Note: Descriptions are shown in the official language in which they were submitted.
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METHOD FOR COMBINATORIAL MATERIALS DEVELOPMENT
FIELD OF THE INVENTION
The present invention is in the field of biological and chemical synthesis and
processing.
The present invention relates to methods for developing materials featuring
the combination of
more than one components. The present invention may be applied in the field
of, but is not
limited to, the field of chemical or biological synthesis, diagnostics and
therapeutics.
The present application claims priority to U.S. Provisional Application Serial
No. 60/116,955 filed January 25, 1999.
BACKGROUND OF THE INVENTION
There are many situations where it is desirable to generate a large number of
compounds
to test for efficacy. Such compounds may be composed of one or more combined
materials, e.g.,
nucleotides, amino acids, chemical moieties, etc. Time constraints are a
tremendous obstacle to
such an endeavor. Examples of such situations include the generation of
polypeptides as drug
candidates, the generation of small molecules as drug candidates, and the
generation of electrode
materials for use in batteries. For example, in the process for discovery of
drug candidates, it is
normal to produce sets of different polypeptides and then determine how well
these polypeptides
bind to a receptor of interest. Once a polypeptide is found from among the
test set or sets that
binds well to a receptor, such polypeptide is pursued as a drug candidate or a
drug-precursor
candidate.
The number of polypeptides that may be made is enormous thereby making it
problematic to test them all, even if the testing is done in a massively
parallel way. For instance,
if 10-mer polypeptide sequences are made from a set of 20 amino acids, there
are 20'° (i.e., about
10'3) different peptide chains that can be made. Even if a million
polypeptides are tested at a
time, and each set of a million takes only a minute to test, the total time to
test all the
polypeptides for desired binding is about 19 years at 24 hours per day, 365
days per year.
Hence, it can take an inordinate amount of time to search every possible
polypeptide. It
is an object of the present invention to expedite this process. Optimizing can
help focus on a
region of interest thereby necessitating testing only a small subset of all
possible polypeptides.
Similar principles of optimizing may be applied to other combinatorial
materials thereby
providing an efficient and economical means for developing materials
comprising one or more
parts in combination.
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SUMMARY OF THE INVENTION
The present invention provides methods for developing combinatorial materials
comprising the steps of:
1. Creating a set of initial points;
2. Testing the set of points according to a determined definition of fitness;
3. Choosing a subset of the points based on the selection criteria;
4. Perturbing points in the subset until a new larger set is generated that
satisfies
any determined constraints; and
5. Repeating steps 2 and forward.using this new larger set.
DETAILED DESCRIPTION OF THE INVENTION
The present invention provides methods for combinatorial materials
development. The
methods are applicable generally in situations where it is practical to
generate only a small
portion of the total available materials for testing of efficacy. The methods
of according to the
present invention comprise the steps of:
1. Creating a set of initial points;
2. Testing the set of points according to a determined definition of fitness;
3. Choosing a subset of the points based on the selection criteria;
4. Perturbing points in the subset until a new larger set of points is
generated that
satisfies any determined constraints; and
S. Repeating steps 2 and forward using this new larger set.
In preferred embodiments, it is useful to employ the concept of scale of the
perturbation
as described in the definitions listed below. In some instances, it is
possible that the initial set is
chosen without clear insight of the best points. Thus, it is often useful to
begin with a widely
dispersed set of points in the space. As the method according to the present
invention proceeds, it
selects regions of the space having better fitness than other regions. If the
scale of perturbation is
then reduced, perturbation results in points closer to each other. For
example, if the scale of
perturbation is reduced as the process proceeds, the process narrows in on
regions of interest
causing the process to explore these regions in a more detailed manner,
eliminating larger
perturbations that would create points outside the regions of interest that
would then be rejected
in the next ranking of points based on fitness. It is also possible that, if
the process enters in a
region that is not particularly good according to its measure of fitness, the
scale of perturbation
can be increased in order to jar the process out of this less-favorable
region. For such reasons, it
is sometimes useful to adjust the scale of the perturbation as the method
according to the present
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invention proceeds. Hence, a preferred method according the present invention
comprises the
steps of:
Creating a set of initial points;
2. Choosing a scale of perturbation;
Testing the set of points according to a determined definition of fitness;
4. Choosing a subset of the points based on the selection criteria;
5. Perturbing the points in the subset thereby generating a new larger set of
points
satisfying determined criteria; and
6. Repeating steps 2 and forward until an acceptable set of points is found.
The methods according to the present invention are especially applicable in
situations
where it is desirable to prepare a set of polypeptides and test for binding of
such polypeptides.
