def each_group_should_have_six_restraints(self):
results = parse.get_secondary_structure_restraints(
content=self.ss, system=self.system, scaler=self.scaler
)
for group in results:
self.assertEqual(len(group), 6)
def each_group_should_have_six_restraints(self):
results = parse.get_secondary_structure_restraints(contents=self.ss,
system=self.system,
scaler=self.scaler)
for group in results:
self.assertEqual(len(group), 6)
def setup_system():
# get the sequence
sequence = "AAAAAAAAAAAAAAAA"
sequence = parse.get_sequence_from_AA1(contents=sequence)
# create the system
p = system.ProteinMoleculeFromSequence(sequence)
b = system.SystemBuilder()
s = b.build_system_from_molecules([p])
s.temperature_scaler = system.LinearTemperatureScaler(0, 1, 300, 310)
rest_scaler = s.restraints.create_scaler("nonlinear",
alpha_min=0,
alpha_max=1,
factor=4.0)
secondary = "H" * 16
secondary_restraints = parse.get_secondary_structure_restraints(
system=s, scaler=rest_scaler, contents=secondary)
s.restraints.add_selectively_active_collection(secondary_restraints,
len(secondary_restraints))
# create the options
options = system.RunOptions()
options.use_big_timestep = True
# create a store
store = vault.DataStore(s.n_atoms,
N_REPLICAS,
s.get_pdb_writer(),
block_size=BACKUP_FREQ)
store.initialize(mode="w")
store.save_system(s)
store.save_run_options(options)
# create and store the remd_runner
l = ladder.NearestNeighborLadder(n_trials=1)
policy = adaptor.AdaptationPolicy(1.0, 50, 100)
a = adaptor.EqualAcceptanceAdaptor(n_replicas=N_REPLICAS,
adaptation_policy=policy)
remd_runner = master_runner.MasterReplicaExchangeRunner(N_REPLICAS,
max_steps=N_STEPS,
ladder=l,
adaptor=a)
store.save_remd_runner(remd_runner)
# create and store the communicator
c = comm.MPICommunicator(s.n_atoms, N_REPLICAS)
store.save_communicator(c)
# create and save the initial states
states = [gen_state(s) for i in range(N_REPLICAS)]
states[1].alpha = 1.0
store.save_states(states, 0)
# save data_store
store.save_data_store()
return s.n_atoms
def setup_system():
# get the sequence
sequence = "AAAAAAAAAAAAAAAA"
sequence = parse.get_sequence_from_AA1(contents=sequence)
# create the system
p = system.ProteinMoleculeFromSequence(sequence)
b = system.SystemBuilder()
s = b.build_system_from_molecules([p])
s.temperature_scaler = system.LinearTemperatureScaler(0, 1, 300, 310)
rest_scaler = s.restraints.create_scaler(
"nonlinear", alpha_min=0, alpha_max=1, factor=4.0
)
secondary = "H" * 16
secondary_restraints = parse.get_secondary_structure_restraints(
system=s, scaler=rest_scaler, contents=secondary
)
s.restraints.add_selectively_active_collection(
secondary_restraints, len(secondary_restraints)
)
# create the options
options = system.RunOptions()
options.use_big_timestep = True
# create a store
store = vault.DataStore(
s.n_atoms, N_REPLICAS, s.get_pdb_writer(), block_size=BACKUP_FREQ
)
store.initialize(mode="w")
store.save_system(s)
store.save_run_options(options)
# create and store the remd_runner
l = ladder.NearestNeighborLadder(n_trials=1)
policy = adaptor.AdaptationPolicy(1.0, 50, 100)
a = adaptor.EqualAcceptanceAdaptor(n_replicas=N_REPLICAS, adaptation_policy=policy)
remd_runner = master_runner.MasterReplicaExchangeRunner(
N_REPLICAS, max_steps=N_STEPS, ladder=l, adaptor=a
)
store.save_remd_runner(remd_runner)
# create and store the communicator
c = comm.MPICommunicator(s.n_atoms, N_REPLICAS)
store.save_communicator(c)
# create and save the initial states
states = [gen_state(s) for i in range(N_REPLICAS)]
states[1].alpha = 1.0
store.save_states(states, 0)
# save data_store
store.save_data_store()
return s.n_atoms
def setup_system():
# get the sequence
sequence = 'AAAAAAAAAAAAAAAA'
sequence = parse.get_sequence_from_AA1(contents=sequence)
# create the system
p = system.ProteinMoleculeFromSequence(sequence)
b = system.SystemBuilder()
s = b.build_system_from_molecules([p])
s.temperature_scaler = system.LinearTemperatureScaler(0, 1, 300, 310)
rest_scaler = s.restraints.create_scaler('nonlinear', alpha_min=0, alpha_max=1, factor=4.0)
secondary = 'H'*16
secondary_restraints = parse.get_secondary_structure_restraints(system=s, scaler=rest_scaler,
contents=secondary)
s.restraints.add_selectively_active_collection(secondary_restraints, len(secondary_restraints))
# create com restraint
com = s.restraints.create_restraint('com', rest_scaler, ramp=None,
group1=[(1, 'CA')],
group2=[(3, 'CA')],
weights1=None,
weights2=None,
dims='xyz',
force_const=100.,
distance=0.5)
s.restraints.add_as_always_active(com)
# create absolute com restraint
com = s.restraints.create_restraint('abs_com', rest_scaler, ramp=None,
group=[(1, 'CA')],
weights=None,
dims='xyz',
force_const=1.,
position=[0., 0., 0.])
