def get_potential_energy(item, coordinates=None, platform_name='CUDA'):
from simtk.openmm import LangevinIntegrator, Platform, Context
from simtk import unit
import numpy as np
integrator = LangevinIntegrator(0.0 * unit.kelvin, 0.0 / unit.picoseconds,
2.0 * unit.femtoseconds)
platform = Platform.getPlatformByName(platform_name)
context = Context(item.system, integrator, platform)
if coordinates is None:
context.setPositions(item.coordinates)
else:
context.setPositions(coordinates)
if item.box is not None:
context.setPeriodicBoxVectors(item.box[0], item.box[1], item.box[2])
state = context.getState(getEnergy=True)
potential_energy = state.getPotentialEnergy()
return potential_energy
def __init__(self, topology=None, system=None, pbc=False, platform='CUDA'):
from .md import MD
from .quench import Quench
from .move import Move
from .distance import Distance
from .acceptance import Acceptance
if topology is None:
raise ValueError('topology is needed')
if system is None:
raise ValueError('system is needed')
integrator = LangevinIntegrator(0 * u.kelvin, 1.0 / u.picoseconds,
2.0 * u.femtoseconds)
#integrator.setConstraintTolerance(0.00001)
if platform == 'CUDA':
platform = Platform.getPlatformByName('CUDA')
properties = {'CudaPrecision': 'mixed'}
elif platform == 'CPU':
platform = Platform.getPlatformByName('CPU')
properties = {}
self.topology = topology
self.context = Context(system, integrator, platform, properties)
self.n_atoms = msm.get(self.context, target='system', n_atoms=True)
self.n_dof = 0
for i in range(system.getNumParticles()):
if system.getParticleMass(i) > 0 * u.dalton:
self.n_dof += 3
for i in range(system.getNumConstraints()):
p1, p2, distance = system.getConstraintParameters(i)
if system.getParticleMass(
p1) > 0 * u.dalton or system.getParticleMass(
p2) > 0 * u.dalton:
self.n_dof -= 1
if any(
type(system.getForce(i)) == CMMotionRemover
for i in range(system.getNumForces())):
self.n_dof -= 3
self.pbc = pbc
if self.pbc:
raise NotImplementedError
self.md = MD(self)
self.quench = Quench(self)
self.move = Move(self)
self.distance = Distance(self)
self.acceptance = Acceptance(self)
def run(self):
"""Run the process: set positions and compute energies and forces.
Positions and box vectors are received from the task_queue in units of nanometers.
Energies and forces are pushed to the result_queue in units of kJ/mole and kJ/mole/nm, respectively.
"""
from simtk import unit
from simtk.openmm import Platform, Context
# create the context
# it is crucial to do that in the run function and not in the constructor
# for some reason, the CPU platform hangs if the context is created in the constructor
# see also https://github.com/openmm/openmm/issues/2602
openmm_platform = Platform.getPlatformByName(self._openmm_platform_name)
self._openmm_context = Context(
self._openmm_system,
self._openmm_integrator,
openmm_platform,
self._openmm_platform_properties
)
self._openmm_context.reinitialize(preserveState=True)
# get tasks from the task queue
for task in iter(self._task_queue.get, None):
(index, positions, box_vectors, evaluate_energy, evaluate_force,
evaluate_positions, evaluate_path_probability_ratio, err_handling, n_simulation_steps) = task
try:
# initialize state
self._openmm_context.setPositions(positions)
if box_vectors is not None:
self._openmm_context.setPeriodicBoxVectors(box_vectors)
log_path_probability_ratio = self._openmm_integrator.step(n_simulation_steps)
# compute energy and forces
state = self._openmm_context.getState(
getEnergy=evaluate_energy,
getForces=evaluate_force,
getPositions=evaluate_positions
)
energy = state.getPotentialEnergy().value_in_unit(unit.kilojoule_per_mole) if evaluate_energy else None
forces = (
state.getForces(asNumpy=True).value_in_unit(unit.kilojoule_per_mole / unit.nanometer)
if evaluate_force else None
)
new_positions = state.getPositions().value_in_unit(unit.nanometers) if evaluate_positions else None
except Exception as e:
if err_handling == "warning":
warnings.warn("Suppressed exception: {}".format(e))
elif err_handling == "exception":
raise e
# push energies and forces to the results queue
self._result_queue.put(
[index, energy, forces, new_positions, log_path_probability_ratio]
)
def _initialize(self):
system = self._explorer.context.getSystem()
platform = self._explorer.context.getPlatform()
properties = {}
if platform.getName()=='CUDA':
properties['CudaPrecision'] = 'mixed'
self._integrator = GradientDescentMinimizationIntegrator(initial_step_size=self._initial_step_size)
self._context = Context(system, self._integrator, platform, properties)
self._initialized = True
def _initialize(self):
system = self._explorer.context.getSystem()
platform = self._explorer.context.getPlatform()
properties = {}
if platform.getName() == 'CUDA':
properties['CudaPrecision'] = 'mixed'
self._integrator = LangevinIntegrator(self._temperature,
self._collision_rate,
self._timestep)
self._context = Context(system, self._integrator, platform, properties)
self._initialized = True
class OpenMMStateBuilder(StateBuilder):
"""Build an OpenMM "state" that can be sent to a device to simulate.
