retroviral
systems. A
mammalian promoter is any DNA sequence capable of binding mammalian RNA
polymerase and
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initiating the downstream (3') transcription of a coding sequence for library
protein into mRNA. A
promoter will have a transcription initiating region, which is usually placed
proximal to the 5' end of the
coding sequence, and a TATA box, using a located 25-30 base pairs upstream of
the transcription
initiation site. The TATA box is thought to direct RNA polymerase II to begin
RNA synthesis at the
correct site. A mammalian promoter will also contain an upstream promoter
element (enhancer
element), typically located within 100 to 200 base pairs upstream of the TATA
box. An upstream
promoter element determines the rate at which transcription is initiated and
can act in either
orientation. Of particular use as mammalian promoters are the promoters from
mammalian viral
genes, since the viral genes are often highly expressed and have a broad host
range. Examples
include the SV40 early promoter, mouse mammary tumor virus LTR promoter,
adenovirus major late
promoter, herpes simplex virus promoter, and the CMV promoter.
Typically, transcription termination and polyadenylation sequences recognized
by mammalian cells are
regulatory regions located 3' to the translation stop codon and thus, together
with the promoter
elements, flank the coding sequence. The 3' terminus of the mature mRNA is
formed by site-specific
post-translational cleavage and polyadenylation. Examples of transcription
terminator and
polyadenlytion signals include those derived form SV40.
The methods of introducing exogenous nucleic acid into mammalian hosts, as
well as other hosts, is
well known in the art, and will vary with the host cell used. Techniques
include dextran-mediated
transfection, calcium phosphate precipitation, polybrene mediated
transfection, protoplast fusion,
2 0 electroporation, viral infection, encapsulation of the polynucleotide{s)
in liposomes, and direct
microinjection of the DNA into nuclei.
In a preferred embodiment, library proteins are expressed in bacterial
systems. Bacterial expression
systems are well known in the art.
A suitable bacterial promoter is any nucleic acid sequence capable of binding
bacterial RNA
polymerase and initiating the downstream (3') transcription of the coding
sequence of library protein
into mRNA. A bacterial promoter has a transcription initiation region which is
usually placed proximal
to the 5' end of the coding sequence. This transcription initiation region
typically includes an RNA
polymerase binding site and a transcription initiation site. Sequences
encoding metabolic pathway
enzymes provide particularly useful promoter sequences. Examples include
promoter sequences
3 0 derived from sugar metabolizing enzymes, such as galactose, lactose and
maltose, and sequences
derived from biosynthetic enzymes such as tryptophan. Promoters from
bacteriophage may also be
used and are known in the art. In addition, synthetic promoters and hybrid
promoters are also useful;
for example, the tac promoter is a hybrid of the trp and lac promoter
sequences. Furthermore, a
34
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bacterial promoter can include naturally occurring promoters of non-bacterial
origin that have the
ability to bind bacterial RNA polymerase and initiate transcription.
In addition to a functioning promoter sequence, an efficient ribosome binding
site is desirable. In E.
coli, the ribosome binding site is called the Shine-Delgarno (SD) sequence and
includes an initiation
codon and a sequence 3-9 nucleotides in length located 3 - 11 nucleotides
upstream of the initiation
codon.
The expression vector may also include a signal peptide sequence that provides
for secretion of the
library protein in bacteria. The signal sequence typically encodes a signal
peptide comprised of
hydrophobic amino acids which direct the secretion of the protein from the
cell, as is well known in the
art. The protein is either secreted into the growth media (gram-positive
bacteria) or into the
periplasmic space, located between the inner and outer membrane of the cell
(gram-negative
bacteria).
The bacterial expression vector may also include a selectable marker gene to
allow for the selection of
bacterial strains that have been transformed. Suitable selection genes include
genes which render the
bacteria resistant to drugs such as ampicillin, chloramphenicol, erythromycin,
kanamycin, neomycin
and tetracycline. Selectable markers also include biosynthetic genes, such as
those in the histidine,
tryptophan and leucine biosynthetic pathways.
These components are assembled into expression vectors. Expression vectors for
bacteria are well
2 0 known in the art, and include vectors for Bacillus subtilis, E. coli,
Streptococcus cremoris, and
Streptococcus lividans, among others.
The bacterial expression vectors are transformed into bacterial host cells
using techniques well known
in the art, such as calcium chloride treatment, electroporation, and others.
