METHOD OF GENERATING AN OPTIMIZED, DIVERSE POPULATION OF VARIANTS

• US 20110034342A1
• Filed: 02/12/2009
• Published: 02/10/2011
• Est. Priority Date: 02/12/2008
• Status: Active Grant

• Current Assignee: Codexis, Inc.
• Original Assignee: Codexis, Inc.
• Inventors: Fox, Richard
• Granted Patent: US 8,768,871 B2
• Time in Patent Office: Days
• US Class Current: 506/7
• CPC Class Codes: C12N 15/1058 Directional evolution of libraries, e.g. evolution of libraries is achieved by mutagenesis and screening or selection of mixed population of organisms
• CPC Class Codes: C12N 15/1089 Design, preparation, screening or analysis of libraries using computer algorithms
• CPC Class Codes: G06N 3/126 Evolutionary algorithms, e.g. genetic algorithms or genetic programming
• CPC Class Codes: G16B 35/00 ICT specially adapted for in silico combinatorial libraries of nucleic acids, proteins or peptides
• CPC Class Codes: G16B 35/20 Screening of libraries
• CPC Class Codes: G16B 40/00 ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• CPC Class Codes: G16C 20/60 In silico combinatorial chemistry
• CPC Class Codes: G16C 20/70 Machine learning, data mining or chemometrics

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.

Claims

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.

2. The method of claim 1 in which the primary fitness function is based on niche counting.

3. The method of claim 1 in which the primary fitness function is based on D-optimality or A-optimality.

4. The method of claim 1 in which the penalty fitness function is the average number of mutations per genome.

5. The method of claim 1 in which the penalty fitness function is the occurrence of a defined mutation in the genome.

6. The method of claim 5 in which the defined mutation is a mutation that increases thermal stability of polypeptide variants.

7. The method of claim 5 in which the defined mutation is a mutation that increases substrate recognition.

8. The method of claim 5 in which the defined mutation is an evolutionarily invariant residue.

41. A diverse, optimized set of molecular variants, created by the method of claim 1.

9. A method for generating a maximally diverse population of molecular variants, the method comprising the steps of:

(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;

10. The method of claim 9 in which the primary fitness function is based on niche counting.

11. The method of claim 9 in which the primary fitness function is based on D-optimality or A-optimality.

12. The method of claim 9 in which the penalty fitness function is the average number of mutations per genome.

13. The method of claim 9 in which the penalty fitness function is the occurrence of a defined mutation in the genome.

14. The method of claim 13 in which the defined mutation is a mutation that increases thermal stability the molecular variants.

15. The method of claim 13 in which the defined mutation is a mutation that increases substrate recognition.

16. The method of claim 13 in which the defined mutation is an evolutionarily invariant residue.

17. The method of claim 9 further comprising the step of synthesizing the final set of molecular variants.

18. The method of, claim 9 further comprising the step of testing the properties of the final set of molecular variants.

49. A diverse, optimized set of molecular variants, created by the method of claim 9.

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:

(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.

20-22. -22. (canceled)

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:

(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.

24-26. -26. (canceled)

27. A system for generating an optimized diverse population of molecular variants, the system comprising:

(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,

59. The system of claim 27, wherein the system is a client-server system.

28-29. -29. (canceled)

30. A system for generating an optimized diverse population of molecular variants, the system comprising:

(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.

60. The system of claim 30, wherein the system is a client-server system.

31-40. -40. (canceled)

42-48. -48. (canceled)

50-58. -58. (canceled)