def run_mc_simulation(job):
start = time.time()
os.chdir(job.ws)
cg_pyrosetta.init()
# Build Annealer Parameters
# Annealing simulation with 500000
annealer_params = cg_pyrosetta.CG_monte_carlo.\
CGMonteCarloAnnealerParameters(n_inner = 1000,
t_init = 10,
anneal_rate = 0.9,
n_anneals = 50,
annealer_criteron = cg_pyrosetta.CG_monte_carlo.Repeat10Convergence,
mc_output = True,
out_freq = 500,
)
# Build Energy Function
energy_function = cg_pyrosetta.CG_monte_carlo.EnergyFunctionFactory(
).build_energy_function({
"mm_twist": 1,
"mm_bend": 1,
"fa_atr": 1,
"fa_rep": 1,
"fa_intra_rep": 1,
"fa_intra_atr": 1,
})
# Pose to be folded
pose = cg_pyrosetta.pyrosetta.pose_from_sequence("X[CG13x3:CGLower]" +
"X[CG13x3]" *
(job.sp.nmer - 2) +
"X[CG13x3:CGUpper]")
# Build Minimizer
mini = cg_pyrosetta.pyrosetta.rosetta.protocols.minimization_packing.MinMover(
)
mini.min_type('lbfgs_armijo_nonmonotone')
mini.score_function(energy_function)
movemap = cg_pyrosetta.pyrosetta.MoveMap()
movemap.set_bb_true_range(1, pose.size())
movemap.set_branches(True)
movemap.set(cg_pyrosetta.pyrosetta.rosetta.core.id.THETA, True)
movemap.set(cg_pyrosetta.pyrosetta.rosetta.core.id.PHI, True)
# print("Using the following MoveMap:")
# print(movemap)
mini.movemap(movemap)
# Build Sequence Mover for MC object
sequence_mover_factory = cg_pyrosetta.CG_monte_carlo.SequenceMoverFactory(
pose, {"mini": mini})
sequence_mover = sequence_mover_factory.build_seq_mover({
"small_dihe": 1,
"small_angle": 1,
"mini": 1,
})
cg_annealer = cg_pyrosetta.CG_monte_carlo.CGMonteCarloAnnealer(
seq_mover=sequence_mover,
score_function=energy_function,
pose=pose,
param_file_object=annealer_params)
# Setup Configuration/Energy observer for saving minimum energy structures
struct_obs = cg_pyrosetta.CG_monte_carlo.StructureObserver(
cg_annealer.get_mc_sim())
energy_obs = cg_pyrosetta.CG_monte_carlo.EnergyObserver(
cg_annealer.get_mc_sim())
cg_annealer.registerObserver(struct_obs)
cg_annealer.registerObserver(energy_obs)
# Run Annealer
cg_annealer.run_schedule()
min_pose = cg_annealer._cg_mc_sim.get_minimum_energy_pose()
print("Writing structure to:")
print(job.fn("minimum.pdb"))
min_pose.dump_pdb(job.fn("minimum.pdb"))
end = time.time()
t_rep = end - start
print(t_rep, "seconds")
job.data['timing'] = t_rep
def main():
cgpy.change_parameters.changeAtomParameters(
{"CG1": ['X', 1.5, 0.2]},
atom_types_path="parameters/atom_properties.txt",
mm_atom_types_path="parameters/mm_atom_type_sets/mm_atom_properties.txt"
)
# Initialize CG_PyRosetta with extra mm_types
cgpy.init()
# cgpy.pyrosetta.init()
# CG MC Annealer Parameters
params = cgpy.CG_monte_carlo.\
CGMonteCarloAnnealerParameters(n_inner=100,
t_init=5,
anneal_rate=0.9,
n_anneals=3,
annealer_criteron=cgpy.CG_monte_carlo.Repeat10Convergence,
traj_out = "testing.pdb",
mc_output = True,
mc_traj = False,
out_freq = 10,
)
# Energy Function
energy_function = cgpy.CG_monte_carlo.\
EnergyFunctionFactory().build_energy_function(
{
"mm_twist": 1,
"mm_bend": 1,
"fa_atr": 1,
"fa_rep": 1,
"fa_intra_rep": 1,
"fa_intra_atr": 1
}
)
# Pose to be folded
pose = cgpy.pyrosetta.pose_from_sequence(
"X[CG11x3:CGLower]X[CG11x3]X[CG11x3]X[CG11x3]X[CG11x3:CGUpper]")
change_lengths = cgpy.CG_movers.setBondLengths(pose, {
"BB1 BB2": 1,
"BB2 BB3": 1,
"BB3 BB1": 1
})
change_lengths.apply(pose)
print(energy_function(pose))
# exit()
# Build Minimizer
mini = cgpy.pyrosetta.rosetta.protocols.minimization_packing.MinMover()