"""
def __init__(self, system, integrator=None):
# if strings are passed in, assume that they are paths to
# xml files on disk
if isinstance(system, basestring):
with open(system) as f:
system = XmlSerializer.deserialize(f.read())
if isinstance(integrator, basestring):
with open(integrator) as f:
integrator = XmlSerializer.deserialize(f.read())
if integrator is None:
# this integrator isn't really necessary, but it has to be something
# for the openmm API to let us serialize the state
integrator = VerletIntegrator(2 * femtoseconds)
self.context = Context(system, integrator,
Platform.getPlatformByName('Reference'))
def build(self, trajectory):
"""Create a serialized XML state from the first frame in a trajectory
Parameteters
------------
trajectory : mdtraj.trajectory.Trajectory
The trajectory to take the frame from. We'll use both the the
positions and the box vectors (if you're using periodic boundary
conditions)
"""
periodic = False
if trajectory.unitcell_vectors is not None:
a, b, c = trajectory.unitcell_lengths[0]
np.testing.assert_array_almost_equal(trajectory.unitcell_angles[0],
np.ones(3) * 90)
self.context.setPeriodicBoxVectors([a, 0, 0] * nanometers,
[0, b, 0] * nanometers,
[0, 0, c] * nanometers)
periodic = True
self.context.setPositions(trajectory.openmm_positions(0))
state = self.context.getState(getPositions=True,
getVelocities=True,
getForces=True,
getEnergy=True,
getParameters=True,
enforcePeriodicBox=periodic)
return XmlSerializer.serialize(state)
def __init__(self, system, integrator=None):
# if strings are passed in, assume that they are paths to
# xml files on disk
if isinstance(system, basestring):
with open(system) as f:
system = XmlSerializer.deserialize(f.read())
if isinstance(integrator, basestring):
with open(integrator) as f:
integrator = XmlSerializer.deserialize(f.read())
if integrator is None:
# this integrator isn't really necessary, but it has to be something
# for the openmm API to let us serialize the state
integrator = VerletIntegrator(2 * femtoseconds)
self.context = Context(system, integrator,
Platform.getPlatformByName('Reference'))
def energy_minimization(item, platform_name='CUDA', verbose=True):
from simtk.openmm import LangevinIntegrator, Platform, Context, LocalEnergyMinimizer_minimize
from simtk import unit
# Integrator.
integrator = LangevinIntegrator(0 * unit.kelvin, 1.0 / unit.picoseconds,
2.0 * unit.femtoseconds)
# Platform.
platform = Platform.getPlatformByName(platform_name)
# Context.
context = Context(item.system, integrator, platform)
context.setPositions(item.coordinates)
# Minimization.
if verbose == True:
energy = context.getState(getEnergy=True).getPotentialEnergy()
print('Potential energy before minimization: {}'.format(energy))
LocalEnergyMinimizer_minimize(context)
if verbose == True:
energy = context.getState(getEnergy=True).getPotentialEnergy()
print('Potential energy after minimization: {}'.format(energy))
item.coordinates = context.getState(getPositions=True).getPositions(
asNumpy=True)
pass
class OpenMMStateBuilder(StateBuilder):
"""Build an OpenMM "state" that can be sent to a device to simulate.
