def make_mutant(self,
pose: pyrosetta.Pose,
mutation: Union[str, Mutation],
chain='A',
distance: int = 10,
cycles: int = 5) -> pyrosetta.Pose:
"""
Make a point mutant (``A23D``).
:param pose: pose
:param mutation:
:param chain:
:return:
"""
if pose is None:
mutant = self.pose.clone()
else:
mutant = pose.clone()
if isinstance(mutation, str):
mutation = Mutation(mutation, chain, mutant)
MutateResidue = pyrosetta.rosetta.protocols.simple_moves.MutateResidue
MutateResidue(target=mutation.pose_resi,
new_res=mutation.to_resn3).apply(mutant)
self.relax_around_mover(mutant,
mutation=mutation,
distance=distance,
cycles=cycles,
own_chain_only=False)
return mutant
def make_mutant(self,
pose: pyrosetta.Pose,
mutation: str,
chain='A') -> pyrosetta.Pose:
"""
Make a point mutant (``A23D``).
:param pose: pose
:param mutation:
:param chain:
:return:
"""
mutant = pose.clone()
pose2pdb = pose.pdb_info().pdb2pose
rex = re.match('(\w)(\d+)(\w)', mutation)
r = pose2pdb(res=int(rex.group(2)), chain=chain)
rn = pose.residue(r).name1()
assert rn == rex.group(
1
), f'residue {r}(pose)/{rex.group(2)}(pdb) is a {rn}, not a {rex.group()}'
MutateResidue = pyrosetta.rosetta.protocols.simple_moves.MutateResidue
MutateResidue(target=r,
new_res=self._name3[rex.group(3)]).apply(mutant)
self.relax_around_mover(mutant,
int(rex.group(2)),
chain,
distance=12,
cycles=15)
return mutant
def movement(self, original: pyrosetta.Pose, resi: int, chain: str, distance: int,
trials: int = 50, temperature: int = 1.0, replicate_number: int = 10):
"""
This method adapted from a notebook of mine, but not from an official source, is not well written.
It should be a filter and score combo.
It returns the largest bb_rmsd of the pdb residue resi following backrub.
"""
# this code is experimental
n = self.get_neighbour_vector(pose=original, resi=resi, chain=chain, distance=distance,
own_chain_only=False)
# resi
if chain is None: # pose numbering.
target_res = resi
else:
target_res = original.pdb_info().pdb2pose(chain=chain, res=resi)
# prep
rv = pyrosetta.rosetta.core.select.residue_selector.ResidueVector(n)
backrub = pyrosetta.rosetta.protocols.backrub.BackrubMover()
backrub.set_pivot_residues(rv)
# https://www.rosettacommons.org/docs/latest/scripting_documentation/RosettaScripts/Movers/movers_pages/GenericMonteCarloMover
monégasque = pyrosetta.rosetta.protocols.monte_carlo.GenericMonteCarloMover(maxtrials=trials,
max_accepted_trials=trials,
# gen.max_accepted_trials() = 0
task_scaling=5,
# gen.task_scaling()
mover=backrub,
temperature=temperature,
sample_type='low',
drift=True)
monégasque.set_scorefxn(self.scorefxn)
# monégasque.add_filter(filters , False , 0.005 , 'low' , True )
# define the first 4 atoms (N C CA O)
am = pyrosetta.rosetta.utility.vector1_unsigned_long(4)
for i in range(1, 5):
am[i] = i
# find most deviant
best_r = 0
for i in range(replicate_number):
variant = original.clone()
monégasque.apply(variant)
if monégasque.accept_counter() > 0:
variant = monégasque.last_accepted_pose() # pretty sure redundant
# bb_rmsd is all residues: pyrosetta.rosetta.core.scoring.bb_rmsd(pose, ori)
r = pyrosetta.rosetta.core.scoring.residue_rmsd_nosuper(variant.residue(target_res),
original.residue(target_res), am)
if r > best_r:
best_r = r
return best_r