In one embodiment, library proteins are produced in insect cells. Expression
vectors for the
2 5 transformation of insect cells, and in particular, baculovirus-based
expression vectors, are well known
in the art and are described e.g., in O'Reilly et al., Saculovirus Expression
Vectors: A Laboratory
Manual (New York: Oxford University Press, 1994).
In a preferred embodiment, library protein is produced in yeast cells. Yeast
expression systems are
well known in the art, and include expression vectors for Saccharomyces
cerevisiae, Candida albicans
3 0 and C. maltosa, Hansenula polymorpha, Kluyveromyces fragilis and K.
lactis, Pichia guillerimondii and
P. pastoris, Schizosaccharomyces pombe, and Yarrovria lipolytica. Preferred
promoter sequences for
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expression in yeast include the inducible GAL1,10 promoter, the promoters from
alcohol
dehydrogenase, enolase, glucokinase, glucose-6-phosphate isomerase,
glyceraldehyde-3-phosphate-
dehydrogenase, hexokinase, phosphofructokinase, 3-phosphoglycerate mutase,
pyruvate kinase, and
the acid phosphatase gene. Yeast selectable markers include ADE2, HIS4, LEU2,
TRP1, and ALG7,
which confers resistance to tunicamycin; the neomycin phosphotransferase gene,
which confers
resistance to 6418; and the CUP1 gene, which allows yeast to grow in the
presence of copper ions.
The library protein may also be made as a fusion protein, using techniques
well known in the art.
Thus, for example, for the creation of monoclonal antibodies, if the desired
epitope is small, the library
protein may be fused to a carrier protein to form an immunogen. Alternatively,
the library protein may
be made as a fusion protein to increase expression, or for other reasons. For
example, when the
library protein is an library peptide, the nucleic acid encoding the peptide
may be linked to other
nucleic acid for expression purposes. Similarly, other fusion partners may be
used, such as targeting
sequences which allow the localization of the library members into a
subcellular or extracellular
compartment of the cell, rescue sequences or purification tags which allow the
purification or isolation
of either the library protein or the nucleic acids encoding them; stability
sequences, which confer
stability or protection from degradation to the library protein or the nucleic
acid encoding it, for example
resistance to proteolytic degradation, or combinations of these, as well as
linker sequences as
needed.
Thus, suitable targeting sequences include, but are not limited to, binding
sequences capable of
causing binding of the expression product to a predetermined molecule or class
of molecules while
retaining bioactivity of the expression product, (for example by using enzyme
inhibitor or substrate
sequences to target a class of relevant enzymes); sequences signalling
selective degradation, of itself
or co-bound proteins; and signal sequences capable of constitutively
localizing the candidate
expression products to a predetermined cellular locale, including a)
subcellular locations such as the
2 5 Golgi, endoplasmic reticulum, nucleus, nucleoli, nuclear membrane,
mitochondria, chloroplast,
secretory vesicles, lysosome, and cellular membrane; and b) extracellular
locations via a secretory
signal. Particularly preferred is localization to either subcellular locations
or to the outside of the cell
via secretion.
In a preferred embodiment, the library member comprises a rescue sequence. A
rescue sequence is
3 0 a sequence which may be used to purify or isolate either the candidate
agent or the nucleic acid
encoding it. Thus, for example, peptide rescue sequences include purification
sequences such as the
Hiss tag for use with Ni affinity columns and epitope tags for detection,
immunoprecipitation or FACS
(fluoroscence-activated cell sorting). Suitable epitope tags include myc (for
use with the commercially
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available 9E10 antibody), the BSP biotinylation target sequence of the
bacterial enzyme BirA, flu tags,
IacZ, and GST.
Alternatively, the rescue sequence may be a unique oligonucleotide sequence
which serves as a
probe target site to allow the quick and easy isolation of the retroviral
construct, via PCR, related
techniques, or hybridization.
In a preferred embodiment, the fusion partner is a stability sequence to
confer stability to the library
member or the nucleic acid encoding it. Thus, for example, peptides may be
stabilized by the
incorporation of glycines after the initiation methionine (MG or MGGO), for
protection of the peptide to
ubiquitination as per Varshavsky's N-End Rule, thus conferring long half-life
in the cytoplasm.
Similarly, two prolines at the C-terminus impart peptides that are largely
resistant to carboxypeptidase
action. The presence of two glycines prior to the prolines impart both
flexibility and prevent structure
initiating events in the di-proline to be propagated into the candidate
peptide structure. Thus, preferred
stability sequences are as follows: MG(X)~GGPP, where X is any amino acid and
n is an integer of at
least four.
