def test_md(self):
"""Test you can setup MD"""
mdl = IMP.Model()
s = IMP.pmi.topology.System(mdl)
st1 = s.create_state()
seqs = IMP.pmi.topology.Sequences(self.get_input_file_name('seqs.fasta'))
m1 = st1.create_molecule("Prot1",sequence=seqs["Protein_1"])
atomic_res = m1.add_structure(self.get_input_file_name('prot.pdb'),
chain_id='A',res_range=(55,63),offset=-54)
m1.add_representation(atomic_res,resolutions=[0])
hier = s.build()
dof = IMP.pmi.dof.DegreesOfFreedom(mdl)
md_ps = dof.setup_md(m1)
rex = IMP.pmi.macros.ReplicaExchange0(mdl,
root_hier=hier,
molecular_dynamics_sample_objects=md_ps,
number_of_frames=2,
test_mode=True,
replica_exchange_object=rem)
rex.execute_macro()
def test_md(self):
"""Test you can setup MD"""
mdl = IMP.Model()
s = IMP.pmi.topology.System(mdl)
st1 = s.create_state()
seqs = IMP.pmi.topology.Sequences(
self.get_input_file_name('seqs.fasta'))
m1 = st1.create_molecule("Prot1", sequence=seqs["Protein_1"])
atomic_res = m1.add_structure(self.get_input_file_name('prot.pdb'),
chain_id='A',
res_range=(55, 63),
offset=-54)
m1.add_representation(atomic_res, resolutions=[0])
hier = s.build()
dof = IMP.pmi.dof.DegreesOfFreedom(mdl)
md_ps = dof.setup_md(m1)
rex = IMP.pmi.macros.ReplicaExchange0(
mdl,
root_hier=hier,
molecular_dynamics_sample_objects=md_ps,
number_of_frames=2,
test_mode=True,
replica_exchange_object=rem)
rex.execute_macro()
# add charmm restraints
print("adding restraints")
charmm = IMP.pmi.restraints.stereochemistry.CharmmForceFieldRestraint(hier)
charmm.add_to_model()
# add elastic network on secondary structure units
sses = IMP.pmi.io.parse_dssp(IMP.pmi.get_example_path("data/gcp2.dssp"), "A")
all_rs = []
for sse in sses["helix"] + sses["beta"]:
er = IMP.pmi.restraints.stereochemistry.ElasticNetworkRestraint(
selection_tuples=sse, strength=10.0, dist_cutoff=5.0, ca_only=True, hierarchy=hier
)
all_rs.append(er)
er.add_to_model()
# seutp MD and run
dof = IMP.pmi.dof.DegreesOfFreedom(mdl)
md_ps = dof.setup_md(gcp2)
rex = IMP.pmi.macros.ReplicaExchange0(
mdl,
root_hier=hier,
crosslink_restraints=all_rs, # for visualizing SSEs in RMF
molecular_dynamics_sample_objects=md_ps,
molecular_dynamics_steps=5,
number_of_best_scoring_models=0, # set >0 to store best PDB files (but this is slow to do online)
number_of_frames=1, # increase number of frames to get better results!
global_output_directory="atomistic_output/",
)
rex.execute_macro()
# add elastic network on secondary structure units
sses = IMP.pmi.io.parse_dssp(IMP.pmi.get_example_path('data/gcp2.dssp'), 'A',
name_map={'A': 'GCP2'})
all_rs = []
for sse in sses['helix']+sses['beta']:
er = IMP.pmi.restraints.stereochemistry.ElasticNetworkRestraint(
selection_tuples=sse,
strength=10.0,
dist_cutoff=5.0,
ca_only=True,
hierarchy=hier)
all_rs.append(er)
er.add_to_model()
# setup MD and run
dof = IMP.pmi.dof.DegreesOfFreedom(mdl)
md_ps = dof.setup_md(gcp2)
rex = IMP.pmi.macros.ReplicaExchange0(
mdl,
root_hier=hier,
# for visualizing SSEs in RMF
crosslink_restraints=all_rs,
molecular_dynamics_sample_objects=md_ps,
molecular_dynamics_steps=5,
# set >0 to store best PDB files (but this is slow to do online)
number_of_best_scoring_models=0,
# increase number of frames to get better results!
number_of_frames=1,
global_output_directory='atomistic_output/')
rex.execute_macro()
rdihed.add_to_model()
all_rs.append(rdihed)
output_rs.append(rdihed)
# add CHARMM restraints
rcharmm = CharmmForceFieldRestraint(hier)
rcharmm.add_to_model()
all_rs.append(rcharmm)
# set up simulation
dof = IMP.pmi.dof.DegreesOfFreedom(m)
dof.get_nuisances_from_restraint(rnoes)
dof.get_nuisances_from_restraint(rdihed)
# minimize via md
md_ps = dof.setup_md(ubq)
IMP.set_log_level(IMP.SILENT)
# run replica exchange md with monte carlo
rex = IMP.pmi.macros.ReplicaExchange0(m,
root_hier=hier,
crosslink_restraints=output_rs,
output_objects=all_rs,
number_of_frames=100000,
monte_carlo_sample_objects=dof.get_movers(),
monte_carlo_steps=10,
molecular_dynamics_sample_objects=md_ps,
molecular_dynamics_steps=10,
replica_exchange_minimum_temperature=1.,
replica_exchange_maximum_temperature=2.5,
number_of_best_scoring_models=0,
atomistic=True,