s.restraints.add_as_always_active(com)
# create the options
options = system.RunOptions()
# create a store
store = vault.DataStore(s.n_atoms, N_REPLICAS, s.get_pdb_writer(), block_size=BACKUP_FREQ)
store.initialize(mode='w')
store.save_system(s)
store.save_run_options(options)
# create and store the remd_runner
l = ladder.NearestNeighborLadder(n_trials=1)
policy = adaptor.AdaptationPolicy(1.0, 50, 100)
a = adaptor.EqualAcceptanceAdaptor(n_replicas=N_REPLICAS, adaptation_policy=policy)
remd_runner = master_runner.MasterReplicaExchangeRunner(N_REPLICAS, max_steps=N_STEPS, ladder=l, adaptor=a)
store.save_remd_runner(remd_runner)
# create and store the communicator
c = comm.MPICommunicator(s.n_atoms, N_REPLICAS)
store.save_communicator(c)
# create and save the initial states
states = [gen_state(s) for i in range(N_REPLICAS)]
states[1].alpha = 1.0
store.save_states(states, 0)
# save data_store
store.save_data_store()
return s.n_atoms
def test_adds_correct_number_of_groups(self):
results = parse.get_secondary_structure_restraints(contents=self.ss, system=self.system,
scaler=self.scaler)
self.assertEqual(len(results), 4)
def test_adds_correct_number_of_groups(self):
results = parse.get_secondary_structure_restraints(contents=self.ss,
system=self.system,
scaler=self.scaler)
self.assertEqual(len(results), 4)
def setup_system():
# load the sequence
sequence = parse.get_sequence_from_AA1(filename='sequence.dat')
n_res = len(sequence.split())
# build the system
p = system.ProteinMoleculeFromSequence(sequence)
b = system.SystemBuilder()
s = b.build_system_from_molecules([p])
s.temperature_scaler = system.GeometricTemperatureScaler(0, 0.4, 300., 550.)
#
# Secondary Structure
#
ss_scaler = s.restraints.create_scaler('constant')
ss_rests = parse.get_secondary_structure_restraints(filename='ss.dat', system=s, scaler=ss_scaler,
torsion_force_constant=2.5, distance_force_constant=2.5)
n_ss_keep = int(len(ss_rests) * 0.85)
s.restraints.add_selectively_active_collection(ss_rests, n_ss_keep)
#
# Confinement Restraints
#
conf_scaler = s.restraints.create_scaler('constant')
confinement_rests = []
confinement_dist = (16.9*np.log(s.residue_numbers[-1])-15.8)/28
for index in range(n_res):
rest = s.restraints.create_restraint('confine', conf_scaler, res_index=index+1, atom_name='CA', radius=confinement_dist, force_const=250.0)
confinement_rests.append(rest)
s.restraints.add_as_always_active_list(confinement_rests)
#
# Distance Restraints
#
# High reliability
#
dist_scaler = s.restraints.create_scaler('nonlinear', alpha_min=0.4, alpha_max=1.0, factor=4.0)
selected_dist = get_dist_restraints('contacts.dat', s, dist_scaler)
n_high_keep = int(1.00 * len(selected_dist))
s.restraints.add_selectively_active_collection(selected_dist, n_high_keep)
#
# create the options
options = system.RunOptions()
options.implicit_solvent_model = 'gbNeck2'
options.use_big_timestep = True
options.cutoff = 1.8
options.use_amap = True
options.amap_beta_bias = 1.0
options.timesteps = 14286
options.minimize_steps = 20000
# create a store
store = vault.DataStore(s.n_atoms, N_REPLICAS, s.get_pdb_writer(), block_size=BLOCK_SIZE)
store.initialize(mode='w')
store.save_system(s)
store.save_run_options(options)
# create and store the remd_runner
l = ladder.NearestNeighborLadder(n_trials=48 * 48)
policy = adaptor.AdaptationPolicy(2.0, 50, 50)
a = adaptor.EqualAcceptanceAdaptor(n_replicas=N_REPLICAS, adaptation_policy=policy)
remd_runner = master_runner.MasterReplicaExchangeRunner(N_REPLICAS, max_steps=N_STEPS, ladder=l, adaptor=a, ramp_steps=50)
store.save_remd_runner(remd_runner)
# create and store the communicator
c = comm.MPICommunicator(s.n_atoms, N_REPLICAS)
store.save_communicator(c)
# create and save the initial states
states = [gen_state(s, i) for i in range(N_REPLICAS)]
store.save_states(states, 0)
# save data_store
store.save_data_store()
return s.n_atoms
def setup_system():
# load the sequence
sequence = parse.get_sequence_from_AA1(filename='sequence.dat')
n_res = len(sequence.split())
# build the system
p = system.ProteinMoleculeFromSequence(sequence)
b = system.SystemBuilder()
s = b.build_system_from_molecules([p])
s.temperature_scaler = system.GeometricTemperatureScaler(
0, 0.4, 300., 550.)