In one embodiment, the library nucleic acids, proteins and antibodies of the
invention are labeled. By
"labeled" herein is meant that nucleic acids, proteins and antibodies of the
invention have at least one
element, isotope or chemical compound attached to enable the detection of
nucleic acids, proteins and
antibodies of the invention. In general, labels fall into three classes: a)
isotopic labels, which may be
radioactive or heavy isotopes; b) immune labels, which may be antibodies or
antigens; and c) colored
2 0 or fluorescent dyes. The labels may be incorporated into the compound at
any position.
In a preferred embodiment, the library protein is purified or isolated after
expression. Library proteins
may be isolated or purified in a variety of ways known to those skilled in the
art depending on what
other components are present in the sample. Standard purification methods
include electrophoretic,
molecular, immunological and chromatographic techniques, including ion
exchange, hydrophobic,
affinity, and reverse-phase HPLC chromatography, and chromatofocusing. For
example, the library
protein may be purified using a standard anti-library antibody column.
Ultrafiltration and diafiltration
techniques, in conjunction with protein concentration, are also useful. For
general guidance in suitable
purification techniques, see Scopes, R., Protein Purification, Springer-
Verlag, NY (1982). The degree
of purification necessary will vary depending on the use of the library
protein. In some instances no
3 0 purification will be necessary.
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Once expressed and purified if necessary, the library proteins and nucleic
acids are useful in a
number of applications.
In general, the secondary libraries are screened for biological activity.
These screens will be based on
the scaffold protein chosen, as is known in the art. Thus, any number of
protein activities or attributes
may be tested, including its binding to its known binding members (for
example, its substrates, if it is
an enzyme), activity profiles, stability profiles (pH, thermal, buffer
conditions), substrate specificity,
immunogenicity, toxicity, etc.
When random peptides are made, these may be used in a variety of ways to
screen for activity. In a
preferred embodiment, a first plurality of cells is screened. That is, the
cells into which the library
member nucleic acids are introduced are screened for an altered phenotype.
Thus, in this
embodiment, the effect of the library member is seen in the same cells in
which it is made; i.e. an
autocrine effect.
By a "plurality of cells" herein is meant roughly from about 103 cells to 108
or 109, with from 106 to 108
being preferred. This plurality of cells comprises a cellular library, wherein
generally each cell within
the library contains a member of the secondary library, i.e. a different
library member, although as will
be appreciated by those in the art, some cells within the library may not
contain one and and some
may contain more than one. When methods other than retrovirai infection are
used to introduce the
library members into a plurality of cells, the distribution of library members
within the individual cell
members of the cellular library may vary widely, as it is generally difficult
to control the number of
2 0 nucleic acids which enter a cell during electroporation, etc.
In a preferred embodiment, the library nucleic acids are introduced into a
first plurality of cells, and the
effect of the library members is screened in a second or third plurality of
cells, different from the first
plurality of cells, i.e. generally a different cell type. That is, the effect
of the library member is due to an
extracellular effect on a second cell; i.e. an endocrine or paracrine effect.
This is done using standard
techniques. The first plurality of cells may be grown in or on one media, and
the media is allowed to
touch a second plurality of cells, and the effect measured. Alternatively,
there may be direct contact
between the cells. Thus, "contacting" is functional contact, and includes both
direct and indirect. In
this embodiment, the first plurality of cells may or may not be screened.
If necessary, the cells are treated to conditions suitable for the expression
of the library members (for
3 0 example, when inducible promoters are used), to produce the library
proteins.
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Thus, in one embodiment, the methods of the present invention comprise
introducing a molecular
library of library members into a plurality of cells, a cellular library. The
plurality of cells is then
screened, as is more fully outlined below, for a cell exhibiting an altered
phenotype. The altered
phenotype is due to the presence of a library member.
