def Optimizemodel(pdb_file):
"""
This functions returns a file with the optimized model from the input pdb.
It also returns the energies.
"""
env = environ()
env.io.atom_files_directory = ['../atom_files']
env.edat.dynamic_sphere = True
env.libs.topology.read(file='$(LIB)/top_heav.lib')
env.libs.parameters.read(file='$(LIB)/par.lib')
code, ext = pdb_file.split('.')
mdl = complete_pdb(env, pdb_file)
mdl.write(file=code + '.ini')
# Select all atoms:
atmsel = selection(mdl)
mpdf2 = atmsel.energy()
# Generate the restraints:
#mdl.restraints.make(atmsel, restraint_type='improper', spline_on_site=False)
#mdl.restraints.make(atmsel, restraint_type='bond', spline_on_site=False)
#mdl.restraints.make(atmsel, restraint_type='sphere', spline_on_site=False)
mdl.restraints.make(atmsel, restraint_type='stereo', spline_on_site=False)
mdl.restraints.write(file=code + '.rsr')
mpdf1 = atmsel.energy()
# Create optimizer objects and set defaults for all further optimizations
cg = conjugate_gradients(output='REPORT')
md = molecular_dynamics(output='REPORT')
# Open a file to get basic stats on each optimization
trcfil = open(code + '.D00000001', 'w')
# Run CG on the all-atom selection; write stats every 5 steps
cg.optimize(atmsel, max_iterations=20, actions=actions.trace(5, trcfil))
# Run MD; write out a PDB structure (called '1fas.D9999xxxx.pdb') every
# 10 steps during the run, and write stats every 10 steps
md.optimize(atmsel,
temperature=300,
max_iterations=50,
actions=[
actions.write_structure(10, code + '.D9999%04d.pdb'),
actions.trace(10, trcfil)
])
#refine(atmsel, code, trcfil)
# Finish off with some more CG, and write stats every 5 steps
cg.optimize(atmsel, max_iterations=20, actions=[actions.trace(5, trcfil)])
mpdf = atmsel.energy()
print("The initial energy of " + code + " is " + str(mpdf1[0]))
print("The final energy of " + code + " is " + str(mpdf[0]))
print("The final energy of " + code + " is " + str(mpdf2[0]))
mdl.write(file=code + '_optimized.pdb')
def optimize(pdb, pdb_path):
print(1, pdb_path)
# Environ data
env = environ(0)
env.io.atom_files_directory = ['../atom_files']
env.edat.dynamic_sphere = True
env.libs.topology.read(file='$(LIB)/top_heav.lib')
env.libs.parameters.read(file='$(LIB)/par.lib')
code = pdb.split('.')[0]
mdl = complete_pdb(env, pdb)
mdl.write(file=code+'.ini')
# Select all atoms:
atmsel = selection(mdl)
# Generate the restraints:
mdl.restraints.make(atmsel, restraint_type='stereo', spline_on_site=False)
mdl.restraints.write(file=code+'.rsr')
mpdf_prior = atmsel.energy()
# Create optimizer objects and set defaults for all further optimizations
cg = conjugate_gradients(output='REPORT')
md = molecular_dynamics(output='REPORT')
# Open a file to get basic stats on each optimization
trcfil = open(code+'.D00000001', 'w')
# Run CG on the all-atom selection; write stats every 5 steps
cg.optimize(atmsel, max_iterations=20, actions=actions.trace(5, trcfil))
# Run MD; write out a PDB structure (called '1fas.D9999xxxx.pdb') every
# 10 steps during the run, and write stats every 10 steps
md.optimize(atmsel, temperature=300, max_iterations=50,
actions=[actions.write_structure(10, code+'.D9999%04d.pdb'),
actions.trace(10, trcfil)])
# Finish off with some more CG, and write stats every 5 steps
cg.optimize(atmsel, max_iterations=20,
actions=[actions.trace(5, trcfil)])
mpdf_after = atmsel.energy()
mdl.write(file=os.path.join(pdb_path, 'optimized.pdb'))
return (mpdf_prior, mpdf_after)
def refine(atmsel, code, trcfil):