"""
def __init__(self, system, integrator=None):
# if strings are passed in, assume that they are paths to
# xml files on disk
if isinstance(system, basestring):
with open(system) as f:
system = XmlSerializer.deserialize(f.read())
if isinstance(integrator, basestring):
with open(integrator) as f:
integrator = XmlSerializer.deserialize(f.read())
if integrator is None:
# this integrator isn't really necessary, but it has to be something
# for the openmm API to let us serialize the state
integrator = VerletIntegrator(2*femtoseconds)
self.context = Context(system, integrator, Platform.getPlatformByName('Reference'))
def build(self, trajectory):
"""Create a serialized XML state from the first frame in a trajectory
Parameteters
------------
trajectory : mdtraj.trajectory.Trajectory
The trajectory to take the frame from. We'll use both the the
positions and the box vectors (if you're using periodic boundary
conditions)
"""
periodic = False
if trajectory.unitcell_vectors is not None:
a, b, c = trajectory.unitcell_lengths[0]
np.testing.assert_array_almost_equal(trajectory.unitcell_angles[0], np.ones(3)*90)
self.context.setPeriodicBoxVectors([a, 0, 0] * nanometers, [0, b, 0] * nanometers, [0, 0, c] * nanometers)
periodic = True
self.context.setPositions(trajectory.openmm_positions(0))
state = self.context.getState(getPositions=True, getVelocities=True,
getForces=True, getEnergy=True,
getParameters=True, enforcePeriodicBox=periodic)
return XmlSerializer.serialize(state)
def _initialize(self):
system = self._explorer.context.getSystem()
platform = self._explorer.context.getPlatform()
properties = {}
if platform.getName() == 'CUDA':
properties['CudaPrecision'] = 'mixed'
self._integrator = FIREMinimizationIntegrator(
timestep=self._timestep,
tolerance=self._tolerance,
alpha=self._alpha,
dt_max=self._dt_max,
f_inc=self._f_inc,
f_dec=self._f_dec,
f_alpha=self._f_alpha,
N_min=self._N_min)
self._context = Context(system, self._integrator, platform, properties)
self._initialized = True
def __init__(self, system, integrator=None):
# if strings are passed in, assume that they are paths to
# xml files on disk
if isinstance(system, basestring):
with open(system) as f:
system = XmlSerializer.deserialize(f.read())
if isinstance(integrator, basestring):
with open(integrator) as f:
integrator = XmlSerializer.deserialize(f.read())
if integrator is None:
# this integrator isn't really necessary, but it has to be something
# for the openmm API to let us serialize the state
integrator = VerletIntegrator(2*femtoseconds)
self.context = Context(system, integrator, Platform.getPlatformByName('Reference'))
def __init__(self, n_workers, system, integrator, platform_name, platform_properties={}):
"""Set up workers and queues."""
from simtk.openmm import Platform, Context
assert n_workers == 1
openmm_platform = Platform.getPlatformByName(platform_name)
self._openmm_context = Context(system, integrator, openmm_platform, platform_properties)
class SingleContext:
"""Mimics the MultiContext API but does not spawn worker processes.
Parameters:
-----------
n_workers : int
Needs to be 1.
system : simtk.openmm.System
The system that contains all forces.
integrator : simtk.openmm.Integrator
An OpenMM integrator.
platform_name : str
The name of an OpenMM platform ('Reference', 'CPU', 'CUDA', or 'OpenCL')
platform_properties : dict, optional
A dictionary of platform properties.
