METHOD OF GENERATING AN OPTIMIZED, DIVERSE POPULATION OF VARIANTS

• US 20110029468A1
• Filed: 02/12/2009
• Published: 02/03/2011
• Est. Priority Date: 02/12/2008
• Status: Active Grant

• Current Assignee: Codexis, Inc.
• Original Assignee: Codexis, Inc.
• Inventors: Fox, Richard
• Granted Patent: US 8,504,498 B2
• Time in Patent Office: Days
• US Class Current: 706/13
• 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

Abstract

The present disclosure relates to methods of rapidly and efficiently searching biologically-related data space to identify a population set maximally diverse and optimized for sets of desired properties. More specifically, the disclosure provides methods of identifying a diverse, evolutionary separated bio-molecules with desired properties from complex bio-molecule libraries. The disclosure additionally provides digital systems and software for performing these methods.

Claims

1. A method of selecting an optimized, diverse set of molecular variants from a plurality of molecular variants, the method comprising:

(a) selecting one or more objectives for optimization of a plurality of molecular variants, wherein each molecular variant in the plurality is described by two or more objective data;

(b) determining a first Pareto front membership for each of the plurality of molecule variants based on the objectives of optimization;

(c) setting optimization parameters, wherein the optimization parameters comprise;

(i) number nvar of molecular variants to create;

(ii) a molecular variant population size popSize;

(iii) a crossover rate crossrate;

(iv) a mutation rate mutrate;

(v) a fitness function comprising a penalty fitness function and an overall fitness function, wherein the penalty fitness function is based on niche counting and the overall fitness function is based on the location of the molecular variant in a descending Pareto front divided by the shared number of molecular variants within each front; and

(vi) specific molecular variants to be included in the nvar set of molecular variants; and

(d) identifying a search space of acceptable molecular variants;

(e) generating a random population of genomes from the search space of acceptable molecular variants using a selection operator; and

(f) selecting a first set of nvar molecular variants by applying a crossover operator, a mutation operator, a repair operator, and a fitness operator to the random population of genomes.

2. The method of claim 1 in which the molecular variant is a bioactive polypeptide.

3. The method of claim 2 in which the bioactive polypeptide binds a ligand.

4. The method of claim 2 in which the bioactive polypeptide comprises an enzyme.

5. The method of claim 1 in which the objective data for the enzyme is selected from one or more of stereoselectivity, stereospecificity, enzyme activity, thermal stability, inhibitor resistance, and substrate specificity.

6. The method of claim 5 in which the substrate specificity is for two or more structurally different substrates.

7. The method of claim 1 further comprising repeating step (f) on a combined population of the first nvar set of molecular variants and the molecular variant set popSize to generate a second nvar set of molecular variants.

8. The method of claim 7 further comprising reiterating step (f) to generate a plurality of selected nvar sets of molecular variants.

9. A method of selecting an optimized, diverse set of molecular variants from a plurality of molecular variants, the method comprising:

(a) selecting one or more objectives for optimization of a plurality of molecular variants, wherein each molecular variant in the plurality is described by two or more objective data;

(b) determining a first Pareto front membership for each of the plurality of molecule variants based on the objectives of optimization;

(c) setting optimization parameters, wherein the optimization parameters comprise;

(i) number nvar of molecular variants to create;

(ii) a molecular variant population size popSize;

(iii) a crossover rate crossrate;

(iv) number of generations to create nGen;

(v) a mutation rate mutrate;

(vi) a fitness function comprising a penalty fitness function and an overall fitness function, wherein the penalty fitness function is based on niche counting and the overall fitness function is based on the location of the molecular variant in a descending Pareto front divided by the shared number of molecular variants within each front; and

(vii) specific molecular variants to be included in the nvar set of molecular variants; and

(d) identifying a search space of acceptable molecular variants;

(e) generating a random population of genomes from the search space of acceptable molecular variants;

(f) selecting a first set of genomes of size popSize from the random population, wherein each genome consisting of nvar molecular variants is created by applying a selection operator, a crossover operator, a mutation operator, a repair operator, and a fitness operator to the random population of genomes; and

(g) returning the genome with the highest fitness as the final, optimized, diverse nvar set of molecular variants.

10. The method of claim 9 in which the molecular variant is a bioactive polypeptide.

11. The method of claim 10 in which the bioactive polypeptide binds a ligand.

12. The method of claim 10 in which the bioactive polypeptide comprises an enzyme.

13. The method of claim 9 in which the objective data for the enzyme is selected from one or more of stereoselectivity, stereospecificity, enzyme activity, thermal stability, inhibitor resistance, and substrate specificity.

14. The method of claim 13 in which the substrate specificity is for two or more structurally different substrates.

15. The method of claim 9 further comprising reiterating step (f) nGen times to select a new set of genomes of size popSize.

16. A computer program product comprising a machine readable medium having program instructions for selecting an optimized, diverse set of molecular variants from a plurality of molecular variants, the program instructions comprising:

(a) code for receiving an objective data set representing two or more properties of each molecular variant in a plurality of molecular variants;

(b) code for generating a first Pareto front membership of all molecular variants based on the one or more objectives for optimization;

(c) code for setting optimization parameters, wherein the optimization parameters comprise;

(i) number nvar of molecular variants to create;

(ii) a molecular variant population size popSize;

(iii) a crossover rate crossrate;

(iv) a mutation rate mutrate;

(v) a fitness function comprising a penalty fitness function and an overall fitness function, wherein the penalty fitness is based on niche count and the overall fitness function is based on location in a descending Pareto front divided by the shared number of molecular variants within each front; and

(vi) specific molecular variants to be included in the nvar of molecular variants; and

(d) code for identifying a search space of acceptable molecular variants;

(e) code for generating a random population of genomes from the search space of acceptable molecular variants by applying a selection operator; and

(f) code for applying a set of operators comprising a crossover operator, a mutation operator, a fitness operator, and a repair operator on the random plurality of genomes to select the nvar set of optimized, diverse molecular variants.

