"""Design API providing the interface to design novel proteins based on a your design criteria."""
from openprotein.base import APISession
from openprotein.data import AssayDataset, DataAPI
from openprotein.jobs import JobsAPI
from . import api
from .future import DesignFuture
from .schemas import Criteria, Criterion, DesignConstraint, Subcriterion
[docs]
class DesignAPI:
"""Design API providing the interface to design novel proteins based on your design criteria."""
def __init__(
self,
session: APISession,
):
self.session = session
[docs]
def list_designs(self) -> list[DesignFuture]:
"""
List all designs.
Returns
-------
list of DesignFuture
A list of DesignFuture objects representing all designs.
"""
return [
DesignFuture(
session=self.session,
metadata=m,
)
for m in api.designs_list(session=self.session)
]
[docs]
def get_design(self, design_id: str) -> DesignFuture:
"""
Retrieve a specific design by its ID.
Parameters
----------
design_id : str
ID of the design to retrieve.
Returns
-------
DesignFuture
A future object representing the design job and its results.
"""
return DesignFuture(
session=self.session,
metadata=api.design_get(session=self.session, design_id=design_id),
)
[docs]
def create_genetic_algorithm_design(
self,
assay: AssayDataset,
criteria: Criteria | Subcriterion | Criterion,
num_steps: int = 25,
pop_size: int = 1024,
n_offsprings: int = 5120,
crossover_prob: float = 1.0,
crossover_prob_pointwise: float = 0.2,
mutation_average_mutations_per_seq: int = 1,
allowed_tokens: DesignConstraint | dict[int, list[str]] = {},
) -> DesignFuture:
"""
Start a protein design job using a genetic algorithm based on assay data, a trained ML model, and specified criteria.
Parameters
----------
assay : AssayDataset
The AssayDataset to design from.
criteria : Criteria or Subcriterion or Criterion
Criteria for evaluating the design.
num_steps : int, optional
The number of steps in the genetic algorithm. Default is 25.
pop_size : int, optional
The population size for the genetic algorithm. Default is 1024.
n_offsprings : int, optional
The number of offspring for the genetic algorithm. Default is 5120.
crossover_prob : float, optional
The crossover probability for the genetic algorithm. Default is 1.0.
crossover_prob_pointwise : float, optional
The pointwise crossover probability for the genetic algorithm. Default is 0.2.
mutation_average_mutations_per_seq : int, optional
The average number of mutations per sequence. Default is 1.
allowed_tokens : DesignConstraint or dict of int to list of str, optional
A dict of positions and allowed tokens (e.g. {1: ['G', 'L']}) designating how mutations may occur. Defaults to empty dict.
Returns
-------
DesignFuture
A future object representing the design job and its results.
"""
return DesignFuture.create(
session=self.session,
job=api.designer_create_genetic_algorithm(
self.session,
assay_id=assay.id,
criteria=criteria,
num_steps=num_steps,
pop_size=pop_size,
n_offsprings=n_offsprings,
crossover_prob=crossover_prob,
crossover_prob_pointwise=crossover_prob_pointwise,
mutation_average_mutations_per_seq=mutation_average_mutations_per_seq,
allowed_tokens=allowed_tokens,
),
)
def create_design_job(
self,
*args,
):
raise AttributeError(
"create_design_job belongs to the deprecated design interface. Use create_genetic_algorithm_design instead in the new design interface."
)
def get_design_results(self, *args):
raise AttributeError(
"get_design_results belongs to the deprecated design interface. Use get_design and wait instead in the new design interface."
)