"""
def __init__(self, n_workers, system, integrator, platform_name, platform_properties={}):
"""Set up workers and queues."""
from simtk.openmm import Platform, Context
assert n_workers == 1
openmm_platform = Platform.getPlatformByName(platform_name)
self._openmm_context = Context(system, integrator, openmm_platform, platform_properties)
def evaluate(
self,
positions,
box_vectors=None,
evaluate_energy=True,
evaluate_force=True,
evaluate_positions=False,
evaluate_path_probability_ratio=False,
err_handling="warning",
n_simulation_steps=0
):
"""Compute energies and/or forces.
Parameters:
-----------
positions : numpy.ndarray
The particle positions in nanometer; its shape is (batch_size, num_particles, 3).
box_vectors : numpy.ndarray, optional
The periodic box vectors in nanometer; its shape is (batch_size, 3, 3).
If not specified, don't change the box vectors.
evaluate_energy : bool, optional
Whether to compute energies.
evaluate_force : bool, optional
Whether to compute forces.
evaluate_positions : bool, optional
Whether to return positions.
evaluate_path_probability_ratio : bool, optional
Whether to compute the log path probability ratio. Makes only sense for PathProbabilityIntegrator instances.
_err_handling : str, optional
How to handle infinite energies (one of {"warning", "ignore", "exception"}).
n_simulation_steps : int, optional
If > 0, perform a number of simulation steps and compute energy and forces for the resulting state.
Returns:
--------
energies : np.ndarray or None
The energies in units of kilojoule/mole; its shape is (len(positions), )
forces : np.ndarray or None
The forces in units of kilojoule/mole/nm; its shape is (len(positions), num_particles, 3)
new_positions : np.ndarray or None
The positions in units of nm; its shape is (len(positions), num_particles, 3)
log_path_probability_ratio : np.ndarray or None
The logarithmic path probability ratios; its shape is (len(positions), )
"""
from simtk import unit
assert box_vectors is None or len(box_vectors) == len(positions), \
"box_vectors and positions have to be the same length"
box_vectors = [None for _ in positions] if box_vectors is None else box_vectors
forces = np.zeros_like(positions)
energies = np.zeros_like(positions[:,0,0])
new_positions = np.zeros_like(positions)
log_path_probability_ratios = np.zeros_like(positions[:,0,0])
for i, (p, bv) in enumerate(zip(positions, box_vectors)):
try:
# initialize state
self._openmm_context.setPositions(p)
if bv is not None:
self._openmm_context.setPeriodicBoxVectors(bv)
log_path_probability_ratio = self._openmm_context.getIntegrator().step(n_simulation_steps)
# compute energy and forces
state = self._openmm_context.getState(
getEnergy=evaluate_energy,
getForces=evaluate_force,
getPositions=evaluate_positions
)
energy = state.getPotentialEnergy().value_in_unit(unit.kilojoule_per_mole) if evaluate_energy else None
force = (
state.getForces(asNumpy=True).value_in_unit(unit.kilojoule_per_mole / unit.nanometer)
if evaluate_force else None
)
new_pos = state.getPositions().value_in_unit(unit.nanometers) if evaluate_positions else None
energies[i] = energy if evaluate_energy else 0.0
forces[i,:,:] = force if evaluate_force else 0.0
new_positions[i,:,:] = new_pos if evaluate_positions else 0.0
log_path_probability_ratios[i] = log_path_probability_ratio if evaluate_path_probability_ratio else 0.0
except Exception as e:
if err_handling == "warning":
warnings.warn("Suppressed exception: {}".format(e))
elif err_handling == "exception":
raise e
return (
energies if evaluate_energy else None,
forces if evaluate_force else None,
new_positions if evaluate_positions else None,
log_path_probability_ratios if evaluate_path_probability_ratio else None
)
class Worker(mp.Process):
"""A worker process that computes energies in its own context.
Parameters:
-----------
task_queue : multiprocessing.Queue
The queue that the MultiContext pushes tasks to.
result_queue : multiprocessing.Queue
The queue that the MultiContext receives results from.
system : simtk.openmm.System
The system that contains all forces.
integrator : simtk.openmm.Integrator
An OpenMM integrator.
platform_name : str
The name of an OpenMM platform ('Reference', 'CPU', 'CUDA', or 'OpenCL')
platform_properties : dict
A dictionary of platform properties.
