def __init__(
self,
pose:
object = None, # pyrosetta.rosetta.core.pose.Pose, Pose of starting structure
score:
object = None, # pyrosetta.ScoreFunction, # scorefunction from pyrosetta
seq_mover:
object = None, # pyrosetta.rosetta.protocols.moves.SequenceMover, # sequence of moves between MC evaluations
n_steps: int = 1000000,
kT: float = 1,
output: bool = True,
out_freq: int = 500,
):
super().__init__()
# initialize input values
self.pose = pose
self._score = score
self.seq_mover = seq_mover
self._kT = kT
self.n_steps = n_steps
self._output = output
self._out_freq = out_freq
if self._output is False:
self._out_freq = n_steps
# Build MC Object
self.mc = pyrosetta.MonteCarlo(self.pose, self._score, self._kT)
self.mc_trial = pyrosetta.TrialMover(self.seq_mover, self.mc)
if self._output:
self.pymol = pyrosetta.PyMOLMover()
print("Initial Energy :", self.get_energy())
def build_trial_mc_alg(self, mover):
"""
Write any sequence mover (stored in 'folding_protocols') to a trial object for
MC simulations. Depends on MC mover, so change any mc parameters (kT, pose, etc.)
before running this.
mover: mover object to be added to TrialMover object
"""
self.trial_mc = pyrosetta.TrialMover(mover, self.mc)
def monte_carlo_fixed(self, pose, mover, score_function, temperature,
trajectory, fixed_moves):
"""Performs Metropolis Monte Carlo (MMC) search with a specified
move, a fixed number of times in different trajectories. Each
trajectory is created by performing moves from the initial
conformation passed in the argument.
Args:
pose: A pyrosetta Pose object containing initial
conformation.
mover: A pyrosetta Mover object derermining the moves in MMC
search.
score_function: A pyrosetta ScoreFunction object for scoring
each move.
temperature: An int/float defining the temperature of MMC
search.
trajectory: A positive int indicating the number of
trajectories.
fixed_moves: A positive int indicating the number of moves
in each trajectory.
Returns:
A list containing the population generated by the MMC
search.
"""
population = []
# Perform MMC on all trajectories
for i in range(trajectory):
new_pose = pr.Pose()
new_pose.assign(pose)
mc = pr.MonteCarlo(new_pose, score_function, temperature)
trial_mover = pr.TrialMover(mover, mc)
# Perform MMC for a fixed number of moves
for j in range(fixed_moves):
trial_mover.apply(new_pose)
pose_ca_rmsd = pr.rosetta.core.scoring.CA_rmsd(
self.native_pose, new_pose)
if pose_ca_rmsd < self.last_op_min_ca_rmsd:
self.last_op_min_ca_rmsd = pose_ca_rmsd
self.last_op_min_ca_rmsd_pose.assign(new_pose)
population.append(new_pose)
# Bookkeeping
self.last_op_energy_evals = (fixed_moves * trajectory)
self.total_energy_evals += (fixed_moves * trajectory)
if self.last_op_min_ca_rmsd < self.min_ca_rmsd:
self.min_ca_rmsd = self.last_op_min_ca_rmsd
self.min_ca_rmsd_pose.assign(self.last_op_min_ca_rmsd_pose)
return population
def __init__(self,
sequence,
BBB_angle=120,
BBBB_dihe=180,
file_name='outputs/traj.pdb',
energy_graph_output=False):
"""
folding object used for easily implementing different
movers into a single folding algorithm.
Arguments
---------
sequence : str
Sequence of CG residues
BBB_angle : float
Desired angle of all B-B-B angles. Generalizes to all backbone models (not working)
BBBB_angle : float
Desired dihedral of all B-B-B-B torsion angles. Generalizes to all backbone models (not working)
"""
# Build CG model and set desired initial angles
self.pose = pyrosetta.pose_from_sequence(sequence, auto_termini=False)
self.energy_graph_output = energy_graph_output
# self.pose = self.set_BBB_angles(self.pose, BBB_angle)
# self.pose = self.set_BBBB_dihe(self.pose, BBBB_dihe)
# PyMOL mover, if wanting to visualize
self.pymol = pyrosetta.PyMOLMover()
self.pymol.apply(self.pose)
# randomizer = CG_movers.randomizeBackBone(self.pose)
# randomizer.apply(self.pose)
self.pymol.apply(self.pose)
# Building PDBTrajWriter object, used for writing multiple structures
# to a single file
self.PDB_writer = pyrosetta.rosetta.protocols.canonical_sampling.PDBTrajectoryRecorder(
)
# self.PDB_writer.apply(self.pose) # write initial structure
self.PDB_writer.file_name('outputs/traj.pdb')
self.PDB_writer.stride(100)
# Define scorefunction terms
self.scorefxn = pyrosetta.ScoreFunction()
self.scorefxn.set_weight(pyrosetta.rosetta.core.scoring.fa_rep, 1)
self.scorefxn.set_weight(pyrosetta.rosetta.core.scoring.fa_atr, 1)
self.scorefxn.set_weight(pyrosetta.rosetta.core.scoring.fa_intra_atr,
1)
self.scorefxn.set_weight(pyrosetta.rosetta.core.scoring.fa_intra_rep,
1)
self.scorefxn.set_weight(pyrosetta.rosetta.core.scoring.mm_twist, 1)
self.scorefxn.set_weight(pyrosetta.rosetta.core.scoring.mm_bend, 1)
# self.scorefxn.set_weight(pyrosetta.rosetta.core.scoring.mm_lj_inter_rep, 1) # segfaults beware!
# self.scorefxn.set_weight(pyrosetta.rosetta.core.scoring.mm_lj_inter_atr, 1)
# self.scorefxn.set_weight(pyrosetta.rosetta.core.scoring.mm_lj_intra_rep, 1)
# self.scorefxn.set_weight(pyrosetta.rosetta.core.scoring.mm_lj_intra_atr, 1)
# Build standard CG 1-1 movers
self.small = CG_movers.CGSmallMover(self.pose)
# self.shear = CG_movers.CGShearMover(self.pose)
self.small_angle = CG_movers.CGSmallAngleMover(self.pose)
# Build minimization movers
self.mini = pyrosetta.rosetta.protocols.minimization_packing.MinMover()
self.mini.min_type('lbfgs_armijo_nonmonotone')
self.movemap = pyrosetta.MoveMap()
self.mini.score_function(self.scorefxn)
# for atom in self.small_angle.bb_atoms:
# self.movemap.set(pyrosetta.rosetta.core.id.DOF_ID(atom , pyrosetta.rosetta.core.id.THETA), True)
self.movemap.set_bb_true_range(1, self.pose.size())
self.mini.movemap(self.movemap)
# Build MC object + Trial Mover (empty for now)
self.mc = pyrosetta.MonteCarlo(self.pose, self.scorefxn, 1)
self.trial_mc = pyrosetta.TrialMover()
# Building variable to store various folding algorithms
self.folding_protocols = {}
# Adding a default mover
self.build_fold_alg('default')
self.add_folding_move('default', pyrosetta.RepeatMover(self.small, 10))
# self.add_folding_move('default', pyrosetta.RepeatMover(self.shear, 10))
self.add_folding_move('default', pyrosetta.RepeatMover(self.mini, 10))
def kT(self, kT):
self._kT = kT
self.mc.set_temperature(self.kT)
self.mc_trial = pyrosetta.TrialMover(self.seq_mover, self.mc)