def test_grompp(self):
returncode = Grompp(properties=self.properties, **self.paths).launch()
assert fx.not_empty(self.paths['output_tpr_path'])
assert gmx_check(self.paths['output_tpr_path'],
self.paths['ref_output_tpr_path'],
gmx=self.properties.get('gmx_path', 'gmx'))
assert fx.exe_success(returncode)
def _grompp(input_gro_path, input_top_zip_path, output_tpr_path,
input_cpt_path, input_ndx_path, input_mdp_path, properties,
**kwargs):
task_config.pop_pmi(os.environ)
try:
Grompp(input_gro_path=input_gro_path,
input_top_zip_path=input_top_zip_path,
output_tpr_path=output_tpr_path,
input_cpt_path=input_cpt_path,
input_ndx_path=input_ndx_path,
input_mdp_path=input_mdp_path,
properties=properties,
**kwargs).launch()
except Exception as e:
traceback.print_exc()
raise e
finally:
sys.stdout.flush()
sys.stderr.flush()
# Create the [portable binary run file (.tpr)](http://manual.gromacs.org/documentation/2019/reference-manual/file-formats.html#tpr) for ion generation, using the Grompp module. The main default parameters for this execution are:
# - integrator = steep ; Algorithm (steep = steepest descent minimization)
# - emtol = 1000.0 ; Stop minimization when the maximum force < 1000.0 kJ/mol/nm
# - emstep = 0.01 ; Minimization step size
# - nsteps = 50000 ; Maximum number of (minimization) steps to perform
#
# The main output of this BB will be the portable binary run file (.tpr).
# Grompp: Creating portable binary run file for ion generation
from biobb_md.gromacs.grompp import Grompp
# Create prop dict and inputs/outputs
prop = {'mdp': {'type': 'minimization', 'nsteps': '5000'}}
output_gppion_tpr_path = base_path + '/gppion.tpr'
#Create and launch bb
Grompp(input_gro_path=output_solvate_gro_path,
input_top_zip_path=output_solvate_top_zip_path,
output_tpr_path=output_gppion_tpr_path,
properties=prop).launch()
# ## Ion generation
# Replace solvent molecules to neutralice the system and then reach a 0.05 mol/litre concentration by default. Using the Genion module.
# The main output of this BB will be an updated post-procesed Gromacs structure file (.gro) and a zip file containing the updated topology file (.top) and the restriction files (.itp).
# Genion: Adding ions to reach a 0.05 nm concentration
from biobb_md.gromacs.genion import Genion
# Create prop dict and inputs/outputs
prop = {'neutral': True, 'concentration': 0.05}
output_genion_gro_path = base_path + '/genion.gro'
output_genion_top_zip_path = base_path + '/genion_top.zip'
#Create and launch bb
Genion(input_tpr_path=output_gppion_tpr_path,
output_gro_path=output_genion_gro_path,
def main(config, system=None):
start_time = time.time()
conf = settings.ConfReader(config, system)
global_log, _ = fu.get_logs(path=conf.get_working_dir_path(),
light_format=True)
global_prop = conf.get_prop_dic(global_log=global_log)
global_paths = conf.get_paths_dic()
dhdl_paths_listA = []
dhdl_paths_listB = []
for ensemble, mutation in conf.properties['mutations'].items():
ensemble_prop = conf.get_prop_dic(prefix=ensemble,
global_log=global_log)
ensemble_paths = conf.get_paths_dic(prefix=ensemble)
#Create and launch bb
global_log.info(
ensemble +
" Step 0: gmx trjconv: Extract snapshots from equilibrium trajectories"
)
ensemble_paths['step0_trjconv']['input_traj_path'] = conf.properties[
'input_trajs'][ensemble]['input_traj_path']
ensemble_paths['step0_trjconv']['input_top_path'] = conf.properties[
'input_trajs'][ensemble]['input_tpr_path']
GMXTrjConvStrEns(**ensemble_paths["step0_trjconv"],
properties=ensemble_prop["step0_trjconv"]).launch()
with zipfile.ZipFile(
ensemble_paths["step0_trjconv"]["output_str_ens_path"],
'r') as zip_f:
zip_f.extractall()
state_pdb_list = zip_f.namelist()
for pdb_path in state_pdb_list:
pdb_name = os.path.splitext(pdb_path)[0]
prop = conf.get_prop_dic(prefix=os.path.join(ensemble, pdb_name),
global_log=global_log)
paths = conf.get_paths_dic(prefix=os.path.join(ensemble, pdb_name))
#Create and launch bb
global_log.info("Step 1: pmx mutate: Generate Hybrid Structure")
paths['step1_pmx_mutate']['input_structure_path'] = pdb_path
prop['step1_pmx_mutate']['mutation_list'] = mutation
Mutate(**paths["step1_pmx_mutate"],
properties=prop["step1_pmx_mutate"]).launch()