By °altered phenotype" or "changed physiology" or other grammatical
equivalents herein is meant that
the phenotype of the cell is altered in some way, preferably in some
detectable andlor measurable
way. As will be appreciated in the art, a strength of the present invention is
the wide variety of cell
types and potential phenotypic changes which may be tested using the present
methods. Accordingly,
any phenotypic change which may be observed, detected, or measured may be the
basis of the
screening methods herein. Suitable phenotypic changes include, but are not
limited to: gross physical
changes such as changes in cell morphology, cell growth, cell viability,
adhesion to substrates or other
cells, and cellular density; changes in the expression of one or more RNAs,
proteins, lipids, hormones,
cytokines, or other molecules; changes in the equilibrium state (i.e. half-
life) or one or more RNAs,
proteins, lipids, hormones, cytokines, or other molecules; changes in the
localization of one or more
RNAs, proteins, lipids, hormones, cytokines, or other molecules; changes in
the bioactivity or specific
activity of one or more RNAs, proteins, lipids, hormones, cytokines,
receptors, or other molecules;
changes in the secretion of ions, cytokines, hormones, growth factors, or
other molecules; alterations
in cellular membrane potentials, polarization, integrity or transport; changes
in infectivity,
susceptability, latency, adhesion, and uptake of viruses and bacterial
pathogens; etc. By "capable of
2 0 altering the phenotype" herein is meant that the library member can change
the phenotype of the cell
in some detectable andlor measurable way.
The altered phenotype may be detected in a wide variety of ways, and will
generally depend and
correspond to the phenotype that is being changed. Generally, the changed
phenotype is detected
using, for example: microscopic analysis of cell morphology; standard cell
viability assays, including
2 5 both increased cell death and increased cell viability, for example, cells
that are now resistant to cell
death via virus, bacteria, or bacterial or synthetic toxins; standard labeling
assays such as fluorometric
indicator assays for the presence or level of a particular cell or molecule,
including FACS or other dye
staining techniques; biochemical detection of the expression of target
compounds after killing the cells;
etc. In some cases, as is more fully described herein, the altered phenotype
is detected in the cell in
3 0 which the randomized nucleic acid was introduced; in other embodiments,
the altered phenotype is
detected in a second cell which is responding to some molecular signal from
the first cell.
In a preferred embodiment, the library member is isolated from the positive
cell. This may be done in
a number of ways. In a preferred embodiment, primers complementary to DNA
regions common to
the constructs, or to specific components of the library such as a rescue
sequence, defined above, are
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used to "rescue" the unique random sequence. Alternatively, the member is
isolated using a rescue
sequence. Thus, for example, rescue sequences comprising epitope tags or
purification sequences
may be used to pull out the library member, using immunoprecipitation or
affinity columns. In some
instances, this may also pull out things to which the library member binds
(for example the primary
target molecule) if there is a sufficiently strong binding interaction between
the library member and the
target molecule. Alternatively, the peptide may be detected using mass
spectroscopy.
Once rescued, the sequence of the library member is determined. This
information can then be used
in a number of ways.
In a preferred embodiment, the member is resynthesized and reintroduced into
the target cells, to
verify the effect. This may be done using retroviruses, or alternatively using
fusions to the HIV-1 Tat
protein, and analogs and related proteins, which allows very high uptake into
target cells. See for
example, Fawell et al., PNAS USA 91:664 (1994); Frankel et al., Cell 55:1189
(1988); Savion et al., J.
Biol. Chem. 256:1149 (1981); Derossi et al., J. Biol. Chem. 269:10444 (1994);
and Baldin et al., EMBO
J. 9:1511 (1990), all of which are incorporated by reference.
In a preferred embodiment, the sequence of the member is used to generate more
libraries, as
outlined herein.
In a preferred embodiment, the library member is used to identify target
molecules, i.e. the molecules
with which the member interacts. As will be appreciated by those in the art,
there may be primary
target molecules, to which the library member binds or acts upon directly, and
there may be secondary
2 0 target molecules, which are part of the signalling pathway affected by the
library member; these might
be termed "validated targets".
The screening methods of the present invention may be useful to screen a large
number of cell types
under a wide variety of conditions. Generally, the host cells are cells that
are involved in disease
states, and they are tested or screened under conditions that normally result
in undesirable
consequences on the cells. When a suitable library member is found, the
undesirable effect may be
reduced or eliminated. Alternatively, normally desirable consequences may be
reduced or eliminated,
with an eye towards elucidating the cellular mechanisms associated with the
disease state or
signalling pathway.
The following examples serve to more fully describe the manner of using the
above-described
3 0 invention, as well as to set forth the best modes contemplated for
carrying out various aspects of the
invention. It is understood that these examples in no way serve to limit the
true scope of this invention,
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but rather are presented for illustrative purposes. All references cited
herein are incorporated by
reference.
EXAMPLES
Example 1
Computational Prescreening on [3-lactamase TEM-1 '
Preliminary experiments were performed on the p-lactamase gene TEM-1.
