Method of generating an optimized, diverse population of variants
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.
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,
wherein operations (a)-(d) are performed by executing instructions on a computer system programmed to perform said operations.
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.
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:
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.
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.
19. A computer program product comprising a non-transitory computer-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. A computer program product comprising a non-transitory computer-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.
21. 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.
22. The system of claim 21 , wherein the system is a client-server system.
23. 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:
(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) 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.
24. The system of claim 23 , wherein the system is a client-server system.