For instance, according to the methods of the present invention, it is
possible to test a first set of
polypeptides for binding affinity to a receptor. Of this first set tested, it
is possible to screen those
polypeptides having the highest binding affinity. Next, it is possible to
perturb these highest-
affinity polypeptides thereby creating a second test set of polypeptides.
Next, it is possible to
screen the second set of polypeptides, perturb the resulting highest-affinity
polypeptides to create
a third test set of polypeptides until one or more polypeptides having a
binding affinity that is
equal to or better than desired is located. Following such a method may
provide polypeptides
having optimal binding affinities in a rapid and efficient manner.
The methods according to the present invention are also applicable in
situations where it
is desirable to obtain small molecules having a desired effect or property.
The total number of all
possible small molecules is enormous, and in most instances it is not feasible
to test all possible
small molecules for efficacy as a drug candidate. According to the methods of
the present
invention, it is possible to provide a first set of small molecules, test the
efficacy of the first set of
small molecules according to a defined criteria. Next, according to the
methods of the present
invention it is possible to generate a second test set of small molecules by
perturbing a portion of
the first set of small molecules demonstrating optimal efficacy according to
the first defined
criteria.
The methods according to the present invention are also applicable in
situations where it
is desirable to optimize electrode-materials, e.g. compounds and alloys. Here
examples of
perturbation include changing the ratio of compounds in an alloy, substituting
one or more new
compounds, removing a compound, layering compounds, etc.
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In another aspect, the present invention features a programmed computer system
for
implementing a method for developing combinatorial materials. Such a
programmed computer
system may comprise any one of a large number of possible software programs
that may be
designed by those skilled in the art. Such a programmed computer system
comprises a means for:
(1) determining a set of initial points; (2) optionally choosing a scale of
perturbation; (3) testing
the set of points according to a determined definition of fitness or receiving
from some other
equipment or entered by hand a list of fitness values associated with the
points; (4) choosing a
subset of the points based on the selection criteria; (5) perturbing the
points in the subset thereby
generating a new larger set of points satisfying determined criteria; and (6)
repeating the whole
process from steps (2) on until an acceptable set of points is found.
As used herein, the following terms are understood to mean the following:
A "space of interest" is a set of all possible particular samples that may be
examined or
tested according to one or more criteria. For example, in the situation where
it is desirable to
isolate polypeptides having a particular property, the space of interest is
all polypeptides that can
be made. In the situation where it is desirable to isolate battery electrode
alloys having a
particular property, the space of interest is all alloys that can be made. In
the situation where f
one is interested in a parameter as represented by a real number between 0 and
10, the space of
interest is all real numbers between 0 and 10.
A "point" in a space of interest is a particular sample in the space of
interest. For
example, one particular polypeptide is a point in the space of interest
comprising all polypeptides.
One particular alloy is a point in the space of interest comprising all
alloys. A particular number,
for example 6, is a point in the space of interest comprising all numbers from
0 to 10.
"Perturbation" is the process of changing one point from a space of interest
thereby
creating a new point in the space of interest. For example, it is possible to
perturb the number
6.18 by adding 0.01 thereby creating 6.19, a new point in the space of
interest comprising real
numbers from 0 to 10. A DNA strand defined by GATTACA may be perturbed by
changing its
third position from a T to an A thereby creating a new DNA strand defined by
GAATACA.
Likewise, a DNA strand may be perturbed by adding additional nucleotides or
removing
nucleotides thereby producing different DNA sequences. Perturbing of an alloy
may feature
increasing the fraction of one metal or adding a new metal into the mix.
Similarly, perturbing a
polypeptide may feature adding additional amino acids to the sequence,
removing particular
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amino acids from the sequence or substituting another amino acid for one or
more amino acids in
the sequence.