#
# Secondary Structure
#
ss_scaler = s.restraints.create_scaler('constant')
ss_rests = parse.get_secondary_structure_restraints(
filename='ss.dat',
system=s,
scaler=ss_scaler,
torsion_force_constant=2.5,
distance_force_constant=2.5)
n_ss_keep = int(len(ss_rests) * 0.70) #We enforce 70% of restrains
s.restraints.add_selectively_active_collection(ss_rests, n_ss_keep)
#
# Confinement Restraints
#
conf_scaler = s.restraints.create_scaler('constant')
confinement_rests = []
confinement_dist = (16.9 * np.log(s.residue_numbers[-1]) - 15.8) / 28.
for index in range(n_res):
rest = s.restraints.create_restraint('confine',
conf_scaler,
LinearRamp(0, 100, 0, 1),
res_index=index + 1,
atom_name='CA',
radius=confinement_dist,
force_const=250.0)
confinement_rests.append(rest)
s.restraints.add_as_always_active_list(confinement_rests)
#
# Distance Restraints
#
# High reliability
#
dist_scaler = s.restraints.create_scaler('nonlinear',
alpha_min=0.4,
alpha_max=1.0,
factor=4.0)
#contact80_dist = get_dist_restraints('target_contacts_over_80.dat', s, dist_scaler)
#n_high_keep = int(0.80 * len(contact80_dist))
#s.restraints.add_selectively_active_collection(contact80_dist, n_high_keep)
#
# Long
#
#contact60_dist = get_dist_restraints('target_contacts_over_60.dat', s, dist_scaler)
#n_high_keep = int(0.60 * len(contact60_dist))
#s.restraints.add_selectively_active_collection(contact60_dist, n_high_keep)
#
# Heuristic Restraints
#
subset = np.array(range(n_res)) + 1
#
# Hydrophobic
#
create_hydrophobes(s, scaler=dist_scaler, group_1=subset)
#
# Strand Pairing
#
sse, active = make_ss_groups(subset=subset)
generate_strand_pairs(s, sse, active, subset=subset)
# create the options
options = system.RunOptions()
options.implicit_solvent_model = 'gbNeck2'
options.use_big_timestep = True
options.cutoff = 1.8
options.use_amap = True
options.amap_beta_bias = 1.0
options.timesteps = 14286
options.minimize_steps = 20000
# create a store
store = vault.DataStore(s.n_atoms,
N_REPLICAS,
s.get_pdb_writer(),
block_size=BLOCK_SIZE)
store.initialize(mode='w')
store.save_system(s)
store.save_run_options(options)
# create and store the remd_runner
l = ladder.NearestNeighborLadder(n_trials=48 * 48)
policy = adaptor.AdaptationPolicy(2.0, 50, 50)
a = adaptor.EqualAcceptanceAdaptor(n_replicas=N_REPLICAS,
adaptation_policy=policy)
remd_runner = master_runner.MasterReplicaExchangeRunner(N_REPLICAS,
max_steps=N_STEPS,
ladder=l,
adaptor=a)
store.save_remd_runner(remd_runner)
# create and store the communicator
c = comm.MPICommunicator(s.n_atoms, N_REPLICAS)
store.save_communicator(c)
# create and save the initial states
states = [gen_state(s, i) for i in range(N_REPLICAS)]
store.save_states(states, 0)
# save data_store
store.save_data_store()
return s.n_atoms
def setup_system():
# load the sequence
protein_sequence = parse.get_sequence_from_AA1(filename='protein.dat')
peptide_sequence = parse.get_sequence_from_AA1(filename='peptide.dat')
n_res_protein = len(protein_sequence.split())
n_res_peptide = len(peptide_sequence.split())
# build the system
protein = system.ProteinMoleculeFromSequence(protein_sequence)
peptide = system.ProteinMoleculeFromSequence(peptide_sequence)
protein.set_translation([100, 100, 150])
peptide.set_translation([100, 150, 100])
calcium1 = system.ProteinMoleculeFromSequence('CA')
calcium2 = system.ProteinMoleculeFromSequence('CA')
calcium3 = system.ProteinMoleculeFromSequence('CA')
calcium4 = system.ProteinMoleculeFromSequence('CA')
calcium1.set_translation([100, 105, 50])
calcium2.set_translation([100, 110, 50])
calcium3.set_translation([100, 115, 50])
calcium4.set_translation([100, 120, 50])
rdc_patcher = patchers.RdcAlignmentPatcher(n_tensors=1)
ond_patcher = patchers.VirtualSpinLabelPatcher({
17: 'OND',
34: 'OND',
42: 'OND',
53: 'OND',
86: 'OND',
110: 'OND',
117: 'OND',
127: 'OND',
143: 'OND',
149: 'OND'
})
b = system.SystemBuilder()
s = b.build_system_from_molecules(
[protein, calcium1, calcium2, calcium3, calcium4, peptide],
leap_header_cmds="source leaprc.water.tip3p",
patchers=[rdc_patcher, ond_patcher])
s.temperature_scaler = system.GeometricTemperatureScaler(
0, 0.3, 300., 550.)