"""
def __init__(self, task_queue, result_queue, system, integrator, platform_name, platform_properties):
super(MultiContext.Worker, self).__init__()
self._task_queue = task_queue
self._result_queue = result_queue
self._openmm_system = system
self._openmm_integrator = pickle.loads( pickle.dumps(integrator))
self._openmm_platform_name = platform_name
self._openmm_platform_properties = platform_properties
self._openmm_context = None
def run(self):
"""Run the process: set positions and compute energies and forces.
Positions and box vectors are received from the task_queue in units of nanometers.
Energies and forces are pushed to the result_queue in units of kJ/mole and kJ/mole/nm, respectively.
"""
from simtk import unit
from simtk.openmm import Platform, Context
# create the context
# it is crucial to do that in the run function and not in the constructor
# for some reason, the CPU platform hangs if the context is created in the constructor
# see also https://github.com/openmm/openmm/issues/2602
openmm_platform = Platform.getPlatformByName(self._openmm_platform_name)
self._openmm_context = Context(
self._openmm_system,
self._openmm_integrator,
openmm_platform,
self._openmm_platform_properties
)
self._openmm_context.reinitialize(preserveState=True)
# get tasks from the task queue
for task in iter(self._task_queue.get, None):
(index, positions, box_vectors, evaluate_energy, evaluate_force,
evaluate_positions, evaluate_path_probability_ratio, err_handling, n_simulation_steps) = task
try:
# initialize state
self._openmm_context.setPositions(positions)
if box_vectors is not None:
self._openmm_context.setPeriodicBoxVectors(box_vectors)
log_path_probability_ratio = self._openmm_integrator.step(n_simulation_steps)
# compute energy and forces
state = self._openmm_context.getState(
getEnergy=evaluate_energy,
getForces=evaluate_force,
getPositions=evaluate_positions
)
energy = state.getPotentialEnergy().value_in_unit(unit.kilojoule_per_mole) if evaluate_energy else None
forces = (
state.getForces(asNumpy=True).value_in_unit(unit.kilojoule_per_mole / unit.nanometer)
if evaluate_force else None
)
new_positions = state.getPositions().value_in_unit(unit.nanometers) if evaluate_positions else None
except Exception as e:
if err_handling == "warning":
warnings.warn("Suppressed exception: {}".format(e))
elif err_handling == "exception":
raise e
# push energies and forces to the results queue
self._result_queue.put(
[index, energy, forces, new_positions, log_path_probability_ratio]
)
def addHydrogens(self,
forcefield=None,
pH=None,
variants=None,
platform=None):
"""Add missing hydrogens to the model.
This function automatically changes compatible residues into their constant-pH variant if no variant is specified.:
Aspartic acid:
AS4: Form with a 2 hydrogens on each one of the delta oxygens (syn,anti)
It has 5 titration states.
Alternative:
AS2: Has 2 hydrogens (syn, anti) on one of the delta oxygens
It has 3 titration states.
Cysteine:
CYS: Neutral form with a hydrogen on the sulfur
CYX: No hydrogen on the sulfur (either negatively charged, or part of a disulfide bond)
Glutamic acid:
GL4: Form with a 2 hydrogens on each one of the epsilon oxygens (syn,anti)
It has 5 titration states.
Histidine:
HIP: Positively charged form with hydrogens on both ND1 and NE2
It has 3 titration states.
The variant to use for each residue is determined by the following rules:
1. Any Cysteine that participates in a disulfide bond uses the CYX variant regardless of pH.
2. Other residues are all set to maximally protonated state, which can be updated using a proton drive
You can override these rules by explicitly specifying a variant for any residue. To do that, provide a list for the
'variants' parameter, and set the corresponding element to the name of the variant to use.
A special case is when the model already contains a hydrogen that should not be present in the desired variant.
If you explicitly specify a variant using the 'variants' parameter, the residue will be modified to match the
desired variant, removing hydrogens if necessary. On the other hand, for residues whose variant is selected
automatically, this function will only add hydrogens. It will never remove ones that are already present in the
model.