Perturbation may be limited by "constraints" on the space of interest. For
example,
polypeptides of 10 amino acids or less in length may be of interest. In such a
situation, it is not
desirable to explore the space outside these constraints. Hence, it is not
desirable to choose any
perturbations that create a point outside the constraints (such as a
polypeptide of length 11). An
exemplary constraint on alloys might be that the alloy should not contain both
aluminum and zinc
at the same time. An exemplary constraint on DNA space might be that the DNA
strands are
longer than 10 nucleotides and shorter than 50 nucleotide bases.
The space of interest may have a "metric" or "distance function" such that any
two points
in the space have a corresponding scalar that is used to provide a distance
between the two points.
For example, two points on a road map have a distance between them - the
"metric" can be a
measure of distance in meters, for example. For polypeptides, the metric may
be the minimum
number of peptide positions that do not match up.. For alloys, the metric may
be measured by
sumk(ak- bk)2, where sumk represents a sum over k, k is an index that runs
over all metals present
in the alloys, ak is the fraction of metal k present in the first alloy and bk
is the fraction of metal k
present in the second alloy. For a set of real numbers, a metric might be (nl -
n2) 2, where nl is
the first number and n2 is the second number.
In situations where the space has a metric, the perturbation may have a
"scale" such that
if a perturbation with one scale results in a new point in the space a
distance D from the original
point (as measured by the metric), a perturbation of the same point with a
larger scale on average
results in a new point in the space a distance greater than D from the
original point. A particular
perturbation may or may not be substantially random.
"Fitness" generally means how good or bad a particular point is in accordance
with the
defined criteria. For example, regarding a polypeptide, "fitness" may describe
how strongly the
polypeptide binds its target. In the case of real numbers, "fitness" may
describe how close a
particular function of that number is to a target value. In the case of metal
alloys, "fitness" may
describe how well the current/voltage curve of the alloy matches some desired
current/voltage
curve or how well it scores on a rating of its current/voltage curve.
"Selection criteria" are used to select a subset of points based on how they
are ranked by
fitness. Exemplary "selection criteria" include selecting N best points as
ranked by fitness.
Further exemplary "selection criteria" may include eliminating the best M
points and choosing
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then next N best ones. Exemplary "selection criteria" may include picking
randomly but with a
higher probability of picking optimal points. For example, where y = (f -
fr";") / (fmaX - f",;"), where
f",;" is the minimum fitness number, fmaX is the maximum fitness number, and f
is the fitness
number of the point being considered, a number between 0 and 1 may be
generated randomly and
determined whether to be lower than y. If so, that point may be chosen. If
not, the next point for
selection is considered. This process, for example, may be continued until N
points are chosen
from the original set.
DESCRIPTION OF TAE PREFERRED EMBODIMENTS
The following are provided purely by way of example and are not intended to
limit the
scope of the present invention.
EXAMPLE 1
Arrays that synthesize chemicals including arrays that synthesize DNA are
being
developed to synthesize and process information. Chemical synthesis and
processing on arrays of
electrodes depends upon various parameters such as buffer concentration,
percent acetonitrile in
the buffer, length of time for deblocking chemistry, and the total voltage-on
time to use for a
square-wave voltage signal during deblocking.
Where genetic material such as DNA is being synthesized, the goal of the
synthesis is to
produce high quality DNA. Determining how to optimize the various parameters
to produce
optimal DNA synthesis may require much effort. The methods according to the
present invention
were employed to determine optimal conditions.
Our space here is the possible set of parameters -- each one being a real
number between
0 and infinity. A point in the space is a particular set of parameters, such
as setting the buffer
concentration to 0.0083 molar, setting the ACN percentage at 25%, running the
deblocking for
827 seconds, and setting voltage-on time to 223 seconds. This point can be
represented by
(In(0.0083), 25, 827, 223). We chose to use the natural log of the
concentration as that better
represents that we are evaluating an order of magnitude on the number.
We chose to perturb points in this space by, for each parameter, selecting a
number at
random from a gaussian distribution centered at zero and with a standard
deviation equal to half a
rough estimate of the maximum useful value minus the minimum useful value. For
deblock,
sigma was 500 seconds; for concentration, sigma was 1.5; for percentage, sigma
was S0; for
voltage on, sigma was 500 seconds.