mini.min_type('lbfgs_armijo_nonmonotone')
mini.score_function(energy_function)
# MoveMap for Minimizer
movemap = cgpy.pyrosetta.MoveMap()
movemap.set_bb_true_range(1, pose.size())
movemap.set_branches(True)
movemap.set(cgpy.pyrosetta.rosetta.core.id.THETA, True)
movemap.set(cgpy.pyrosetta.rosetta.core.id.PHI, True)
# Insert MoveMap into Minimizer
mini.movemap(movemap)
# import pdb
# pdb.set_trace()
mini.apply(pose)
# Build Sequence Mover for MC object
sequence_mover_fct = cgpy.CG_monte_carlo.SequenceMoverFactory(
pose, {"mini": mini})
sequence_mover = sequence_mover_fct.build_seq_mover({
"small_dihe": 1,
"small_angle": 1,
"mini": 20
})
# Initialize Annealer
cg_annealer = cgpy.CG_monte_carlo.CGMonteCarloAnnealer(
seq_mover=sequence_mover,
score_function=energy_function,
pose=pose,
param_file_object=params)
min_energy_confs = cgpy.CG_monte_carlo.MinEnergyConfigObserver(
cg_annealer.get_mc_sim())
cg_annealer.registerObserver(min_energy_confs)
# Run Annealer
cg_annealer.run_schedule()
min_pose = cg_annealer._cg_mc_sim.get_minimum_energy_pose()
cg_annealer._cg_mc_sim.pymol.apply(min_pose)
# Extract all structures
print("There are a total of", len(min_energy_confs.structures),
"structures")
print("Energies:")
if not os.path.exists("structures"):
os.mkdir("structures")
for i in range(len(min_energy_confs.energies)):
print("Structure:", i, "Energy:", min_energy_confs.energies[i])
min_energy_confs.structures[i].dump_pdb("structures/structure_" +
str(i) + ".pdb")
# Extract bottom 20 structures
sorted_structures = [
pose for _, pose in sorted(
zip(min_energy_confs.energies, min_energy_confs.structures))
]
if not os.path.exists("50_min_structures"):
os.mkdir("50_min_structures")
for i, p in enumerate(sorted_structures[:50]):
p.dump_pdb("50_min_structures/structure_" + str(i) + ".pdb")
# Save output config object
with open("min_energy_configs.pkl", "wb") as f:
pickle.dump(min_energy_confs, f, pickle.HIGHEST_PROTOCOL)
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--model",
required=False,
help="Type of model that you would like to generate \
random distribution of structures and energy for",
type=str,
default="CG11x3",
)
parser.add_argument(
"--mer",
required=False,
help="Number of monomers for this model",
type=int,
default=5,
)
parser.add_argument(
"--kt",
required=True,
default=100,
help="kT value used to generate a random ensemble",
type=int,
)
parser.add_argument("--params",
required=False,
type=str,
help="YAML file where specific torsions are defined",
default="params.yml")
parser.add_argument(
"--e_output",
required=False,
type=str,
default="outputs/random_distribution.npy",
help="output filename of the energies",
)
parser.add_argument(
"--s_output",
required=False,
type=str,
default="outputs/random_distribution.pdb",
help="output filename of the energies",
)
parser.add_argument(
"--steps",
required=False,
default=1000000,
type=int,
help="Number of steps to run the high T simulation",
)
parser.add_argument(
"--stride",
required=False,
default=1000,
type=int,
help="Number of steps to run the high T simulation",
)
# Parse Arguments
args = parser.parse_args()
# Get parameters from .yml file
param_file = open(args.params, 'r')
params = yaml.load(param_file)
print(params)