ramp = s.restraints.create_scaler('linear_ramp',
start_time=1,
end_time=200,
start_weight=0,
end_weight=1)
#
# Secondary Structure
#
ss_scaler = s.restraints.create_scaler('constant')
protein_ss_rests = parse.get_secondary_structure_restraints(
filename='protein_ss.dat',
system=s,
scaler=ss_scaler,
ramp=ramp,
torsion_force_constant=2.5,
distance_force_constant=2.5,
min_secondary_match=5)
peptide_ss_rests = parse.get_secondary_structure_restraints(
filename='peptide_ss.dat',
system=s,
scaler=ss_scaler,
ramp=ramp,
torsion_force_constant=2.5,
distance_force_constant=2.5,
first_residue=int(n_res_protein) + 5) # + 4 due to calciums
# + 1 for 1-based indexing
protein_ss_keep = int(len(protein_ss_rests) * 0.95)
peptide_ss_keep = int(len(peptide_ss_rests) * 0.95)
s.restraints.add_selectively_active_collection(protein_ss_rests,
protein_ss_keep)
s.restraints.add_selectively_active_collection(peptide_ss_rests,
peptide_ss_keep)
#
# Confinement Restraints
#
conf_scaler = s.restraints.create_scaler('constant')
confinement_rests = []
n_res = n_res_protein + n_res_peptide + 4
for index in range(1, n_res + 1):
protein_rest = s.restraints.create_restraint('confine',
conf_scaler,
ramp=ramp,
res_index=index,
atom_name='CA',
radius=5,
force_const=250.0)
confinement_rests.append(protein_rest)
s.restraints.add_as_always_active_list(confinement_rests)
#
# Calcium restraints
#
scaler = s.restraints.create_scaler('nonlinear',
alpha_min=0.5,
alpha_max=1.0,
factor=4.0)
calcium_rests = get_dist_restraints('calcium_restraints.dat', s, scaler,
ramp)
n_keep_calcium = len(calcium_rests)
s.restraints.add_selectively_active_collection(calcium_rests,
n_keep_calcium)
#
# PRE restraints
#
scaler_short = s.restraints.create_scaler('nonlinear',
alpha_min=0.6,
alpha_max=1.0,
factor=4.0)
scaler_medium = s.restraints.create_scaler('nonlinear',
alpha_min=0.5,
alpha_max=0.6,
factor=4.0)
scaler_long = s.restraints.create_scaler('nonlinear',
alpha_min=0.4,
alpha_max=0.5,
factor=4.0)
scalers = [scaler_short, scaler_medium, scaler_long]
OND_list = [17, 34, 42, 53, 86, 110, 117, 127, 143, 149]
for ond in OND_list:
for length, i in zip(['short', 'medium', 'long'], range(3)):
scaler = scalers[int(i)]
pre_restraints = get_dist_restraints_pre(
'rest_files/' + str(ond) + '-pre-' + length + '.dat', s,
scaler, ramp)
n_keep_pre = int(len(pre_restraints) * 0.90)
s.restraints.add_selectively_active_collection(
pre_restraints, n_keep_pre)
#
# RDC Restraints
#
rdc_scaler = s.restraints.create_scaler('nonlinear',
alpha_min=0.3,
alpha_max=0.4,
factor=4.0,
strength_at_alpha_max=1.0e-2)
rdc_rests = parse.get_rdc_restraints(system=s,
patcher=rdc_patcher,
scaler=rdc_scaler,
ramp=ramp,
quadratic_cut=1.0,
scale_factor=1.0e4,
filename='rdc.dat')
s.restraints.add_as_always_active_list(rdc_rests)
# create the options
options = system.RunOptions()
options.implicit_solvent_model = 'obc'
options.use_big_timestep = False
options.cutoff = 1.8
options.remove_com = True
options.use_amap = True
options.amap_beta_bias = 1.0
options.timesteps = 25000
options.minimize_steps = 5000
# create a store
store = vault.DataStore(s.n_atoms,
N_REPLICAS,
s.get_pdb_writer(),
block_size=BLOCK_SIZE)
store.initialize(mode='w')
store.save_system(s)
store.save_run_options(options)
# create and store the remd_runner
l = ladder.NearestNeighborLadder(n_trials=48 * 48)
policy_1 = adaptor.AdaptationPolicy(2.0, 50, 50)
a = adaptor.EqualAcceptanceAdaptor(n_replicas=N_REPLICAS,
adaptation_policy=policy_1)
remd_runner = master_runner.MasterReplicaExchangeRunner(N_REPLICAS,
max_steps=N_STEPS,
ladder=l,
adaptor=a)
store.save_remd_runner(remd_runner)
# create and store the communicator
c = comm.MPICommunicator(s.n_atoms, N_REPLICAS)
store.save_communicator(c)
# create and save the initial states
states = [gen_state(s, i) for i in range(N_REPLICAS)]
store.save_states(states, 0)
# save data_store
store.save_data_store()
return s.n_atoms
def setup_system():
# create the system starting from coordinates in template.pdb
templates = glob.glob('%s-sep.pdb' % (sys.argv[1]))
p = system.ProteinMoleculeFromPdbFile(templates[0])
b = system.SystemBuilder(forcefield="ff14sbside")
# load non-standard AA force field params, bonds
s = b.build_system_from_molecules([p])
# Create temperature ladder
s.temperature_scaler = system.GeometricTemperatureScaler(
0.0, 0.5, 300., 500.)