The constraints on perturbation were that we didn't want values less than one
number and
larger than another. We did not accept concentration numbers less than 0 or
greater than 1 molar;
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ACN percentage less than 0 or greater than 100; deblock time less than 30
seconds or greater than
3000 seconds; and voltage-on time less than one quarter the deblock time or
greater than the
deblock time.
The metric picked was the euclidean distance between points, i.e., sqrt((vl-
wl)2 + (v2-
w2)2 + ... + (vN-wN)2), where vk and wk are the kth component of the vector.
Thus the distance
between (ln(0.0083), 25, 827, 223) and (ln(0.1), 50, 400, 120) is sqrt((-
4.791+2.303)2 + (25-50)2
+ (827-400)2 + (223-120)2).
The scale of perturbation was defined by the sigmas of the gaussians used in
perturbing
and just remained fixed throughout the process.
The fitness was determined by the quality and brightness of florescent spots
generated by
the oligonucleotides built with florescent tags in the bases. Humans were the
judges of the
quality.
The selection criterion was to take the best point (as ranked by fitness) and
to keep
perturbing it until we found a better point. Then we keep perturbing that
until we found a better
point, and so on.
This resulted rather quickly in fording parameters that worked acceptably well
according
to our fitness criterion without having to test an enormous grid of possible
parameter settings and
including the variability of human judgement when coming up with fitness of
points.
EXAMPLE 2
Polypeptide Drug Candidates
The space of interest is polypeptides composed of the 20 natural amino acids
and from
five to ten amino acids in length. This comprises the specification for the
space and constraints.
These polypeptides may be constructed electrochemically on an electrochemical
array chip;
chemically on beads, pins, or substrates; made in microfluidics chambers; etc.
The perturbation in this instance may include the following basic operations:
(1) addition
of an amino acid (adding one at the front of the polypeptide, inserting one
somewhere in the
middle, or adding one to the end), (2) the deletion of an amino acid, or (3)
changing an amino
acid.
The metric is how many of these basic operations are applied to get from one
polypeptide
to another. The scale of perturbation can likewise be how many basic
operations were used in the
perturbation.
Fitness is chosen to be the binding coefficient determined as follows. For the
reaction A
+ B <-> AB, where A is the receptor of interest, B is the polypeptide
candidate, and AB is the
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polypeptide bound to the receptor, a binding coefficient is defined as K =
[AB]/([A] [B]), where
[x] is the concentration of x. A spot containing polypeptide B may be exposed
to a solution
containing receptor A, which is florescently labeled, thereby providing an
indication of how
strongly A binds to B by the brightness of the resulting spot. So, [AB] can be
estimated as being
proportional to the intensity of the spot; [B] may be varied by how much B is
built at a spot; and
[A] may be kept constant. Then, assuming K is a constant, it is possible to
estimate its size for
one polypeptide compared to another by looking at K = d([AB])/d[B] x (1/[A]),
such as by
plotting [AB] vs. [B], taking the slope and dividing by [A]. We can do this by
building a dilution
series of B on a synthesis array, for example, building several spots of the
same polypeptide B at
different concentrations on the surface. This dilution-series method helps
avoid false positives
and can allow picking a fitness function such that one is looking for a
particular size of K --
neither too high nor too low -- a fitness function equal to (K - Ktarget)2.
There are other fitness
functions that may be chosen. It is possible to choose how well the
polypeptide binds to the
receptor of interest as judged optically by having the receptor florescently
labeled and examining
which spots on an array of polypeptides light up the strongest after applying
a solution containing
the receptor to the polypeptide array. It is possible to avoid false positives
and false negatives to
some extent by building the same polypeptide at several different sites and
eliminating the darkest
and the brightest then taking the mean of the rest to come up with a fitness
number.
Set the selection criterion to be that one takes the best 10% of the
polypeptides generated.
Choose to keep the selected points in the new set, filling out the set with
perturbed
versions where the perturbation is chosen at random from the set of the three
basic operations but
applying only one or two basic operations to generate a perturbation thereby
limiting the scale of
the perturbation.