Definitions for standard amino acids and nucleotides are built in. You can call loadHydrogenDefinitions() to load
additional definitions for other residue types.
Parameters
----------
forcefield : ForceField=None
the ForceField to use for determining the positions of hydrogens.
If this is None, positions will be picked which are generally
reasonable but not optimized for any particular ForceField.
pH : None,
Kept for compatibility reasons. Has no effect.
variants : list=None
an optional list of variants to use. If this is specified, its
length must equal the number of residues in the model. variants[i]
is the name of the variant to use for residue i (indexed starting at
0). If an element is None, the standard rules will be followed to
select a variant for that residue.
platform : Platform=None
the Platform to use when computing the hydrogen atom positions. If
this is None, the default Platform will be used.
Returns
-------
list
a list of what variant was actually selected for each residue,
in the same format as the variants parameter
Notes
-----
This function does not use a pH specification. The argument is kept for compatibility reasons.
"""
# Check the list of variants.
if pH is not None:
print("Ignored pH argument provided for constant-pH residues.")
residues = list(self.topology.residues())
if variants is not None:
if len(variants) != len(residues):
raise ValueError(
"The length of the variants list must equal the number of residues"
)
else:
variants = [None] * len(residues)
actualVariants = [None] * len(residues)
# Load the residue specifications.
if not Modeller._hasLoadedStandardHydrogens:
Modeller.loadHydrogenDefinitions(
os.path.join(os.path.dirname(__file__), "data",
"hydrogens-amber10-constph.xml"))
# Make a list of atoms bonded to each atom.
bonded = {}
for atom in self.topology.atoms():
bonded[atom] = []
for atom1, atom2 in self.topology.bonds():
bonded[atom1].append(atom2)
bonded[atom2].append(atom1)
# Define a function that decides whether a set of atoms form a hydrogen bond, using fairly tolerant criteria.
def isHbond(d, h, a):
if norm(d - a) > 0.35 * nanometer:
return False
deltaDH = h - d
deltaHA = a - h
deltaDH /= norm(deltaDH)
deltaHA /= norm(deltaHA)
return acos(dot(deltaDH, deltaHA)) < 50 * degree
# Loop over residues.
newTopology = Topology()
newTopology.setPeriodicBoxVectors(
self.topology.getPeriodicBoxVectors())
newAtoms = {}
newPositions = [] * nanometer
newIndices = []
acceptors = [
atom for atom in self.topology.atoms()
if atom.element in (elem.oxygen, elem.nitrogen)
]
for chain in self.topology.chains():
newChain = newTopology.addChain(chain.id)
for residue in chain.residues():
newResidue = newTopology.addResidue(residue.name, newChain,
residue.id)
isNTerminal = residue == chain._residues[0]
isCTerminal = residue == chain._residues[-1]
if residue.name in Modeller._residueHydrogens:
# Add hydrogens. First select which variant to use.
spec = Modeller._residueHydrogens[residue.name]
variant = variants[residue.index]
if variant is None:
if residue.name == "CYS":
# If this is part of a disulfide, use CYX.
sulfur = [
atom for atom in residue.atoms()
if atom.element == elem.sulfur
]
if len(sulfur) == 1 and any(
(atom.residue != residue
for atom in bonded[sulfur[0]])):
variant = "CYX"
if residue.name == "HIS":
variant = "HIP"
if residue.name == "GLU":
variant = "GL4"
if residue.name == "ASP":
variant = "AS4"
if variant is not None and variant not in spec.variants:
raise ValueError("Illegal variant for %s residue: %s" %
(residue.name, variant))
actualVariants[residue.index] = variant
removeExtraHydrogens = variants[residue.index] is not None
# Make a list of hydrogens that should be present in the residue.
parents = [
atom for atom in residue.atoms()
if atom.element != elem.hydrogen
]
parentNames = [atom.name for atom in parents]
hydrogens = [
h for h in spec.hydrogens
if (variant is None) or (h.variants is None) or (
h.variants is not None and variant in h.variants)
]
hydrogens = [
h for h in hydrogens if h.terminal is None or (
isNTerminal and h.terminal == "N") or (
isCTerminal and h.terminal == "C")
]
hydrogens = [
h for h in hydrogens if h.parent in parentNames
]
