resampler = WExploreResampler(distance=distance, init_state=init_state, max_region_sizes=MAX_REGION_SIZES, max_n_regions=MAX_N_REGIONS, pmin=PMIN, pmax=PMAX) ## Boundary Conditions # the mdtraj here is needed for the distance function mdtraj_topology = mdj.Topology.from_openmm(test_sys.topology) # initialize the unbinding boundary conditions ubc = UnbindingBC(cutoff_distance=CUTOFF_DISTANCE, initial_state=init_state, topology=mdtraj_topology, ligand_idxs=np.array(test_sys.ligand_indices), receptor_idxs=np.array(test_sys.receptor_indices)) ## Reporters json_str_top = mdtraj_to_json_topology(mdtraj_topology) # make a dictionary of units for adding to the HDF5 units = dict(UNIT_NAMES) # open it in truncate mode first, then switch after first run hdf5_reporter = WepyHDF5Reporter( hdf5_path, mode='w', save_fields=SAVE_FIELDS, resampler=resampler,
# make a Wexplore resampler with default parameters and our # distance metric RESAMPLER = WExploreResampler(distance=DISTANCE_METRIC, init_state=INIT_STATE, max_n_regions=MAX_N_REGIONS, max_region_sizes=MAX_REGION_SIZES, pmin=PMIN, pmax=PMAX) ## Boundary Conditions # makes ref_traj and selects lingand_atom and protein atom indices # instantiate a revo unbindingboudaryconditiobs BC = UnbindingBC(cutoff_distance=CUTOFF_DISTANCE, initial_state=INIT_STATE, topology=TOP_MDTRAJ, ligand_idxs=LIG_IDXS, receptor_idxs=PROT_IDXS) APPARATUS = WepySimApparatus(RUNNER, resampler=RESAMPLER, boundary_conditions=BC) print("created apparatus") ## CONFIGURATION # REPORTERS # list of reporter classes and partial kwargs for using in the # orchestrator
def main(n_runs, n_cycles, steps, n_walkers, n_workers=1, debug_prints=False, seed=None): ## Load objects needed for various purposes # load a json string of the topology with open(json_top_path, mode='r') as rf: sEH_TPPU_system_top_json = rf.read() # an openmm.State object for setting the initial walkers up with open(omm_state_path, mode='rb') as rf: omm_state = pickle.load(rf) ## set up the OpenMM Runner # load the psf which is needed for making a system in OpenMM with # CHARMM force fields psf = omma.CharmmPsfFile(charmm_psf_path) # set the box size lengths and angles lengths = [CUBE_LENGTH for i in range(3)] angles = [CUBE_ANGLE for i in range(3)] psf.setBox(*lengths, *angles) # charmm forcefields parameters params = omma.CharmmParameterSet(*charmm_param_paths) # create a system using the topology method giving it a topology and # the method for calculation system = psf.createSystem(params, nonbondedMethod=omma.CutoffPeriodic, nonbondedCutoff=NONBONDED_CUTOFF, constraints=omma.HBonds) # make this a constant temperature and pressure simulation at 1.0 # atm, 300 K, with volume move attempts every 50 steps barostat = omm.MonteCarloBarostat(PRESSURE, TEMPERATURE, VOLUME_MOVE_FREQ) # add it as a "Force" to the system system.addForce(barostat) # make an integrator object that is constant temperature integrator = omm.LangevinIntegrator(TEMPERATURE, FRICTION_COEFFICIENT, STEP_SIZE) # set up the OpenMMRunner with the system runner = OpenMMRunner(system, psf.topology, integrator, platform=PLATFORM) # the initial state, which is used as reference for many things init_state = OpenMMState(omm_state) ## Make the distance Metric # load the crystal structure coordinates crystal_traj = mdj.load_pdb(pdb_path) # get the atoms in the binding site according to the crystal structure bs_idxs = binding_site_atoms(crystal_traj.top, LIG_RESID, crystal_traj.xyz[0]) lig_idxs = ligand_idxs(crystal_traj.top, LIG_RESID) prot_idxs = protein_idxs(crystal_traj.top) # make the distance metric with the ligand and binding site # indices for selecting atoms for the image and for doing the # alignments to only the binding site. All