# for param_type in params:
# if param_type == "atoms":
# cg_pyrosetta.change_parameters.changeAtomParameters(params["atoms"])
# if param_type == "dihedrals":
# cg_pyrosetta.change_parameters.changeTorsionParameters(params["dihedrals"])
# if param_type == "angles":
# cg_pyrosetta.change_parameters.changeAngleParameters(params["angles"])
# else:
# print("Input YAML file had a key for", param_type+".", "This key is not a valid parameter type for this model.", file=sys.stderr)
# print("Ignoring", param_type, "and continuing!", file=sys.stderr)
cg_pyrosetta.init()
# Build sequence for folder object
monomer = "X[" + args.model + "]"
sequence = monomer * args.mer
# Build Folder Object
folder = cg_pyrosetta.CG_folding.CGFoldingAlgorithm(sequence)
# Change PDBWriter to specific outputs
folder.PDB_writer.stride(args.stride)
folder.PDB_writer.file_name(args.s_output)
# Build MC algorithm to get unfolded ensemble
folder.build_fold_alg('no_min')
folder.add_folding_move('no_min', pyrosetta.RepeatMover(folder.small, 10))
folder.add_folding_move('no_min', folder.PDB_writer)
# Set kT
folder.kT = args.kt
# Run algorithm
folder.run_folding_alg('no_min', args.steps)
energy_list = []
for line in open(args.s_output):
if "pose" in line:
# print(line)
termwise_energy = line.rstrip().split(' ')
energy_list.append(float(termwise_energy[1]))
else:
continue
np.save(args.e_output, np.array(energy_list))
def run_mc_simulation(job):
os.chdir(job.ws)
cg_pyrosetta.init()
# Build Annealer Parameters
annealer_params = cg_pyrosetta.CG_monte_carlo.\
CGMonteCarloAnnealerParameters(n_inner = 100,
t_init = 5,
anneal_rate = 0.9,
n_anneals = 3,
annealer_criteron = cg_pyrosetta.CG_monte_carlo.Repeat10Convergence,
mc_output = True,
out_freq = 50,
)
# Build Energy Function
energy_function = cg_pyrosetta.CG_monte_carlo.EnergyFunctionFactory(
).build_energy_function({
"mm_twist": 1,
"mm_bend": 1,
"fa_atr": 1,
"fa_rep": 1,
"fa_intra_rep": 1,
"fa_intra_atr": 1,
})
# Pose to be folded
pose = cg_pyrosetta.pyrosetta.pose_from_sequence(
"X[CG31:CGLower]X[CG31]X[CG31]X[CG31]X[CG31:CGUpper]")
change_lengths = cg_pyrosetta.CG_movers.setBondLengths(
pose, {
"BB1 BB2": job.sp.bb_length,
"BB2 BB3": job.sp.bb_length,
"BB3 BB1": job.sp.bb_length
})
change_lengths.apply(pose)
# Build Minimizer
mini = cg_pyrosetta.pyrosetta.rosetta.protocols.minimization_packing.MinMover(
)
mini.min_type('lbfgs_armijo_nonmonotone')
mini.score_function(energy_function)
movemap = cg_pyrosetta.pyrosetta.MoveMap()
movemap.set_bb_true_range(1, pose.size())
movemap.set_branches(True)
movemap.set(cg_pyrosetta.pyrosetta.rosetta.core.id.THETA, True)
movemap.set(cg_pyrosetta.pyrosetta.rosetta.core.id.PHI, True)
# print("Using the following MoveMap:")
# print(movemap)
mini.movemap(movemap)
# Build Sequence Mover for MC object
sequence_mover_factory = cg_pyrosetta.CG_monte_carlo.SequenceMoverFactory(
pose, {"mini": mini})
sequence_mover = sequence_mover_factory.build_seq_mover({
"small_dihe": 1,
"small_angle": 1,
"mini": 1,
})
cg_annealer = cg_pyrosetta.CG_monte_carlo.CGMonteCarloAnnealer(
seq_mover=sequence_mover,
score_function=energy_function,
pose=pose,
param_file_object=annealer_params)
# Setup Configuration/Energy observer for saving minimum energy structures
min_energy_confs = cg_pyrosetta.CG_monte_carlo.MinEnergyConfigObserver(
cg_annealer.get_mc_sim())
cg_annealer.registerObserver(min_energy_confs)
# Run Annealer
cg_annealer.run_schedule()
min_pose = cg_annealer._cg_mc_sim.get_minimum_energy_pose()
print("Writing structure to:")
print(job.fn("minimum.pdb"))
min_pose.dump_pdb(job.fn("minimum.pdb"))