Brookhaven Protein Data
Bank entry 1 BTL was used as the starting structure. All water molecules and
the S0,2~ group were
removed and explicit hydrogens were generated on the structure. The structure
was then minimized
for 50 steps without electrostatics using the conjugate gradient method and
the Dreiding II force field.
These steps were performed using the BIOGRAF program (Molecular Simulations,
Inc., San Diego,
CA). This minimized structure served as the template for all the protein
design calculations.
computational Pre-screening
Computational pre-screening of sequences was performed using PDA. A 4 A sphere
was drawn
around the heavy side chain atoms of the four catalytic residues (S70, K73,
5130, and E166) and all
amino acids having heavy side chain atoms within this distance cutoff were
selected. This yielded the
following 7 positions: F72, Y105, N132, N136, L169, N170, and K234. Two of
these residues, N132
and K234, are highly conserved across several different p-lactamases and were
therefore not included
in the design, leaving five variable positions (F72, Y105, N136, L169, N170).
These designed
positions were allowed to change their identity to any of the 20 naturally
occurring amino acids except
2 0 proline, cysteine, and glycine (a total of 17 amino acids). Proline is
usually not allowed since it is
difficult to define appropriate rotamers for proline, cysteine is excluded to
prevent formation of disulfide
bonds, and glycine is excluded because of conformational flexibility. ..
Additionally, a second set of residues within 5 A of the residues selected for
PDA design were floated
(their amino acid identity was retained as wild type, but their conformation
was allowed to change).
2 5 The heavy side chain atoms were again used to determine which residues
were within the cutoff. This
yielded the following 28 positions: M68, M69, S70, T71, K73, V74, L76, V103,
E104, S106, P107, 1127,
M129, S130, A135, L139, L148, L162, 8164, W165, E166, P167, D179, M211, D214,
V216, S235,
1247. A248 was included as a floated position instead of 1247. The two
prolines, P107 and P167,
were excluded from the floated residues, as were positions M69, 8164, and
W165, since their crystal
3 0 structures exhibit highly strained rotamers, leaving 23 floated residues
from the second set. The
conserved residues N132 and K234 from the first sphere (4 A) were also
floated, resulting in a total of
floated residues.
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The potential functions and parameters used in the PDA calculations were as
follows. The van der
Waals scale factor was set to 0.9, and the electrostatic potential was
calculated using a distance
attenuation and a dielectric constant of 40. The well depth for the hydrogen
bond potential was set to
8 kcal/mol with a local and remote backbone scale factor of 0.25 and 1.0
respectively. The solvation
potential was only calculated for designed positions classified as core (F72,
L169, M68, T71, V74,
L76, 1127, A135, L139, L148, L162, M211 and A248). Type 2 solvation was used
(Street and Mayo,
1998). The non-polar exposure multiplication factor was set to 1.6, the non-
polar burial energy was
set to 0.048 kcallmol/A2, and the polar hydrogen burial energy was set to 2.0
kcal/mol.
The Dead End Elimination (DEE) optimization method (see reference) was used to
find the lowest
energy, ground state sequence. DEE cutoffs of 50 and 100 kcal/mol were used
for singles and
doubles energy calculations, respectively.
Starting from the DEE ground state sequence, a Monte Carlo (MC) calculation
was pertormed that
generated a list of the 1000 lowest energy sequences. The MC parameters were
100 annealing
cycles with 1,000,000 steps per cycle. The non-productive cycle limit was set
to 50. In the annealing
schedule, the high and low temperatures were set to 5000 and 100 K
respectively.
The following probability distribution was then calculated from the top 1000
sequences in the MC list
(see Table 3 below). It shows the number of occurrences of each of the amino
acids selected for each
position (the 5 variable positions and the 25 floated positions).
Table 3: Monte Carlo analysis (amino acids and their number of
occurrences (for the top 1000 sequences).