# Keep protein dimer conformation fairly constant
dist_scaler = s.restraints.create_scaler('nonlinear',
alpha_min=0.4,
alpha_max=1.0,
factor=4.0)
const_scaler = s.restraints.create_scaler('constant')
dist = keep_fixed_distance('%s-contacts.dat' % (sys.argv[1]),
s,
scaler=const_scaler)
s.restraints.add_selectively_active_collection(dist, int(len(dist)))
# Keep DNA hbonds
#Read sequence file
sequenceDNA = readSeq('%s-seq.dat' % (sys.argv[1]))
#Generate hbondsDNA.dat
make_hbond_restraint_file(sequenceDNA, 0)
dist_scaler3 = s.restraints.create_scaler('nonlinear',
alpha_min=0.9,
alpha_max=1.0,
factor=4.0)
dist = keep_fixed_distance('hbondsDNA.dat', s, scaler=const_scaler)
s.restraints.add_selectively_active_collection(dist, int(len(dist)))
# Keep DNA close to starting conformation
rest = make_cartesian_collections(s,
const_scaler,
range(1, 43),
atoms=[
"C1'", "C2", "C2'", "C3'", "C4",
"C4'", "C5", "C5'", "C6", "C7", "C8",
"DA3", "N1", "N2", "N3", "N4", "N6",
"N7", "N9", "O2", "O3'", "O4", "O4'",
"O5'", "O6", "OP1", "OP2", "P"
])
# rest = make_cartesian_collections(s, const_scaler, range(1,16),atoms=["C1'", "C2", "C2'", "C3'", "C4", "C4'", "C5", "C5'", "C6", "N1", "N3", "O3'", "O4'"])
#These are the common atoms to all DNA bases including ends:
#C1' C2 C2' C3' C4 C4' C5 C5' C6 N1 N3 O3' O4' O5'
s.restraints.add_as_always_active_list(rest)
# Create Contacts between protein and DNA
dom1 = get_dist_restraints('%s-DNA-contacts.dat' % (sys.argv[1]),
s,
scaler=dist_scaler)
s.restraints.add_selectively_active_collection(dom1, int(len(dom1)))
# Find Glycines and Restrain peptide within reasonable distance from DNA
names = np.array(s.atom_names)
resid = np.array(s.residue_numbers)
# resnames = np.array(s.residue_names)
select = names == 'CB'
non_gly = resid[select]
# scaler3 = s.restraints.create_scaler('nonlinear',alpha_min=0.7,alpha_max=1.0, factor=4.0, strength_at_alpha_min=1.0, strength_at_alpha_max=0.5)
# conf_rest = []
# group1 = []
# group2 = []
# for i in range(2,21):
# group1.append( (i,"O5'") )
# for i in range(22,41):
# group1.append( (i,"O5'") )
# for j in non_gly:
# group2.append( (j,"CB") )
# positioner = s.restraints.create_scaler('linear_positioner',alpha_min=0.7, alpha_max=1.0, pos_min=10., pos_max=15.)
# conf_rest.append(s.restraints.create_restraint('com', scaler3,ramp=LinearRamp(0,100,0,1),
# force_const=75.0,group1=group1,group2=group2,
# distance =positioner,weights1=None, weights2=None, dims='xyz'))
# s.restraints.add_as_always_active_list(conf_rest)
dist_scaler2 = s.restraints.create_scaler('nonlinear',
alpha_min=0.7,
alpha_max=1.0,
factor=4.0)
res_groups = get_distance_rests('%s-res_groups.dat' % (sys.argv[1]),
s,
scaler=dist_scaler2)
s.restraints.add_selectively_active_collection(res_groups,
int(len(res_groups) - 10))
#
# Secondary Structure
#
ss_scaler = s.restraints.create_scaler('constant')
ss_rests = parse.get_secondary_structure_restraints(
filename='%s-ss.dat' % (sys.argv[1]),
system=s,
ramp=LinearRamp(0, 100, 0, 1),
scaler=ss_scaler,
torsion_force_constant=2.5,
distance_force_constant=2.5)
n_ss_keep = int(len(ss_rests) * 0.96)
s.restraints.add_selectively_active_collection(ss_rests, n_ss_keep)
# create the options
options = system.RunOptions()
options.implicit_solvent_model = 'gbNeck2'
options.remove_com = False
options.use_big_timestep = False # MD timestep (3.3 fs)
options.use_bigger_timestep = True # MD timestep (4.0 fs)
options.cutoff = 1.8 # cutoff in nm
options.soluteDielectric = 1.