images will be aligned # to the reference initial state unb_distance = UnbindingDistance(lig_idxs, bs_idxs, init_state) ## Make the resampler # make a Wexplore resampler with default parameters and our # distance metric resampler = WExploreResampler(distance=unb_distance, init_state=init_state, max_n_regions=MAX_N_REGIONS, max_region_sizes=MAX_REGION_SIZES, pmin=PMIN, pmax=PMAX) ## Make the Boundary Conditions # makes ref_traj and selects lingand_atom and protein atom indices # instantiate a revo unbindingboudaryconditiobs ubc = UnbindingBC(cutoff_distance=CUTOFF_DISTANCE, initial_state=init_state, topology=crystal_traj.topology, ligand_idxs=lig_idxs, receptor_idxs=prot_idxs) ## make the reporters # WepyHDF5 # make a dictionary of units for adding to the HDF5 # open it in truncate mode first, then switch after first run hdf5_reporter = WepyHDF5Reporter( hdf5_path, mode='w', # the fields of the State that will be saved in the HDF5 file save_fields=SAVE_FIELDS, # the topology in a JSON format topology=sEH_TPPU_system_top_json, # the resampler and boundary # conditions for getting data # types and shapes for saving resampler=resampler, boundary_conditions=ubc, # the units to save the fields in units=dict(UNITS), # sparse (in time) fields sparse_fields=dict(SPARSE_FIELDS), # sparse atoms fields main_rep_idxs=np.concatenate((lig_idxs, prot_idxs)), all_atoms_rep_freq=ALL_ATOMS_SAVE_FREQ) dashboard_reporter = WExploreDashboardReporter( dashboard_path, mode='w', step_time=STEP_SIZE.value_in_unit(unit.second), max_n_regions=resampler.max_n_regions, max_region_sizes=resampler.max_region_sizes, bc_cutoff_distance=ubc.cutoff_distance) setup_reporter = SetupReporter(setup_state_path, mode='w') restart_reporter = RestartReporter(restart_state_path, mode='w') reporters = [ hdf5_reporter, dashboard_reporter, setup_reporter, restart_reporter ] ## The work mapper # we use a mapper that uses GPUs work_mapper = WorkerMapper(worker_type=OpenMMGPUWorker, num_workers=n_workers) ## Combine all these parts and setup the simulation manager # set up parameters for running the simulation # initial weights init_weight = 1.0 / n_walkers # a list of the initial walkers init_walkers = [ Walker(OpenMMState(omm_state), init_weight) for i in range(n_walkers) ] # Instantiate a simulation manager sim_manager = Manager(init_walkers, runner=runner, resampler=resampler, boundary_conditions=ubc, work_mapper=work_mapper, reporters=reporters) ### RUN the simulation for run_idx in range(n_runs): print("Starting run: {}".format(run_idx)) sim_manager.run_simulation(n_cycles, steps, debug_prints=True) print("Finished run: {}".format(run_idx))
# observable function, fictituous random "cluster" assignments def rand_assg(fields_d, *args, **kwargs): assignments = np.random.random_integers(0, n_classifications, size=fields_d['weights'].shape[0]) return assignments # compute this random assignment "observable" wepy_h5.compute_observable(rand_assg, ['weights'], save_to_hdf5=assg_key, return_results=False) # make a parent matrix from the hdf5 resampling records resampling_panel = wepy_h5.run_resampling_panel(run_idx) parent_panel = MultiCloneMergeDecision.parent_panel(resampling_panel) parent_matrix = MultiCloneMergeDecision.net_parent_table(parent_panel) # take into account warping events as discontinuities in the lineage parent_matrix = UnbindingBC.lineage_discontinuities(parent_matrix, wepy_h5.warping_records(0)) # use the parent matrix to generate the sliding windows window_length = 10 windows = list(sliding_window(np.array(parent_matrix), window_length)) # make the transition matrix from the windows transprob_mat = run_transition_probability_matrix( wepy_h5, run_idx, "observables/{}".format(assg_key), windows)