2 0 Posi-
tion Amino acid occurrences
68 M:1000
70 S:1000
71 T:1000
2 5 72 Y:591 F:365 V: 35 E: 8 L: 1
73 K:1000
74 V:1000
76 L:1000
103 V:1000
3 0 104 E:1000
105 M:183 Q:142 1:132 N:129 E:126
S:115 D: 97 A: 76
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106 5:1000
127 1:1000
129 M:1000
130 S:1000
'132 N:1000
135 A:1000
136 D:530 M:135 N: 97 V: 68 E: 66 S: 38 T: 33 A: 27 Q: 6
139 L:1000
148 L:1000
162 L:1000
166 E:1000
169 L:689 E:156 M: 64 S: 37 D: 23 A: 21 Q: 10
170 M:249 L:118 E:113 D:112 T: 90 Q: 87 S: 66 R: 44 A: 35 N: 24
F: 21
K: 15 Y: 9 H: 9 V: 8
179 D:1000
211 M:1000
214 D:1000
216 V:1000
234 K:1000
2 235 S:1000
0
2 4 8 A . 1 0 0 0
This probability distribution was then transformed into a rounded probability
distribution (see Table 4). A
10% cutoff value was used to round at the designed positions and the wild type
amino acids were forced
to occur with a probability of at least 10%. An E was found at position 169
15.6% of the time. However,
2 5 since this position is adjacentto another designed position, 170, its
closeness would have required a more
complicated oligonucleotidelibrary design; E was therefore not included for
this position when generating
the sequence library (only L was used).
Table 4: PDA probability distribution for the designed positions of ~3-
lactamase (rounded to the nearest
10%).
3 0 72 105 136 169 170
Y 50% M 20% D 70% L 100% M 30%
F 50% Q 20% M 20% L 20%
I 20% N 10% E 20%
N 10% D 20%
E 10% N 10%
S 10%
Y 10%
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As seen from Table 4, the computational pre-screening resulted in an enormous
reduction in the size of
the problem. Originally,17 different amino acids were allowed at each of the 5
designed positions, giving
175=1,419,857 possible sequences. This was pared down to just 2*7*3*1*5=210
possible sequences
- a reduction of nearly four orders of magnitude.
Generation of Sequence Library
Overlappingoligonucleotidescorrespondingto the full length TEM-1 gene for (3-
lacatamaseand all desired
mutations were synthesized and used in a PCR reaction as described previously
(Figure 1), resulting in
a sequence library containing the 210 sequences described above.
S_3rnthesis of mutant TEM-1 genes
To allow the mutation of the TEM-1 gene, pCR2.1 (Invitrogen) was digested with
Xbal and EcoRl, blunt
ended with T4 DNA polymerase, and religated. This removes the Hindlll and Xhol
sites within the polylinker.
A new Xhol site was then introduced into the TEM-1 gene at position 2269
(numbering as of the original
pCR2.1 ) using a Quickchange Site-Directed Mutagenesis Kit as described by the
manufacturer (Stratagene).
Similarly, a new Hindlll site was introduced at position 2674 to give pCR-
Xen1.
To construct the mutated TEM-1 genes, overlapping 40mer oligonucleotides were
synthesized corresponding
to the sequence between the newly introduced Xho1 and Hindlll sites, designed
to allow a 20 nucleotide
overlap with adjacent oligonucleotides. At each of the designed positions (72,
105, 136 and 170) multiple
oligonucleotideswere synthesized, each containing a differentmutation so that
all the possible combinations
of mutant sequences (210) could be made in the desired proportans as shown in
Table 4. For example,
2 0 at position 72, two sets of oligonucleotideswere synthesized, one
containing an F at position 72, the other
containing a Y. Each oligonucleotide was resuspended at a concentration of
1NglNl, and equal molar
concentrations of the oligonucleotides were pooled.
At the redundantpositions,each oiigonucleotidewas added at a concentrationthat
reflected the probabilities
in Table 4. For example, at position 72 equal amounts of the two
oligonucleotideswere added to the pool,
2 5 while at position 136, twice as much M-containing oligonucleotidewas added
compared to the N-containing
oligonucleotide, and seven times as much D-containing oligonucleotide was
added compared to the N-
containing oligonucleotide.
DNA library assembly
For the first round of PCR, 2 NI of pooled oligonucleotidesat the desired
probabilities (Table 4) were added
3 0 to a 100 NI reaction that contained 2 NI 10 mM dNTPs, 10 NI 10x Taq buffer
(Qiagen), 1 NI of Taq DNA
polymerase(5 units/NI: Qiagen) and 2 NI Pfu DNA polymerase (2.5 unitslNl:
Promega). The reaction mixture
was assembled on ice and subjected to 94°C for 5 minutes, 15 cycles of
94°C for 30 seconds, 52°C for
30 seconds and 72°C for 30 seconds, and a final extension step of
72°C for 10 minutes.