17. The computer program of claim 16 further comprising a code for repeating step (f) on a combined population of the first nvar set molecular variant and the molecular variant set popSize to generate a second nvar set of molecular variants.

18. A computer program product comprising a machine readable medium having program instructions for selecting an optimized, diverse set of molecular variants from a plurality of molecular variants, the program instructions comprising:

(a) code for receiving an objective data set representing two or more properties of each molecular variant in a plurality of molecular variants;

(b) code for generating a first Pareto front membership of all molecular variants based on the one or more objectives for optimization;

(c) code for setting optimization parameters, wherein the optimization parameters comprise;

(i) number nvar of molecular variants to create;

(ii) a molecular variant population size popSize;

(iii) a crossover rate crossrate;

(iv) number of generations to create nGen;

(v) a mutation rate mutrate;

(vi) a fitness function comprising a penalty fitness function and an overall fitness function, wherein the penalty fitness is based on niche count and the overall fitness function is based on the location of the molecular variant in a descending Pareto front divided by the shared number of molecular variants within each front; and

(vii) specific molecular variants to be included in the nvar of molecular variants; and

(d) code for identifying a search space of acceptable molecular variants;

(e) code for generating a random population of genomes from the search space of acceptable molecular variants;

(f) code for selecting a first set of genomes of size popSize from the random population, wherein each genome consisting of nvar molecular variants is created by applying a selection operator, a crossover operator, a mutation operator, a repair operator, and a fitness operator to the random population of genomes; and

(g) code for returning the genome with the highest fitness as the final, optimized, diverse nvar set of molecular variants.

19. The computer program of claim 18 further comprising a code for repeating step (f) nGen times to select a new population of genomes of size popSize.

20. A system for selecting an optimized, diverse set of molecular variants from a plurality of molecular variants, comprising:

(a) at least one computer comprising a database capable of storing an objective data set representing two or more properties of each molecular variant in a plurality of molecular variants; and

(b) system software comprising one or more logic instructions for;

receiving an objective data set representing two or more properties of each molecular variant;

(ii) generating a first Pareto front membership of all molecular variants based on the one or more objectives for optimization;

(iii) setting optimization parameters, wherein the optimization parameters comprise;

(a) number nvar of molecular variants to create;

(b) a molecular variant population size popSize;

(c) a crossover rate crossrate;

(d) a mutation rate mutrate;

(e) a fitness function comprising a penalty fitness function and an overall fitness function, wherein the penalty fitness is based on niche counting and the overall fitness function is based on the location of the molecular variant in a descending Pareto front divided by the shared number of molecular variants; and

(f) specific molecular variants to be included in the nvar set of molecular variants; and

(iv) identifying a search space of acceptable molecular variants;

(v) generating a random population of genomes from the search space of acceptable molecular variants by applying a selection operator; and

(vi) applying to the random population of molecular variants a set of operators comprising a selection operator, a crossover operator, a mutation operator, a fitness operator, and a repair operator to select a set nvar of optimized, diverse molecular variants.

21. The system of claim 20 further comprising logic instructions for repeating step (vi) on a combined population of the first nvar set of molecular variants and the molecular variant set popSize to generate a second nvar set of molecular variants.

30. The system of claim 20, wherein the system is a client-server system.

22. A system for selecting an optimized, diverse set of molecular variants from a plurality of molecular variants, comprising:

(a) at least one computer comprising a database capable of storing an objective data set representing two or more properties of each molecular variant in a plurality of molecular variants; and

(b) system software comprising one or more logic instructions for;

(i) receiving an objective data set representing two or more properties of each molecular variant;

(ii) generating a first Pareto front membership of all molecular variants based on the one or more objectives for optimization;

(iii) setting optimization parameters, wherein the optimization parameters comprise;

(a) number nvar of molecular variants to create;

(b) a molecular variant population size popSize;

(c) a crossover rate crossrate;

(d) number of generations to create nGen;

(e) a mutation rate mutrate;

(f) a fitness function comprising a penalty fitness function and an overall fitness function, wherein the penalty fitness is based on niche counting and the overall fitness function is based on the location of the molecular variant in a descending Pareto front divided by the shared number of molecular variants; and

(g) specific molecular variants to be included in the nvar set of molecular variants; and

(iv) identifying a search space of acceptable molecular variants;

(v) generating a random population of genomes from the search space of acceptable molecular variants;

(vi) selecting a first set of genomes of size popSize from the random population, wherein each genome consisting of nvar molecular variants is created by applying a selection operator, a crossover operator, a mutation operator, a repair operator, and a fitness operator to the random population of genomes; and

(vii) returning the genome with the highest fitness as the final, optimized, diverse nvar set of molecular variants.

23. The system of claim 22 further comprising logic instructions for repeating step (vi) nGen times to select a new population of genomes of size popSize.

31. The system of claim 22, wherein the system is a client-server system.

24-29. -29. (canceled)