# Step 2: gmx pdb2gmx: Generate Topology
# From pmx tutorial:
# gmx pdb2gmx -f mut.pdb -ff amber99sb-star-ildn-mut -water tip3p -o pdb2gmx.pdb
global_log.info("Step 2: gmx pdb2gmx: Generate Topology")
Pdb2gmx(**paths["step2_gmx_pdb2gmx"],
properties=prop["step2_gmx_pdb2gmx"]).launch()
# Step 3: pmx gentop: Generate Hybrid Topology
# From pmx tutorial:
# python generate_hybrid_topology.py -itp topol_Protein.itp -o topol_Protein.itp -ff amber99sb-star-ildn-mut
global_log.info("Step 3: pmx gentop: Generate Hybrid Topology")
Gentop(**paths["step3_pmx_gentop"],
properties=prop["step3_pmx_gentop"]).launch()
# Step 4: gmx make_ndx: Generate Gromacs Index File to select atoms to freeze
# From pmx tutorial:
# echo -e "a D*\n0 & ! 19\nname 20 FREEZE\nq\n" | gmx make_ndx -f frame0/pdb2gmx.pdb -o index.ndx
global_log.info(
"Step 4: gmx make_ndx: Generate Gromacs Index file to select atoms to freeze"
)
MakeNdx(**paths["step4_gmx_makendx"],
properties=prop["step4_gmx_makendx"]).launch()
if ensemble == 'stateA':
# In stateA, with lamdda=0, we don't need the energy minimization step, so simply get the output
# from the step2 (pdb2gmx) as output from the step6 (energy minimization)
# From pmx tutorial:
# There are no dummies in this state at lambda=0, therefore simply convert mut.pdb to emout.gro
paths['step7_gmx_grompp']['input_gro_path'] = paths[
'step2_gmx_pdb2gmx']['output_gro_path']
elif ensemble == 'stateB':
# Step 5: gmx grompp: Creating portable binary run file for energy minimization
# From pmx tutorial:
# gmx grompp -c pdb2gmx.pdb -p topol.top -f ../../mdp/em_FREEZE.mdp -o em.tpr -n ../index.ndx
global_log.info(
"Step 5: gmx grompp: Creating portable binary run file for energy minimization"
)
Grompp(**paths["step5_gmx_grompp"],
properties=prop["step5_gmx_grompp"]).launch()
# Step 6: gmx mdrun: Running energy minimization
# From pmx tutorial:
# gmx mdrun -s em.tpr -c emout.gro -v
global_log.info(
ensemble +
" Step 6: gmx mdrun: Running energy minimization")
Mdrun(**paths["step6_gmx_mdrun"],
properties=prop["step6_gmx_mdrun"]).launch()
# Step 7: gmx grompp: Creating portable binary run file for system equilibration
# From pmx tutorial:
# gmx grompp -c emout.gro -p topol.top -f ../../mdp/eq_20ps.mdp -o eq_20ps.tpr -maxwarn 1
global_log.info(
ensemble +
" Step 7: gmx grompp: Creating portable binary run file for system equilibration"
)
Grompp(**paths["step7_gmx_grompp"],
properties=prop["step7_gmx_grompp"]).launch()
# Step 8: gmx mdrun: Running system equilibration
# From pmx tutorial:
# gmx mdrun -s eq_20ps.tpr -c eqout.gro -v
global_log.info(ensemble +
" Step 8: gmx mdrun: Running system equilibration")
Mdrun(**paths["step8_gmx_mdrun"],
properties=prop["step8_gmx_mdrun"]).launch()
# Step 9: gmx grompp: Creating portable binary run file for thermodynamic integration (ti)
# From pmx tutorial:
# gmx grompp -c eqout.gro -p topol.top -f ../../mdp/ti.mdp -o ti.tpr -maxwarn 1
global_log.info(
ensemble +
" Step 9: Creating portable binary run file for thermodynamic integration (ti)"
)
Grompp(**paths["step9_gmx_grompp"],
properties=prop["step9_gmx_grompp"]).launch()
# Step 10: gmx mdrun: Running thermodynamic integration
# From pmx tutorial:
# gmx mdrun -s ti.tpr -c eqout.gro -v
global_log.info(
ensemble +
" Step 10: gmx mdrun: Running thermodynamic integration")
Mdrun(**paths["step10_gmx_mdrun"],
properties=prop["step10_gmx_mdrun"]).launch()
if ensemble == "stateA":
dhdl_paths_listA.append(
paths["step10_gmx_mdrun"]["output_dhdl_path"])
elif ensemble == "stateB":
dhdl_paths_listB.append(
paths["step10_gmx_mdrun"]["output_dhdl_path"])
#Creating zip file containing all the dhdl files
dhdlA_path = 'dhdlA.zip'
dhdlB_path = 'dhdlB.zip'
fu.zip_list(dhdlA_path, dhdl_paths_listA)
fu.zip_list(dhdlB_path, dhdl_paths_listB)
# Step 11: pmx analyse: Calculate free energies from fast growth thermodynamic integration simulations
# From pmx tutorial:
# python analyze_dhdl.py -fA ../stateA/frame*/dhdl*.xvg -fB ../stateB/frame*/dhdl*.xvg --nbins 25 -t 293 --reverseB
global_log.info(
ensemble +
" Step 11: pmx analyse: Calculate free energies from fast growth thermodynamic integration simulations"
)
global_paths["step11_pmx_analyse"]["input_A_xvg_zip_path"] = dhdlA_path
global_paths["step11_pmx_analyse"]["input_B_xvg_zip_path"] = dhdlB_path