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Isolation of full length oliaonucleotides
For the second round of PCR, 2.5 NI of the first round reaction was added to a
100 NI reaction containing
2 NI 10 mM dNTPs, 10 NI of 10x Pfu DNA polymerase buffer (Promega), 2 NI Pfu
DNA polymerase (2.5
unitslNl: Promega), and 1 Ng of oligonucleotides corresponding to the 5' and
3' ends of the synthesized
gene. The reaction mixture was assembled on ice and subjected to 94°C
for 5 minutes, 20 cycles of 94°C
for 30 seconds, 52°C for 30 seconds and 72°C for 30 seconds, and
a final extension step of 72°C for 10
minutes to isolate the full length oligonucleotides.
Purification of DNA libray
The PCR products were purified using a QIAquick PCR Purification Kit (Qiagen),
digested with Xho1 and
Hindlll, electrophoresedthrough a 1.2 % agarose gel and re-purified using a
QIAquick Gel Extraction Kit
(Qiagen).
Verification of Sequence Library Idgntityr
The PCR products containing the library of mutant TEM-1 (3-lactamase genes
were then cloned between
a promoter and terminator in a kanamycin resistantplasmid and transformed into
E. coli. An equal number
of bacteriawere then spread onto media containing either kanamycin or
ampicillin. All transformedcolonies
will be resistantto kanamycin, but only those with active mutated p-
lactamasegenes will grow on ampicillin.
After overnight incubation, several colonies were observed on both plates,
indicating that at least one of
the above sequences encodes an active (i-lactamase. The number of colonies on
the kanamycin plate
far outnumbered those on the ampicillin plate (roughly a 5:1 ratio) suggesting
that either some of the
2 0 sequences destroy activity, or that the PCR introduces errors that yield
an inactive or truncated enzyme.
To distinguish between these possibilities, 60 colonies were picked from the
kanamycin plate and their
piasmid DNA was sequenced. This gave the distribution shown in Table 5.
Table 5: Percentages predicted by PDA vs. those observed from experiment for
the designed positions.
Wild Type PDA Residues (Predicted PercentagelObserved Percentage)
2 5 72F Y 50/50 F 50150
105Y M 20/27 Q 20/18 I 20/21 N 10/7 E 10/7 S 10/10 Y 10/10
136N D 70/72 M 20/17 N 10/11
170N M 30/34 L 20/21 E 20121 D 20/17 N 10/7
Note that the observed percentages of each amino acid at all four positions
closely match the predicted
3 0 percentages. Sequencing also revealed that only one of the 60 colonies
contained a PCR error, a G to
C transition.
This small test demonstrates that multiple PCR with pooled oligonucleotides
can be used to construct a
sequence library that reflects the desired proportions of amino acid changes.
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Experimental Screening of Seduence Library
The purified PCR product containing the library of mutated sequences was then
ligated into pCR-Xen1
that had previously been digestedwith Xho1 and Hindlll and purified. The
ligation reaction was transformed
into competentTOP10 E. colt cells (Invitrogen). After allowing the cells to
recover for 1 hour at 37°C, the
cells were spread onto LB plates containing the antibiotic cefotaxime at
concentrations ranging from 0.1
Ng/ml to 50 Ng/ml and selected for increasing resistance.
A triple mutant was found that improved enzyme function by 35 fold in only a
single round of screening
(see Figure 4). This mutant (Y105Q, N136D, N170L) survived at 50 Ng/ml
cefotaxime.
Example 2
Secondary Library generation of a Xylanase
PDA Pre-screening Leads to Enormous Reduction in Number of Possible Sequences
To demonstrate that computational pre-screening is feasible and will lead to a
significant reduction in the
number of sequences that have to be experimentally screened, initial
calculations for the B. circulans
xylanasewith and withoutthe substratewere performed. The PDB structure 1XNB of
8. circulansxylanase
and 1 BCX for the enzyme substrate complex were used. 27 residues inside the
binding site were visually
identified as belonging to the active site. 8 of these residues were regarded
as absolutely essential for
the enzymatic activity. These positions were treated as wild type residues,
which means that their
conformation was allowed to change but not their amino acid identity (see
Figure 2).
Three of the 20 naturally occurring amino acids were not considered (cysteine,
proline, and glycine).
2 0 Therefore, 17 different amino acids were still possible at the remaining
19 positions; the problem yields
17'9 = 2.4 x1023 different amino acid sequences. This number is 10 orders of
magnitude larger than what
can be handled by state of the art directed evolution methods. Clearly these
approaches cannot be used
to screen the completedimensionalityof the problem and considerall
sequenceswith multiplesubstitutions
Therefore PDA calculationswere performed to reduce the search space. A list of
the 10,000 lowest energy
2 5 sequenceswas created and the probabilityfor each amino acid at each
position was determined (see Table
1 ).