Thus, an initial set of polypeptides would be generated, perhaps as perturbed
versions of
some currently known polypeptide of some functionality or perhaps creating a
set at random from
the space of interest. It is possible to create four spots on an array for
each of the polypeptides
being tested. Then it is possible to apply the labeled receptor, measure the
brightness of the spots,
eliminate the high and low for each spot, and take the mean of the rest
thereby providing fitness
numbers for each polypeptide. It is then possible to take the best 10%,
perturb to fill out a new
array, test, and so on.
EXAMPLE 3
Small-Molecule Drug Candidates
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The space of interest is small molecules. Such small molecules may be
generated by an
electrochemical array; synthesized on beads, pins, or some other substrate; or
synthesized in
microfluidics chambers; etc.
Perturbation may be one of the following basic operations: adding a chemical
compound
to the molecule's scaffold, deleting a chemical compound from the molecule's
scaffold, changing
a molecule on the scaffold, adding another scaffold to the existing scaffold,
changing the chirality
of the scaffold, creating a loop from one terminus of the scaffold to another,
or inserting a metal
into the scaffold.
The metric may be how many of these.basic operations are applied to get from
one small
molecule to another. Another possible metric is a similarity measure that
measures molecular
diversity.
The scale of perturbation may be how many basic operations were used in the
perturbation.
Fitness may be how well the small molecule binds to the protein of interest as
judged
optically by having the protein florescently labeled and determining which
spots on an array of
small molecules light up the strongest after applying a solution containing
the protein to the small
molecule array. One may avoid false positives and false negatives by building
the same small
molecules at several different sites and removing the darkest and the
brightest then taking the
mean of the rest to come up with a fitness number. One could also have chosen
to use the
binding coefficient fitness function (based on a dilution series) as described
in the "polypeptide
drug candidates" example.
The selection criterion is the best 10% of the small-molecules generated.
The selected points are maintained in the new set, filling out the set with
perturbed
versions where the perturbation is chosen at random from the set of the basic
operations but
applying only three or less basic operations to generate a perturbation
thereby limiting the scale
of the perturbation.
It is possible to generate an initial set of small molecules, perhaps as
perturbed versions
of some currently known small molecule of some functionality or perhaps
creating a set at
random from the space of interest. It is possible to create four spots on an
array for each of the
small molecules being tested. Then it is possible to apply the labeled
protein, measure the
brightness of the spots, throw out the high and low for each spot, and take
the mean of the rest.
This generates fitness numbers for each small molecule. It is then possible to
takes the best 10%,
perturbs to fill out a new array, tests, and so on.
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EXAMPLE 4
Chelating Material
Molecules that form chelates with metal ions are often characterized by their
absolute and
relative affinity for different metal ions. Some uses require chelating
molecules that will bind to
all metal ions in a non-specific manner. Other uses require a chelating
molecule that has a very
specific affinity for a particular metal ion and not for other metal ions.
Chelating molecules can be formed from many different organic, inorganic and
mixed
organic/inorganic motifs. For example, chelating molecules can be formed from
polyethers, from
pyridine oligomers, or from small peptides. Other materials include cyclic
molecules such as
ethylene diamine tetra acetate (EDTA) and porphyrin moieties.
Numerous methods can be used to create combinatorial libraries of chelator
materials.
For example, in instances where the chelator molecule is a small peptide, the
peptide may be
synthesized on beads, pin arrays, using ink jet deposition, or on electrode
arrays. Similar
methods may, for example, be used to modify a porphyrin scaffold molecules to
create
combinatorial libraries.
The fitness of a chelator molecule can be determined by evaluating its binding
selectivity
and affinity. This can be accomplished in a variety of ways including testing
sample solutions
that have been exposed to the chelator molecule for unbound metal ions. For
example, beads
from a combinatorial chelator library may be added to individual wells in a
microtiter plate and
exposed to a solution containing the metal ion of interest as well as other
interfering ions. The
solutions in each individual well may then be tested for the metal ion or ions
of interest using, for
example, a colorometric assay. As another example, small peptides made on an
electrode array
may be exposed to a solution containing one or more metal ions of interest.