# at T=1000, max_atom_shift for 4fs is cca 0.15 A.
md = molecular_dynamics(cap_atom_shift=0.39,
md_time_step=4.0,
md_return='FINAL')
init_vel = True
for (its, equil,
temps) in ((200, 20, (150.0, 250.0, 400.0, 700.0, 1000.0)),
(200, 600, (1000.0, 800.0, 600.0, 500.0, 400.0, 300.0))):
for temp in temps:
md.optimize(atmsel,
init_velocities=init_vel,
temperature=temp,
max_iterations=its,
equilibrate=equil,
actions=[
actions.write_structure(10,
code + '.D9999%04d.pdb'),
actions.trace(10, trcfil)
])
init_vel = False
# Select all atoms:
atmsel = selection(mdl)
# Generate the restraints:
mdl.restraints.make(atmsel, restraint_type='stereo', spline_on_site=False)
mdl.restraints.write(file=code+'.rsr')
mpdf = atmsel.energy()
# Create optimizer objects and set defaults for all further optimizations
cg = conjugate_gradients(output='REPORT')
md = molecular_dynamics(output='REPORT')
# Open a file to get basic stats on each optimization
trcfil = file(code+'.D00000001', 'w')
# Run CG on the all-atom selection; write stats every 5 steps
cg.optimize(atmsel, max_iterations=20, actions=actions.trace(5, trcfil))
# Run MD; write out a PDB structure (called '1fas.D9999xxxx.pdb') every
# 10 steps during the run, and write stats every 10 steps
md.optimize(atmsel, temperature=300, max_iterations=50,
actions=[actions.write_structure(10, code+'.D9999%04d.pdb'),
actions.trace(10, trcfil)])
# Finish off with some more CG, and write stats every 5 steps
cg.optimize(atmsel, max_iterations=20,
actions=[actions.trace(5, trcfil)])
mpdf = atmsel.energy()
mdl.write(file=code+'.B')
dih_lib_only=True)
mdl.restraints.make(all_atoms, aln=aln,
restraint_type='PHI_DIHEDRAL',spline_on_site=True,
dih_lib_only=True)
mdl.restraints.make(all_atoms, aln=aln,
restraint_type='PSI_DIHEDRAL',spline_on_site=True,
dih_lib_only=True)
mdl.restraints.condense()
# energy data
env.edat.dynamic_lennard = True
# Prepare optimizer
scaling_factors = physical.values(default=1.0, em_density=0)
MD = molecular_dynamics(output='REPORT',cap_atom_shift=0.0001,
md_time_step=0.05,temperature=params.temperature,
init_velocities=True,md_return=params.md_return,
equilibrate = params.equilibrate,
schedule_scale=scaling_factors)
w=actions.write_structure(params.skip_its_for_writing, 'md-%03d.pdb',
write_all_atoms=True, first=True, last=True, start=0)
# optimize
MD.optimize(all_atoms, max_iterations=params.max_iterations,actions=[w])
for c in mdl.chains:
c.name = 'A'
mdl.remark =""
mdl.reorder_atoms()
mdl.write(params.output_pdb_file, model_format='PDB')
# Open a file to get basic stats on each optimization
trcfil = file(query_id + '.D00000001', 'w')
# Run CG on the all-atom selection; write stats every 5 steps
cg.optimize(atmsel,
max_iterations=20,
actions=actions.trace(5, trcfil),
min_atom_shift=0.01)
# Run MD; write out a PDB structure (called '1fas.D9999xxxx.pdb') every
# 10 steps during the run, and write stats every 10 steps
md.optimize(atmsel,
temperature=300,
max_iterations=50,
actions=[
actions.write_structure(10, query_id + '.D9998%04d.pdb'),
actions.trace(10, trcfil)
])
# Finish off with some more CG, and write stats every 5 steps
cg.optimize(atmsel, max_iterations=20, actions=[actions.trace(5, trcfil)])
mpdf = atmsel.energy()
mdl.write(file=query_id + '.D00000001.pdb')
contacts = parse_contacts.parse(open(contact_filename, 'r'))
count = 0
seq_len = len(aln[query_id])
for (score, i, j) in contacts:
rsr.add(
forms.gaussian(group=physical.xy_distance,
def Optimizemodel(pdb_file):
"""
It creates a PDB file with the optimized model from the input pdb,
with its energies and restraint contributions. Also it will create pdbs on
every step of the Molecular Dynamics optimization. The energy is returned as
the total value of Modeller's objective function, molpdf.
It also shows the topt 10 contributors to the molpdf before and after optimization
"""
# Setting up
env = environ()
env.io.atom_files_directory = ['../atom_files']
env.edat.dynamic_sphere = True
env.libs.topology.read(file='$(LIB)/top_heav.lib')
env.libs.parameters.read(file='$(LIB)/par.lib')
code, ext = pdb_file.split('.')
