METHOD OF GENERATING AN OPTIMIZED, DIVERSE POPULATION OF VARIANTS
First Claim
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1. A method for generating a maximally diverse population of molecular variants, the method comprising the steps of:
- (a) inputting a desired set of mutations;
(b) setting optimization parameters, wherein the optimization parameters comprise;
(i) number nvar of molecular variants to create;
(ii) molecular population size popSize;
(iii) a crossover rate;
(iv) a mutation rate;
(v) repair operator;
(vi) a primary fitness function; and
(vii) a penalty fitness function;
(c) generating a plurality of random genomes of population size popSize;
(d) creating a first generation of genomes of the size nvar by applying a selection operator;
a crossover operator;
a mutation operator;
a repair operator;
a primary fitness function operator; and
penalty function operator on the plurality of random genomes.
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Abstract
The disclosure relates to a method of generating a diverse set of variants to screen improved and novel properties within the variant population, a system for creating the diverse set of variants, and the variant peptides.
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Citations
60 Claims
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1. A method for generating a maximally diverse population of molecular variants, the method comprising the steps of:
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(a) inputting a desired set of mutations; (b) setting optimization parameters, wherein the optimization parameters comprise; (i) number nvar of molecular variants to create; (ii) molecular population size popSize; (iii) a crossover rate; (iv) a mutation rate; (v) repair operator; (vi) a primary fitness function; and (vii) a penalty fitness function; (c) generating a plurality of random genomes of population size popSize; (d) creating a first generation of genomes of the size nvar by applying a selection operator;
a crossover operator;
a mutation operator;
a repair operator;
a primary fitness function operator; and
penalty function operator on the plurality of random genomes. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 41)
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9. A method for generating a maximally diverse population of molecular variants, the method comprising the steps of:
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(a) inputting a desired set of mutations, wherein each mutation has associated with it a preferred frequency of appearance within the set of molecular variants and a weight; (b) setting optimization parameters, wherein the optimization parameters comprise; (i) number nvar of molecular variants to create; (ii) molecular variant population size popSize; (iii) crossover probability crossrate; (iv) mutation rate mutrate; (v) repair operator parameters;
the minimum, maximum and desired number of mutations per molecular variant;(vi) number of generations to evolve nGen; (vii) setting the primary fitness function; and (viii) setting penalty fitness functions; (c) generating a random plurality of sets of molecular variants of the population size popSize; and (d) evolving random pluralities of sets of molecular variants of the size nvar for nGen generations by applying a selection operator, a crossover operator, a mutation operator, a repair operator, a primary fitness operator, and penalty function operator, wherein new populations are created by repeating the steps of; (i) selecting sets of molecular variants for breeding based on the selection operator; (ii) breeding sets of molecular variants; (aa) mating the molecular variants using the crossover operator; (bb) mutagenizing progeny sets of molecular variants according to the mutation operator; - View Dependent Claims (10, 11, 12, 13, 14, 15, 16, 17, 18, 49)
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19. A computer program product comprising a machine readable medium having program instructions for generating a diverse, optimized set of molecular variants, the program instructions comprising:
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(a) code for receiving a data set of mutations; (b) code for setting optimization parameters, wherein the optimization parameters comprise; (i) number nvar of molecular variants to create; (ii) molecular variant population size popSize; (iii) crossover rate crossrate; (iv) mutation rate mutrate; (vi) primary fitness function; and (vii) penalty fitness function and corresponding penalty weight; (c) code for generating a random plurality of genomes of the population size popSize; and (d) code for creating a first optimized genomes from the random plurality of genome by applying a selection operator;
a crossover operator;
a mutation operator;
a primary fitness operator;
a penalty fitness operator; and
repair operator.
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20-22. -22. (canceled)
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23. A computer program product comprising a machine readable medium having program instructions for generating a diverse, optimized set of molecular variants, the program instructions comprising:
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(a) code for inputting a desired set of mutations, wherein each mutation has associated with it a preferred frequency of appearance within the set of molecular variants and a weight; (b) code for setting optimization parameters, wherein the optimization parameters comprise; (i) number nvar of molecular variants to create; (ii) molecular variant population size popSize; (iii) crossover probability crossrate; (iv) mutation rate mutrate; (v) repair operator parameters;
the minimum, maximum and desired number of mutations per molecular variant;(vi) number of generations to evolve nGen; (vii) setting the primary fitness function; and (viii) setting penalty fitness functions; (c) code for generating a random plurality of sets of molecular variants of the population size popSize; and (d) code for evolving random pluralities of sets of molecular variants of the size nvar for nGen generations by applying a selection operator, a crossover operator, a mutation operator, a repair operator, a primary fitness operator, and penalty function operator, wherein new populations are created by repeating the steps of; (i) selecting sets of molecular variants for breeding based on the selection operator; and (ii) breeding sets of molecular variants; (aa) mating the molecular variants using the crossover operator; and (bb) mutagenizing progeny sets of molecular variants according to the mutation operator.
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24-26. -26. (canceled)
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27. A system for generating an optimized diverse population of molecular variants, the system comprising:
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(1) at least one computer comprising a database capable of storing a data set representing a population of mutations; (2) system software comprising one or more logic instructions for; (a) setting optimization parameters, wherein the optimization parameters comprise (i) number nvar of molecular variants to create; (ii) molecular variant population size popSize; (iii) crossover rate crossrate; (iv) mutation rate mutrate; (v) repair operator; (vi) primary fitness function; and (vii) penalty fitness function; (b) selecting a random plurality of parental mutations to generate a random plurality of genomes of size popSize; and (c) creating a progeny genome of size nvar by applying to the random population a crossover operator;
mutation operator;
repair operator, primary fitness operator; and
penalty fitness operator, - View Dependent Claims (59)
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28-29. -29. (canceled)
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30. A system for generating an optimized diverse population of molecular variants, the system comprising:
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(a) at least one computer comprising a database capable of storing a data set of mutations, wherein each mutation has associated with it a preferred frequency of appearance within the set of molecular variants and a weight; (b) system software comprising one or more logic instructions for setting optimization parameters, wherein the optimization parameters comprise;
number nvar of molecular variants to create;(ii) molecular variant population size popSize; (iii) crossover probability crossrate; (iv) mutation rate mutrate; (v) repair operator parameters;
the minimum, maximum and desired number of mutations per molecular variant;(vi) number of generations to evolve nGen; (vii) setting the primary fitness function; and (viii) setting penalty fitness functions; (c) system software comprising one or more logic instructions for generating a random plurality of sets of molecular variants of the population size popSize; and (d) system software comprising one or more logic instructions for evolving new populations of sets of molecular variants of the size nvar for nGen generations by applying a selection operator, a crossover operator, a mutation operator, a repair operator, a primary fitness operator, and penalty function operator, wherein new populations are created by repeating the steps of; (i) selecting sets of molecular variants for breeding based on the selection operator; and (ii) breeding sets of molecular variants; (aa) mating the molecular variants using the crossover operator; and (bb) mutagenizing progeny sets of molecular variants according to the mutation operator. - View Dependent Claims (60)
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31-40. -40. (canceled)
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42-48. -48. (canceled)
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50-58. -58. (canceled)
Specification