#options.implicitSolventSaltConc = None
options.use_amap = False # correction to FF12SB
options.amap_beta_bias = 1.0
options.timesteps = 11111 # number of MD steps per exchange
options.minimize_steps = 20000 # init minimization steps
# create a store
store = vault.DataStore(s.n_atoms,
N_REPLICAS,
s.get_pdb_writer(),
block_size=BLOCK_SIZE)
store.initialize(mode='w')
store.save_system(s)
store.save_run_options(options)
# create and store the remd_runner, sets up replica exchange details
l = ladder.NearestNeighborLadder(n_trials=48)
policy = adaptor.AdaptationPolicy(2.0, 50, 50)
a = adaptor.EqualAcceptanceAdaptor(n_replicas=N_REPLICAS,
adaptation_policy=policy)
remd_runner = master_runner.MasterReplicaExchangeRunner(N_REPLICAS,
max_steps=N_STEPS,
ladder=l,
adaptor=a)
store.save_remd_runner(remd_runner)
# create and store the communicator
c = comm.MPICommunicator(s.n_atoms, N_REPLICAS)
store.save_communicator(c)
# create and save the initial states
# create and save the initial states, initialize each replica with a different template
states = [gen_state_templates(i, templates) for i in range(N_REPLICAS)]
store.save_states(states, 0)
# save data_store
store.save_data_store()
return s.n_atoms
def setup_system():
# ECO settings
#eco_cutoff = 0.8 # the distance (in nm) that qualifies as a connection in the graph
eco_cutoff = 1.0 # the distance (in nm) that qualifies as a connection in the graph
doing_eco_hydrophobe = True
doing_eco_hbond = True
doing_eco_2ndary = False
doing_eco_strand_pairing = True
doing_eco_knob = False
doing_eco_evolutionary = False
#eco_factor = 4.0 # the factor by which we multiply the eco energy adjustment
eco_factor = 1 # the factor by which we multiply the eco energy adjustment
eco_constant = 0.0 # In theory, these could be changed for any restraint
eco_linear = 0.0
# load the sequence
sequence = parse.get_sequence_from_AA1(filename='sequence.dat')
n_res = len(sequence.split())
# build the system
p = system.ProteinMoleculeFromSequence(sequence)
b = system.SystemBuilder(forcefield="ff14sbside")
s = b.build_system_from_molecules([p])
s.temperature_scaler = system.GeometricTemperatureScaler(
0, 0.6, 300., 450.)
#
# Secondary Structure
#
ss_scaler = s.restraints.create_scaler('constant')
ss_rests = parse.get_secondary_structure_restraints(
filename='ss.dat',
system=s,
ramp=LinearRamp(0, 100, 0, 1),
scaler=ss_scaler,
torsion_force_constant=2.5,
distance_force_constant=2.5,
doing_eco=doing_eco_2ndary,
eco_factor=eco_factor,
eco_constant=eco_constant,
eco_linear=eco_linear)
n_ss_keep = int(len(ss_rests) * 0.70) #We enforce 70% of restrains
s.restraints.add_selectively_active_collection(ss_rests, n_ss_keep)
#
# Confinement Restraints
#
#conf_scaler = s.restraints.create_scaler('nonlinear', alpha_min=0.4, alpha_max=1.0, factor=4.0,strength_at_alpha_min=0.0, strength_at_alpha_max=1.0)
#confinement_rests = []
#confinement_dist = (16.9*np.log(s.residue_numbers[-1])-15.8)/28.*1.2
#for index in range(n_res):
# rest = s.restraints.create_restraint('confine', conf_scaler, LinearRamp(0,100,0,1),res_index=index+1, atom_name='CA', radius=confinement_dist, force_const=250.0)
# confinement_rests.append(rest)
#s.restraints.add_as_always_active_list(confinement_rests)
#
# Distance Restraints
#
dist_scaler = s.restraints.create_scaler('nonlinear',
alpha_min=0.4,
alpha_max=1.0,
factor=4.0)
# High reliability
#
#
# Create Plateau kind of scalers
#
low_2 = make_CO_scaler(system=s,
scaler_type='plateaunonlinear',
alpha_min=0.70,
alpha_one=0.85,
alpha_two=0.85,
alpha_max=1.0,
strength_at_alpha_min=1.0,
strength_at_alpha_max=0.0)
low_4 = make_CO_scaler(system=s,
scaler_type='plateaunonlinear',
alpha_min=0.55,
alpha_one=0.70,
alpha_two=0.70,
alpha_max=0.85,
strength_at_alpha_min=1.0,
strength_at_alpha_max=0.0)
low_6 = s.restraints.create_scaler('plateaunonlinear',
alpha_min=0.40,
alpha_one=0.55,
alpha_two=0.55,
alpha_max=0.7,
factor=4.0,
strength_at_alpha_min=1.0,
strength_at_alpha_max=0.0)
low_8 = s.restraints.create_scaler('nonlinear',
alpha_min=0.40,
alpha_max=0.55,
factor=4.0)
#
# Heuristic Restraints
#
subset = np.array(range(n_res)) + 1
#
# Hydrophobic
#
create_hydrophobes(s,
ContactsPerHydroph=1.2,
scaler=dist_scaler,
group_1=subset,
CO=False,
doing_eco=doing_eco_hydrophobe,
eco_factor=eco_factor,
eco_constant=eco_constant,
eco_linear=eco_linear)