# Complete the pdb and make a model
mdl = complete_pdb(env, pdb_file)
mdl.write(file=code + '.ini')
# Select all atoms from the model, make restraints and save them in a file
atmsel = selection(mdl)
mdl.restraints.make(atmsel, restraint_type='stereo', spline_on_site=False)
mdl.restraints.write(file=code + '.rsr')
# Check the energy before optimization and save it in a var
initial_mpdf = atmsel.energy()
# Create optimizer objects
cg = conjugate_gradients(output='REPORT')
md = molecular_dynamics(output='REPORT')
# Open a file to get basic stats on each optimization
stats_file = open(code + '_opt.stats', 'w')
# Run MD. Write out a PDB structure every 10 steps during the run.
# Write stats every 10 steps
md.optimize(atmsel,
temperature=300,
max_iterations=50,
actions=[
actions.write_structure(10, code + '.MD%04d.pdb'),
actions.trace(10, stats_file)
])
# Run CG, and write stats every 5 steps
cg.optimize(atmsel,
max_iterations=50,
actions=[actions.trace(5, stats_file)])
# Final energy
final_mpdf = atmsel.energy()
# Assess DOPE
atmsel.assess_dope(output='ENERGY_PROFILE NO_REPORT',
file='TvLDH.profile',
normalize_profile=True,
smoothing_window=15)
# Print the energies and the contributions
initial_cont_all = dict(initial_mpdf[1])
top_init_conts = dict(
sorted(initial_cont_all.items(), key=itemgetter(1), reverse=True)[:5])
l.info("\n\nThe initial energy of " + code + " is " + str(initial_mpdf[0]))
print("\n\nThe initial energy of " + code + " is " + str(initial_mpdf[0]))
print("The top 10 initial contributions the restraints are:\n")
for keys, values in top_init_conts.items():
print(keys, ":", values)
final_cont_all = dict(final_mpdf[1])
top_final_conts = dict(
sorted(final_cont_all.items(), key=itemgetter(1), reverse=True)[:5])
l.info("\n\nThe final energy of " + code + " is " + str(final_mpdf[0]))
print("\n\nThe final energy of " + code + " is " + str(final_mpdf[0]))
print("Final contributions the restraints are:\n")
for keys, values in top_final_conts.items():
print(keys, ":", values)
mdl.write(file=code + '_optimized.pdb')
def optimization(pdb_file, options):
"""This function needs as an input a PDB file (the model), and gives as an output the optimized model."""
env = environ()
env.io.atom_files_directory = ['../atom_files']
env.edat.dynamic_sphere = True
env.libs.topology.read(file='$(LIB)/top_heav.lib')
env.libs.parameters.read(file='$(LIB)/par.lib')
path, ext = pdb_file.split('.')
list = path.split('/')
#if results/4g83/4g83model.pdb list[0]=results list[1]=4g83 list[2]=4g83model
dir = list[0] + "/" + list[1]
code = list[2]
mdl = complete_pdb(env, pdb_file)
mdl.write(file=path + '.ini')
atmsel = selection(mdl)
mpdf_ini = atmsel.energy()
z_score_ini = mdl.assess_normalized_dope()
mdl_ep_ini = atmsel.get_dope_profile()
mdl_ep_ini_smoothed = mdl_ep_ini.get_smoothed()
energy_profile_txt_path = dir + '/' + code + '_DOPE_EnergyProfile.txt'
mdl_ep_ini_smoothed.write_to_file(energy_profile_txt_path)
print("The unoptimized model's energy of " + code + " is: " +
str(mpdf_ini[0]))
print("The unoptimized Z-score of " + code + " is: " + str(z_score_ini))
energy_profile_txt_path_opt = None
if options.optimize:
cg = conjugate_gradients(output='REPORT')
md = molecular_dynamics(output='REPORT')
trcfil = open(path + '.D00000001', 'w')
cg.optimize(atmsel,
max_iterations=20,
actions=actions.trace(5, trcfil))
md.optimize(atmsel,
temperature=300,
max_iterations=50,
actions=[
actions.write_structure(10, path + '.D9999%04d.pdb'),
actions.trace(10, trcfil)
])
cg.optimize(atmsel,
max_iterations=20,
actions=[actions.trace(5, trcfil)])
mpdf = atmsel.energy()
z_score = mdl.assess_normalized_dope()
print("The final model energy of " + path + " is " + str(mpdf[0]))
print("The final model energy of " + path + " is " + str(mpdf_ini[0]))
print("The final z-score of " + code + " is: " + str(z_score))
mdl.write(file=path + '_optimized.pdb')
mdl_final = atmsel.get_dope_profile()
mdl_final_smoothed = mdl_final.get_smoothed(window=50)
energy_profile_txt_path_opt = dir + '/' + code + '_optimized_DOPE_EnergyProfile.txt'
mdl_final_smoothed.write_to_file(energy_profile_txt_path_opt)
mdl.write(file=dir + '/' + code + '_optimized.pdb')
energy_profile_plot(options, dir, code, energy_profile_txt_path,
energy_profile_txt_path_opt)