Analyse(**global_paths["step11_pmx_analyse"],
properties=global_prop["step11_pmx_analyse"]).launch()
elapsed_time = time.time() - start_time
global_log.info('')
global_log.info('')
global_log.info('Execution successful: ')
global_log.info(' Workflow_path: %s' % conf.get_working_dir_path())
global_log.info(' Config File: %s' % config)
if system:
global_log.info(' System: %s' % system)
global_log.info('')
global_log.info('Elapsed time: %.1f minutes' % (elapsed_time / 60))
global_log.info('')
def main(config, system=None):
start_time = time.time()
conf = settings.ConfReader(config, system)
global_log, _ = fu.get_logs(path=conf.get_working_dir_path(), light_format=True)
global_prop = conf.get_prop_dic(global_log=global_log)
global_paths = conf.get_paths_dic()
initial_structure = conf.properties.get('initial_structure')
if initial_structure:
global_paths["step2_fixsidechain"]['input_pdb_path'] = initial_structure
else:
global_log.info("step1_mmbpdb: Dowload the initial Structure")
Pdb(**global_paths["step1_mmbpdb"], properties=global_prop["step1_mmbpdb"]).launch()
global_log.info("step2_fixsidechain: Modeling the missing heavy atoms in the structure side chains")
FixSideChain(**global_paths["step2_fixsidechain"], properties=global_prop["step2_fixsidechain"]).launch()
for mutation_number, mutation in enumerate(conf.properties['mutations']):
global_log.info('')
global_log.info("Mutation: %s %d/%d" % (mutation, mutation_number+1, len(conf.properties['mutations'])))
global_log.info('')
prop = conf.get_prop_dic(prefix=mutation, global_log=global_log)
paths = conf.get_paths_dic(prefix=mutation)
global_log.info("step3_mutate Modeling mutation")
prop['step3_mutate']['mutation_list'] = mutation
paths['step3_mutate']['input_pdb_path'] = global_paths['step2_fixsidechain']['output_pdb_path']
Mutate(**paths["step3_mutate"], properties=prop["step3_mutate"]).launch()
global_log.info("step4_pdb2gmx: Generate the topology")
Pdb2gmx(**paths["step4_pdb2gmx"], properties=prop["step4_pdb2gmx"]).launch()
global_log.info("step5_editconf: Create the solvent box")
Editconf(**paths["step5_editconf"], properties=prop["step5_editconf"]).launch()
global_log.info("step6_solvate: Fill the solvent box with water molecules")
Solvate(**paths["step6_solvate"], properties=prop["step6_solvate"]).launch()
global_log.info("step7_grompp_genion: Preprocess ion generation")
Grompp(**paths["step7_grompp_genion"], properties=prop["step7_grompp_genion"]).launch()
global_log.info("step8_genion: Ion generation")
Genion(**paths["step8_genion"], properties=prop["step8_genion"]).launch()
global_log.info("step9_grompp_min: Preprocess energy minimization")
Grompp(**paths["step9_grompp_min"], properties=prop["step9_grompp_min"]).launch()
global_log.info("step10_mdrun_min: Execute energy minimization")
Mdrun(**paths["step10_mdrun_min"], properties=prop["step10_mdrun_min"]).launch()
global_log.info("step11_grompp_nvt: Preprocess system temperature equilibration")
Grompp(**paths["step11_grompp_nvt"], properties=prop["step11_grompp_nvt"]).launch()
global_log.info("step12_mdrun_nvt: Execute system temperature equilibration")
Mdrun(**paths["step12_mdrun_nvt"], properties=prop["step12_mdrun_nvt"]).launch()
global_log.info("step13_grompp_npt: Preprocess system pressure equilibration")
Grompp(**paths["step13_grompp_npt"], properties=prop["step13_grompp_npt"]).launch()
global_log.info("step14_mdrun_npt: Execute system pressure equilibration")
Mdrun(**paths["step14_mdrun_npt"], properties=prop["step14_mdrun_npt"]).launch()
global_log.info("step15_grompp_md: Preprocess free dynamics")
Grompp(**paths["step15_grompp_md"], properties=prop["step15_grompp_md"]).launch()
global_log.info("step16_mdrun_md: Execute free molecular dynamics simulation")
Mdrun(**paths["step16_mdrun_md"], properties=prop["step16_mdrun_md"]).launch()
elapsed_time = time.time() - start_time
global_log.info('')
global_log.info('')
global_log.info('Execution sucessful: ')
global_log.info(' Workflow_path: %s' % conf.get_working_dir_path())
global_log.info(' Config File: %s' % config)
if system:
global_log.info(' System: %s' % system)
global_log.info('')
global_log.info('Elapsed time: %.1f minutes' % (elapsed_time/60))
global_log.info('')