#create_hydrophobes(s,ContactsPerHydroph=1.2/4.,scaler=low_2,group_1=subset,CO=True)
#create_hydrophobes(s,ContactsPerHydroph=1.2/2.,scaler=low_4,group_1=subset,CO=True)
#create_hydrophobes(s,ContactsPerHydroph=1.2*3/4.,scaler=low_6,group_1=subset,CO=False)
#create_hydrophobes(s,ContactsPerHydroph=1.2,scaler=low_8,group_1=subset,CO=False)
#
# HBonds
#
create_HydrogenBond(s,
HBPerResidue=0.10,
scaler=dist_scaler,
group_1=subset,
CO=False,
doing_eco=doing_eco_hbond,
eco_factor=eco_factor,
eco_constant=eco_constant,
eco_linear=eco_linear)
#create_HydrogenBond(s,HBPerResidue=0.10/4.,scaler=low_2,group_1=subset,CO=True)
#create_HydrogenBond(s,HBPerResidue=0.10/2.,scaler=low_4,group_1=subset,CO=True)
#create_HydrogenBond(s,HBPerResidue=0.10*3/4.,scaler=low_6,group_1=subset,CO=False)
#create_HydrogenBond(s,HBPerResidue=0.10,scaler=low_8,group_1=subset,CO=False)
#
# Strand Pairing
#
sse, active = make_ss_groups(subset=subset)
try:
generate_strand_pairs(s,
sse,
float(active),
subset=subset,
scaler=dist_scaler,
CO=False,
doing_eco=doing_eco_strand_pairing,
eco_factor=eco_factor,
eco_constant=eco_constant,
eco_linear=eco_linear)
#generate_strand_pairs(s,sse,float(active)/4.,subset=subset,scaler=low_2,CO=True)
#generate_strand_pairs(s,sse,float(active)/2.,subset=subset,scaler=low_4,CO=True)
#generate_strand_pairs(s,sse,float(active)*3/4.,subset=subset,scaler=low_6,CO=False)
#generate_strand_pairs(s,sse,float(active),subset=subset,scaler=low_8,CO=False)
except:
print "Not using Strand Pairing Heuristic"
pass
#
# Evolutionary restraints
#
try:
create_Evolution(s,
scaler=dist_scaler,
fname='evolution_contacts.dat',
doing_eco=doing_eco_evolutionary,
eco_factor=eco_factor,
eco_constant=eco_constant,
eco_linear=eco_linear)
except:
print "Not using Evolutionary restraints"
pass
#
# Distance Restraints
#
#
# Knob restraints
#
try:
knobs, knob_accuracy = get_knob_restraints('Knob.data',
s,
scaler=dist_scaler,
doing_eco=doing_eco_knob,
eco_factor=eco_factor,
eco_constant=eco_constant,
eco_linear=eco_linear)
n_knobs = int(len(knobs) * knob_accuracy)
s.restraints.add_selectively_active_collection(knobs, n_knobs)
except:
print "Not using Knob-Socket predictions"
pass
# setup mcmc at startup
movers = []
n_atoms = s.n_atoms
for i in range(1, n_res + 1):
n = s.index_of_atom(i, 'N') - 1
ca = s.index_of_atom(i, 'CA') - 1
c = s.index_of_atom(i, 'C') - 1
mover = mc.DoubleTorsionMover(n, ca, list(range(ca, n_atoms)), ca, c,
list(range(c, n_atoms)))
movers.append((mover, 1))
sched = mc.MonteCarloScheduler(movers, n_res * 60)
# create the options
options = system.RunOptions()
options.implicit_solvent_model = 'gbNeck2'
options.use_big_timestep = False
options.use_bigger_timestep = True
options.cutoff = 1.8
#options.eco_cutoff = eco_cutoff
# set eco_output very high so that log file does not print
options.eco_params = {
'eco_cutoff': 1.0,
'eco_output_freq': 10000000,
'print_avg_eco': False,
'print_eco_value_array': False,
}
options.use_amap = False
options.amap_beta_bias = 1.0
options.timesteps = 11111
options.minimize_steps = 20000
options.min_mc = sched
options.make_alpha_carbon_list(s.atom_names)
print "alpha_carbon_indeces:", options.alpha_carbon_indeces
# create a store
store = vault.DataStore(s.n_atoms,
N_REPLICAS,
s.get_pdb_writer(),
block_size=BLOCK_SIZE)
store.initialize(mode='w')
store.save_system(s)
store.save_run_options(options)
# create and store the remd_runner
l = ladder.NearestNeighborLadder(n_trials=48 * 48)
policy = adaptor.AdaptationPolicy(2.0, 50, 50)
a = adaptor.EqualAcceptanceAdaptor(n_replicas=N_REPLICAS,
adaptation_policy=policy)
remd_runner = master_runner.MasterReplicaExchangeRunner(N_REPLICAS,
max_steps=N_STEPS,
ladder=l,
adaptor=a)
store.save_remd_runner(remd_runner)
# create and store the communicator
c = comm.MPICommunicator(s.n_atoms, N_REPLICAS)
store.save_communicator(c)
# create and save the initial states
states = [gen_state(s, i) for i in range(N_REPLICAS)]
store.save_states(states, 0)
# save data_store
store.save_data_store()
return s.n_atoms
def setup_system():
# load the sequence
sequence = parse.get_sequence_from_AA1(filename='sequence.dat')
n_res = len(sequence.split())
# build the system
p = system.ProteinMoleculeFromSequence(sequence)
b = system.SystemBuilder(forcefield="ff14sbside")
s = b.build_system_from_molecules([p])
s.temperature_scaler = system.GeometricTemperatureScaler(0, 0.6, 300., 450.)