The quantity and
identity of the metal ions that are incorporated into each peptide chelator
molecule may be
determined electrochemically. Further, binding affinity coefficients may be
determined on an
electrode array by constructing a dilution series in the candidate peptide
chelator molecules by
making different amounts at different electrodes in the electrode array. The
relative quantity of
metal ion or ions sequestered by the candidate chelator may be evaluated
electrochemically at
each point in the dilution series, which produces a dilution series curve.
After evaluating chelator molecule candidates for selectivity and binding
affinity, the top
10% of the candidate materials from a combinatorial chelator molecule library
are selected for
optimization using perturbation by the methods described previously.
Perturbations may be
introduced by many methods. For example, different amino acids may be inserted
into a peptide
chelator sequence or new molecular motifs may be added to a porphyin scaffold
moiety. Also,
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perturbations may be introduced into the composition of the solution
containing the metal ion or
ions to which the chelator candidate molecules is exposed. Such perturbations
include changes in
the solvent composition, spectator electrolytes, interfering ions, pH and
other such changes as
will be evident to one skilled in the art. 'These perturbations can help
optimize the operating
parameters for applications that use the candidate chelator molecules.
EXAMPLE 5
Electrodeposition
Deposition of material by electrochemical methods depends on numerous
experimental
variables. These variables reflect the complex interplay between such factors
as the composition
of a plating solution, the electrical parameters and the substrate material or
materials. There are
also many different desired results from the process of electrodepostion. A
metal like platinum
may be electrodeposited on a decorative item for aesthetic reasons or may be
electrodeposited to
form a catalyst.
The search space for optimizing the conditions for electrochemical deposition
includes
such diverse variables as the composition of electrolytes or additives in a
plating solution, the
schedule on which electrical variables are applied to the conductive substrate
onto which
materials are electrodeposited and the nature of the deposition substrate.
Combinatorial
exploration of this electrodeposition variable space can be accomplished by
many means
including electrode arrays, mechanical deposition on a conductive substrate
using ink jet or pen
nib spotting techniques, photolithographic masking, the use of chemical redox
agents among
others.
The test metrics of merit depend on the ultimate use of the electrodeposited
material. For
example, a catalyst material may be evaluated based on its longevity or its
catalytic activity, a
decorative coating may be evaluated on the basis of its smoothness and luster,
a superconducting
material on the basis of its superconducting transition temperature.
The top 10% of the candidate electrodeposition protocols from a combinatorial
protocol
library may be selected for optimization using perturbation by the methods
described previously.
Perturbations may be introduced by many methods. For example, the voltage
applied for
electrodeposition may be changed and the length of time that the voltage is
applied may be
varied. The current or voltage as a function of time may take any form, and
these functions may
be perturbed in a wide variety of ways. A sinusoidal current signal, for
example, may be
perturbed by changing its frequency, by adding in additional fourier
components, or by
introducing or perturbing an amplitude envelop among others. The composition
of the solution
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from which materials are electroplated may by varied. For examples, the
electrolyte composition
and concentration may be changed, surfactant additives may be removed or
added, the solution
may have two or more immisible phases among others.
EXAMPLE 6
Fuel Cell Catalyst
Catalysts for fuel cells are often made of one or more metals. One of these
metals is
usually a transition metal, such as platinum, rhodium, or ruthenium. There are
many different
important variables that determine fuel cell catalyst performance. These
include alloy
composition, surface morphology, alloy phase segregation, crystallinity,
doping inclusions and
the like. Alloy composition, morphology and other factors derive from a
complex interplay
between the materials that are deposited in a combinatorial manner and the
methods used for their
deposition.
Numerous methods may be used to create combinatorial libraries of fuel cell
catalyst
materials. These methods include physical deposition from ink jet print heads,
mechanical
spotting onto substrates using pin-nib spotting, electrodepostition onto
electrode arrays, and
combinations of these different methods. After deposition, combinatorial fuel
cell catalyst
candidate materials can be further processed by many methods. These post-
processing methods
include thermal annealing, electrochemical conditioning, doping and
combinations of these
methods.
The fitness of a fuel cell catalyst may be tested by many methods. For
example, the
current density that can be achieved at a given voltage in a test solution is
one test. Another test
is the longevity of this current density under electrochemical load cycling
and the susceptibility of
the materials to poisoning from contaminants.