46
CA 02347214 2001-05-31
WO 00/23564 ~ PCT/US99/24229
Table 1: Probability of amino acids at the designed positions resulting from
the PDA calculation of the wild
type (WT) enzyme structure. Only amino acids with a probability greater than 1
% are shown.
tutu
rrvuauuuv
mamuuu~m
Y W 37.2% F 25.8% Y 22.9% H 14.0%
5 7Q E69.1% L30.2%
11 D I 41.2% D 10.7% V 10.1% M 7.9% L 6.4% , E 5.3% T
4.2%
Q 3.8% Y 2.6% F 2.1 N 1.9% S 1.9% A 1.1
%
37 V D 29.9% M 29.4% V 21.4% S 12.8% I 4.1 E 1.0%
%
39 G A 99.8%
63 N W 91.2% Q 6.7% A 1.4%
65 Y E 91.7% L 4.9% M 3.4%
67 T E 81.0% D 12.3% L 3.9% A 1.7%
71 W V 37.8% F 25.5% W 8.5% M 6.0% D 5.8% E 4.3% I 1.0%
80 Y M 32.4% L 31.5% F 19.0% I 5.9% Y 5.7% E 3.7%
82 V V 88.6% D 11.0%
88 Y N 91.1 K 6.6% W 1.3%
%
110 T D 99.9%
115 A A 35.6% Y 27.8% T 14.4% D 10.2% S 9.2% F 2.6%
118 E E 92.2% D 2.6% I 2.0% A 1.7%
125 F F 79.4% Y 11.8% M 7.3% L 1.5%
2 129 W E 91.3% S 8.6%
0
168 V D 98.1 A 1.0%
%
170 A A 78.7% S 17.6% D 3.7%
If we consider all the amino acids obtained from the PDA calculation,
including those with probabilities less
than 1 %, we obtain 4.1 x 10'5 differentamino acid sequences. This is a
reduction by 7 orders of magnitude.
2 5 If one only considers those amino acids that have at least a probability
of more than 1 % as shown in Table
1 (1 % criterion), the problem is decreased to 3.3 x 109 sequences. If one
neglects all amino acids with
a probability of less than 5% (5% criterion) there are only 4.0 x 106
sequences left. This is a number that
can be easily handled by screening and gene shuffling techniques. Increasing
the list of low energy
sequences to 100,000 does not change these numbers significantly and the
effect on the amino acids
3 0 obtained at each position is negligible. Changes occur only among the
amino acids with a probability of
less than 1 %.
Including the substrate in the PDA calculation further reduced the number of
amino acids found at each
position. Ifwe considerthose amino acids with a probabilityhigherthan 5%, we
obtain 2.4 x 106 sequences
(see Table 2).
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CA 02347214 2001-05-31
WO 00/23564 ~ PCT/US99/24229
Table robabilityno acidsthe designed positions resulting from
2: P of ami at the PDA calculation of the
enzyme bstrate . Only
su complex those
amino
acids
with
a probability
greater
than
1% are
shown.
WT PDA Probability Distribution
Y _ W 17.0% H 7.3% F 6.0%
Y 69.2%
~
5 7 Q Q 78.1 E 18.0% L 3.9%
%
11 D D 97.1%
37 V V 50.9% D 33.9% S 5.4% A 1.2% L 1.0%
39 G S 80.6% A 19.4%
63 N W 92.2% D 3.9% Q 2.9%
65 Y E 91.1 L 8.7%
%
67 T E 92.8% L 5.2%
71 W W 62.6% E 13.3% M 11.0% S 6.9% D 4.0%
80 Y M 66.4% F 13.6% E 10.7% I 6.0% L 1.3%
82 V V 86.0% D 12.8%
88 Y W 55.1% Y 15.9% N 11.4% F 9.5% K 1.9% Q 1.4% D 1.4%
M 1.4%
110 T D 99.9%
115A D46.1% .527.8% T17.1% A 7.9%
118 E I 47.6% D 43.0% E 3.6% V 2.5% A 1.4%
125 F Y 51.1 F 43.3% L 3.4% M 2.0%
%
2 129 W L 63.2% M 28.1 E 7.5%
0 %
168 V D 98.2%
170 A T 92.3% A 5.9%
These iminary
prel calculations
show
that
PDA can
significantly
reduce
the dimensionality
of the
problem
and can
bring
it into
the scope
of gene
shuffling
and screening
techniques
(see
Figure
3).
48