#
# Secondary Structure
#
ss_scaler = s.restraints.create_scaler('constant')
ss_rests = parse.get_secondary_structure_restraints(filename='ss.dat', system=s,ramp=LinearRamp(0,100,0,1), scaler=ss_scaler,
torsion_force_constant=2.5, distance_force_constant=2.5)
n_ss_keep = int(len(ss_rests) * 1.0) #We enforce 100% of restrains
s.restraints.add_selectively_active_collection(ss_rests, n_ss_keep)
#
# Distance Restraints
#
dist_scaler = s.restraints.create_scaler('nonlinear', alpha_min=0.4, alpha_max=1.0, factor=4.0)
# High reliability
#
#
old_protocol = s.restraints.create_scaler('nonlinear', alpha_min=0.40, alpha_max=1.00, factor=4.0)
#
# Heuristic Restraints
#
subset= np.array(list(range(n_res))) + 1
#
# Hydrophobic
#
create_hydrophobes(s,ContactsPerHydroph=1.2,scaler=old_protocol,group_1=subset,CO=False)
#
# Strand Pairing
#
sse,active = make_ss_groups(subset=subset)
try:
generate_strand_pairs(s,sse,float(active),subset=subset,scaler=old_protocol,CO=False)
except:
print("Not using Strand Pairing Heuristic")
pass
#
# Evolutionary restraints
#
try:
create_Evolution(s,accuracy=0.7,scaler=dist_scaler,fname='evolution_contacts.dat')
except:
print("Not using Evolutionary restraints")
pass
#
# Distance Restraints
#
#
# Knob restraints
#
try:
knobs,knob_accuracy = get_knob_restraints('Knob.data',s,scaler=dist_scaler)
n_knobs = int(len(knobs) * knob_accuracy)
s.restraints.add_selectively_active_collection(knobs,n_knobs)
except:
print("Not using Knob-Socket predictions")
pass
# setup mcmc at startup
movers = []
n_atoms = s.n_atoms
for i in range(1, n_res + 1):
n = s.index_of_atom(i, 'N') - 1
ca = s.index_of_atom(i, 'CA') - 1
c = s.index_of_atom(i, 'C') - 1
mover = mc.DoubleTorsionMover(n, ca, list(range(ca, n_atoms)),
ca, c, list(range(c, n_atoms)))
movers.append((mover, 1))
sched = mc.MonteCarloScheduler(movers, n_res * 60)
# create the options
options = system.RunOptions()
options.implicit_solvent_model = 'gbNeck2'
options.use_big_timestep = False
options.use_bigger_timestep = True
options.cutoff = 1.8
options.use_amap = False
options.amap_alpha_bias = 1.0
options.amap_beta_bias = 1.0
options.timesteps = 11111
options.minimize_steps = 20000
# for i in range(30):
# print("Heads up! using MC minimizer!")
# options.min_mc = sched
# create a store
store = vault.DataStore(s.n_atoms, N_REPLICAS, s.get_pdb_writer(), block_size=BLOCK_SIZE)
store.initialize(mode='w')
store.save_system(s)
store.save_run_options(options)
# create and store the remd_runner
l = ladder.NearestNeighborLadder(n_trials=100)
policy = adaptor.AdaptationPolicy(2.0, 50, 50)
a = adaptor.EqualAcceptanceAdaptor(n_replicas=N_REPLICAS, adaptation_policy=policy)
remd_runner = master_runner.MasterReplicaExchangeRunner(N_REPLICAS, max_steps=N_STEPS, ladder=l, adaptor=a)
store.save_remd_runner(remd_runner)
# create and store the communicator
c = comm.MPICommunicator(s.n_atoms, N_REPLICAS)
store.save_communicator(c)
# create and save the initial states
states = [gen_state(s, i) for i in range(N_REPLICAS)]
store.save_states(states, 0)
# save data_store
store.save_data_store()
return s.n_atoms
