Method of generating an optimized, diverse population of variants

Patent metadata

Field	Value
Publication number	US-10155943-B2
Application number	US-201414281421-A
Country	US
Kind code	B2
Filing date	May 19, 2014
Priority date	Feb 12, 2008
Publication date	Dec 18, 2018
Grant date	Dec 18, 2018

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.

First claim

What is claimed is:

1. A set of physical bio-molecules produced by a method comprising:
(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 diverse, optimized set of molecular variants 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, wherein operations (a)-(d) are performed by executing instructions on a computer system programmed to perform said operations, and wherein each molecular variant represents a bio-molecule sequence; and
(e) synthesizing or expressing, in vitro or in vivo, a set of physical bio-molecules, wherein each physical bio-molecule comprises or is encoded by a polymer having a bio-molecule sequence represented by a molecular variant of the diverse, optimized set of molecular variants.

2. The set of physical bio-molecules of claim 1 , wherein the primary fitness function is based on niche counting.

3. The set of physical bio-molecules of claim 1 , wherein the penalty fitness function is the average number of mutations per genome.

4. The set of physical bio-molecules of claim 1 , wherein the penalty fitness function is an occurrence of a defined mutation in a genome, and wherein the defined mutation is a mutation that increases thermal stability of polypeptide variants.

5. The set of physical bio-molecules of claim 1 , wherein the penalty fitness function is an occurrence of a defined mutation in a genome, and wherein the defined mutation is an evolutionarily invariant residue.

6. The set of physical bio-molecules of claim 1 , wherein the primary fitness function is based on D-optimality or A-optimality.

7. The set of physical bio-molecules of claim 1 , wherein the penalty fitness function is an occurrence of a defined mutation in a genome, and wherein the defined mutation is a mutation that increases substrate recognition.

8. The set of physical bio-molecules of claim 1 , wherein the bio-molecules comprises a molecule selected from the group consisting of: a polynucleotide, a polypeptide, a lipid, a carbohydrate, and any combinations thereof.

9. A set of physical bio-molecules produced by a 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 a 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) a primary fitness function; and
(viii) a penalty fitness function;

(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 a diverse, optimized set of molecular variants 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,

wherein operations (a)-(d) are performed by executing instructions on a computer system programmed to perform said operations, and wherein each molecular variant represents a bio-molecule sequence; and
(e) synthesizing or expressing, in vitro or in vivo, a set of physical bio-molecules, wherein each physical bio-molecule comprises or is encoded by a polymer having a bio-molecule sequence represented by a molecular variant of the diverse, optimized set of molecular variants.

10. The set of physical bio-molecules of claim 9 , wherein the primary fitness function is based on niche counting.

11. The set of physical bio-molecules of claim 9 , wherein the penalty fitness function is the average number of mutations per genome.

12. The set of physical bio-molecules of claim 9 , wherein the penalty fitness function is an occurrence of a defined mutation in a genome, and wherein the defined mutation is a mutation that increases thermal stability the molecular variants.

13. The set of physical bio-molecules of claim 9 , wherein the penalty fitness function is an occurrence of a defined mutation in a genome, and wherein the defined mutation is an evolutionarily invariant residue.

14. The set of physical bio-molecules of claim 9 , wherein the method further comprises testing the properties of the set of physical bio-molecules.

15. The set of physical bio-molecules of claim 9 , wherein the primary fitness function is based on D-optimality or A-optimality.

16. A method comprising:
(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 diverse, optimized set of molecular variants 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, wherein operations (a)-(d) are performed by executing instructions on a computer system programmed to perform said operations, and wherein each molecular variant represents a bio-molecule sequence; and
(e) synthesizing or expressing, in vitro or in vivo, a set of physical bio-molecules, wherein each physical bio-molecule comprises or is encoded by a polymer having a bio-molecule sequence represented by a molecular variant of the diverse, optimized set of molecular variants.

17. The set of physical bio-molecules of claim 16 , wherein the penalty fitness function is an occurrence of a defined mutation in a genome, and wherein the defined mutation is a mutation that increases substrate recognition.

18. The method of claim 16 , wherein the primary fitness function is based on niche counting.

19. The method of claim 16 , wherein the primary fitness function is based on D-optimality or A-optimality.

20. The method of claim 16 , wherein the penalty fitness function is the average number of mutations per genome.

21. The method of claim 16 , wherein the penalty fitness function is an occurrence of a defined mutation in a genome, and wherein the defined mutation is a mutation that increases thermal stability of polypeptide variants.

22. The

Assignees

• Codexis Inc

Inventors

• Fox Richard

Classifications

• G06F19/24

  Physics · mapped topic

• C12N15/1058

  Directional evolution of libraries, e.g. evolution of libraries is achieved by mutagenesis and screening or selection of mixed population of organisms · CPC title

• C40B50/02

  Chemistry & Metallurgy · mapped topic

• G06F19/707

  Physics · mapped topic

• C12N15/1089 Primary

  Design, preparation, screening or analysis of libraries using computer algorithms · CPC title