The top 10% of the candidate materials from a combinatorial fuel cell library
that are
tested may be selected for optimization using perturbation by the methods
described previously.
Perturbations may be introduced by many methods. For example, varying the
ratios of alloys in
the deposition solutions may yield different alloy compositions, varying the
current-voltage
characteristics of the electrochemical deposition protocol may result in a
host of perturbations
ranging from alloy composition to surface morphology, and varying thermal
annealing protocols
may result in perturbations of morphology, crystallinity and doping levels
among other effects.
EXAMPLE 7
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Zinc finger protein DNA binding
The zinc finger motif is an important functional element of many proteins that
bind to
DNA sequences. These motifs are found in numerous transcription factors and
steroid receptor
complexes. The primary DNA sequence that these proteins bind to is not always
known. Any
secondary DNA sequences that these proteins may bind to often is not known.
For example, it is
often desirable to determine the effects of mutations on a proteins sequence
selectivity and
binding affinity.
Combinatorial libraries of DNA can be used to determine the binding affinity
and
selectivity for proteins that carry a zinc finger motif. These libraries can
be created by many
means. Such means include electrode arrays, photolithographic patterning,
mechanical
deposition by ink jet or pen-nib spotting among others.
The affinity and selectivity of one or more proteins carrying a zinc finger
motif for a
particular sequence of DNA may be determined by many methods. For example, an
array of
electrodes that have different DNA sequences over different electrodes may be
used to detect, to
monitor and to quantify any proteins labeled with an electrochemically active
tag that bind to the
DNA sequence at any particular electrode. As a further example, fluorescence
may be used in an
analogous manner to detect, to monitor and to quantify any proteins labeled
with a fluorescent tag
that bind to DNA sequences in arrays of DNA sequences that have been prepared
by
photolithographic methods. Binding affinity may be evaluated in a facile
manner by determining
the relative amount of protein that binds to different locations in an array
that have different
amounts of the same DNA sequence. It may also be desirable to, for example, to
determine that
effect of a particular DNA sequence on the binding affinity and selectivity of
a receptor cofactor
such as a steroid hormone.
The top 10% of the candidate sequences from a combinatorial DNA sequence may
be
selected for optimization using perturbation by the methods described
previously. Perturbations
can be introduced by many methods. For example, the DNA sequence may be varied
by addition,
subtraction or substitution of a different nucleic acid into the DNA sequence.
EXAMPLE 8
Efficient phosphor materials
Materials that have phosphorescent properties are often composed of one or
more
inorganic elements and often are sensitive to trace levels of doped
inactivator complexes. These
materials have many uses such as flat panel plasma displays. The efficiency,
lifetime and
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CA 02361151 2001-07-25
WO 00/43411 PCT/US00/01998
stability of the color output of a phosphor material determine its utility for
commercial
applications.
Numerous methods may be used to create combinatorial libraries of low voltage
phosphor materials. These methods include precipitation from solutions,
physical deposition
from ink jet print heads or pen-nib spotting, electrodeposition onto electrode
arrays and
combinations of these and other methods. After deposition, the materials may
be processed in
many different ways including thermal and electrical processing, doping and
combinations of
these methods.
The fitness of a particular material as a .low voltage phosphor may be
evaluated by
numerous methods. For example, the intrinsic phosphorescence of an array of
materials may be
evaluated by illuminating the array with IJV light and measuring the
luminescent output from
each of the materials. Another method for testing low voltage phosphors
involves placing an
array of candidate materials in a chamber filled with an inert gas such as
Xenon, exciting the gas
with a voltage source and measuring the luminescent output from each of the
materials in the
array.
The top 10% of the candidate materials from a combinatorial phosphor library
may be
selected for optimization using perturbation by the methods described
previously. Perturbations
can be introduced by many methods. For example, varying the ratios of elements
and dopants in
the deposition solutions can yield different alloy compositions. Other
perturbations include
varying the current-voltage characteristics of an electrochemical deposition
protocol, varying the
thermal annealing schedule and varying the type and amount of doping
introduced after the initial
deposition.
Although the invention has been described with reference to the presently
preferred
embodiments, it should be understood that various modifications can be made
without departing
from the spirit of the invention. Accordingly, the invention is limited only
by the following
claims.
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