def generate_topology(self):
"""Creates a one-atom, dummy trajectory and topology for
the randomwalk system using the mdtraj package. Then creates a
JSON format for the topology. This JSON string is used in making
the WepyHDF5 reporter.
Returns
-------
topology: str
JSON string representing the topology of system being simulated.
"""
n_atoms = 1
data = []
for i in range(n_atoms):
data.append(
dict(serial=i,
name="H",
element="H",
resSeq=i + 1,
resName="UNK",
chainID=0))
data = pd.DataFrame(data)
xyz = np.zeros((1, 1, 3))
unitcell_lengths = 0.943 * np.ones((1, 3))
unitcell_angles = 90 * np.ones((1, 3))
top = mdj.Topology.from_dataframe(data,
bonds=np.zeros((0, 2), dtype='int'))
json_top_str = mdtraj_to_json_topology(top)
return json_top_str
def json_top(cls):
test_sys = cls.TEST_SYS()
# convert to a JSON top
json_top = mdtraj_to_json_topology(
mdj.Topology.from_openmm(test_sys.topology))
return json_top
# between two walkers, for our scorer class
distance = PairDistance()
## Resampler
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)
json_str_top = mdtraj_to_json_topology(mdtraj_topology)
# initialize the unbinding boundary conditions
ubc = UnbindingBC(cutoff_distance=CUTOFF_DISTANCE,
initial_state=init_state,
topology=json_str_top,
ligand_idxs=np.array(test_sys.ligand_indices),
receptor_idxs=np.array(test_sys.receptor_indices))
## Reporters
# 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(file_path=hdf5_path,
charmm_psf_path = osp.join(inputs_dir, charmm_psf_filename)
pdb_path = osp.join(inputs_dir, starting_coords_pdb)
charmm_param_paths = [osp.join(inputs_dir, filename) for filename
in charmm_param_files]
json_top_path = osp.join(inputs_dir, json_top_filename)
omm_state_path = osp.join(inputs_dir, omm_state_filename)
# load the charmm file for the topology
psf = omma.CharmmPsfFile(charmm_psf_path)
# load the coordinates
pdb = mdj.load_pdb(pdb_path)
# convert the mdtraj topology to a json one
top_str = mdtraj_to_json_topology(pdb.topology)
# write the JSON topology out
with open(json_top_path, mode='w') as json_wf:
json_wf.write(top_str)
# to use charmm forcefields get your parameters
params = omma.CharmmParameterSet(*charmm_param_paths)
# set the box size lengths and angles
lengths = [8.2435*unit.nanometer for i in range(3)]
angles = [90*unit.degree for i in range(3)]
psf.setBox(*lengths, *angles)
# create a system using the topology method giving it a topology and
get_state_kwargs = dict(GET_STATE_KWARG_DEFAULTS)
random_pos = random.sample(range(1000), n_walkers)
walker_pos = pickle.load(open(f'inputs/{epsilon}eps_init_positions.pkl', 'rb'))
for i in range(n_walkers):
# initiate the test system
test_sys = LennardJonesPair(epsilon=epsilon * unit.kilocalories_per_mole)
# change the box size
# Vectors set in Vec3. unit=nanometers
test_sys.system.setDefaultPeriodicBoxVectors([4, 0, 0], [0, 4, 0],
[0, 0, 4])
system = test_sys.system
omm_topology = test_sys.topology
mdj_top = mdj.Topology.from_openmm(omm_topology)
json_top = mdtraj_to_json_topology(mdj_top)
# make the integrator and barostat
integrator = omm.LangevinIntegrator(TEMPERATURE, FRICTION_COEFFICIENT,
STEP_SIZE)
# build the harmonic force
total_lj = CustomBondForce("0.5*k*(r-r0)^2")
total_lj.addPerBondParameter('k')
total_lj.addPerBondParameter('r0')
# param [0] is the spring constant, param [1] is the init distance (nm)
total_lj.addBond(0, 1, [2000, 0.320 * unit.nanometer])
system.addForce(total_lj)
# load positions (randomy selects unrepreated position pairs from
# a previously generated, well equilibrated system)
# distance from the ligand in the crystal structure used to determine
# the binding site, used to align ligands in the Unbinding distance
# metric
BINDING_SITE_CUTOFF = 0.8 # in nanometers
# the residue id for the ligand so that it's indices can be determined
LIG_RESID = "2RV"
# Initial experimental coordinates
PDB = mdj.load_pdb(pdb_path)
EXPERIMENTAL_POSITIONS = PDB.xyz[0]
EXPERIMENTAL_OMM_POSITIONS = PDB.openmm_positions(frame=0)
# topologies
TOP_MDTRAJ = PDB.topology
TOP_JSON = mdtraj_to_json_topology(TOP_MDTRAJ)
# the protein, binding site, and ligand atoms
BS_IDXS = binding_site_atoms(TOP_MDTRAJ, LIG_RESID, EXPERIMENTAL_POSITIONS)
LIG_IDXS = ligand_idxs(TOP_MDTRAJ, LIG_RESID)
PROT_IDXS = protein_idxs(TOP_MDTRAJ)
# reporting parameters
# these are the properties of the states (i.e. from OpenMM) which will
# be saved into the HDF5
SAVE_FIELDS = ('positions', 'box_vectors', 'velocities')
# these are the names of the units which will be stored with each
# field in the HDF5
UNITS = UNIT_NAMES
import numpy as np
import pandas as pd
import tables as pytables
import mdtraj as mdj
from wepy.util.mdtraj import mdtraj_to_json_topology
from wepy.util.json_top import json_top_chain_df, json_top_residue_df, json_top_atom_df
traj = mdj.load('../lysozyme_pxylene.pdb')
if osp.exists("outputs"):
shutil.rmtree("outputs")
os.makedirs("outputs")
# the JSON format
json_top = mdtraj_to_json_topology(traj.top)
with open('outputs/lysozyme_pxylene.top.json', 'w') as wf:
wf.write(json_top)
# FASTA residue sequence
fasta_str = traj.top.to_fasta(chain=0)
with open('outputs/lysozyme_pxylene.res.fasta', 'w') as wf:
wf.write(fasta_str)
## topology tables
# Bonds
# you can get a table using mdtraj, but we just use the bonds here
mdj_atoms_df, bonds = traj.top.to_dataframe()