# Loop over atoms in the residue, adding them to the new topology along with required hydrogens.
for parent in residue.atoms():
# Check whether this is a hydrogen that should be removed.
if (removeExtraHydrogens
and parent.element == elem.hydrogen
and not any(parent.name == h.name
for h in hydrogens)):
continue
# Add the atom.
newAtom = newTopology.addAtom(parent.name,
parent.element,
newResidue)
newAtoms[parent] = newAtom
newPositions.append(
deepcopy(self.positions[parent.index]))
if parent in parents:
# Match expected hydrogens with existing ones and find which ones need to be added.
existing = [
atom for atom in bonded[parent]
if atom.element == elem.hydrogen
]
expected = [
h for h in hydrogens if h.parent == parent.name
]
if len(existing) < len(expected):
# Try to match up existing hydrogens to expected ones.
matches = []
for e in existing:
match = [
h for h in expected if h.name == e.name
]
if len(match) > 0:
matches.append(match[0])
expected.remove(match[0])
else:
matches.append(None)
# If any hydrogens couldn't be matched by name, just match them arbitrarily.
for i in range(len(matches)):
if matches[i] is None:
matches[i] = expected[-1]
expected.remove(expected[-1])
# Add the missing hydrogens.
for h in expected:
newH = newTopology.addAtom(
h.name, elem.hydrogen, newResidue)
newIndices.append(newH.index)
delta = Vec3(0, 0, 0) * nanometer
if len(bonded[parent]) > 0:
for other in bonded[parent]:
delta += (
self.positions[parent.index] -
self.positions[other.index])
else:
delta = (Vec3(
random.random(),
random.random(),
random.random(),
) * nanometer)
delta *= 0.1 * nanometer / norm(delta)
delta += (0.05 * Vec3(
random.random(),
random.random(),
random.random(),
) * nanometer)
delta *= 0.1 * nanometer / norm(delta)
newPositions.append(
self.positions[parent.index] + delta)
newTopology.addBond(newAtom, newH)
else:
# Just copy over the residue.
for atom in residue.atoms():
newAtom = newTopology.addAtom(atom.name, atom.element,
newResidue)
newAtoms[atom] = newAtom
newPositions.append(
deepcopy(self.positions[atom.index]))
for bond in self.topology.bonds():
if bond[0] in newAtoms and bond[1] in newAtoms:
newTopology.addBond(newAtoms[bond[0]], newAtoms[bond[1]])
# The hydrogens were added at random positions. Now perform an energy minimization to fix them up.
if forcefield is not None:
# Use the ForceField the user specified.
system = forcefield.createSystem(newTopology, rigidWater=False)
atoms = list(newTopology.atoms())
for i in range(system.getNumParticles()):
if atoms[i].element != elem.hydrogen:
# This is a heavy atom, so make it immobile.
system.setParticleMass(i, 0)
else:
# Create a System that restrains the distance of each hydrogen from its parent atom
# and causes hydrogens to spread out evenly.
system = System()
nonbonded = CustomNonbondedForce("100/((r/0.1)^4+1)")
bonds = HarmonicBondForce()
angles = HarmonicAngleForce()
system.addForce(nonbonded)
system.addForce(bonds)
system.addForce(angles)
bondedTo = []
for atom in newTopology.atoms():
nonbonded.addParticle([])
if atom.element != elem.hydrogen:
system.addParticle(0.0)
else:
system.addParticle(1.0)
bondedTo.append([])
for atom1, atom2 in newTopology.bonds():
if atom1.element == elem.hydrogen or atom2.element == elem.hydrogen:
bonds.addBond(atom1.index, atom2.index, 0.1, 100_000.0)
bondedTo[atom1.index].append(atom2)
bondedTo[atom2.index].append(atom1)
for residue in newTopology.residues():
if residue.name == "HOH":
# Add an angle term to make the water geometry correct.
atoms = list(residue.atoms())
oindex = [
i for i in range(len(atoms))
if atoms[i].element == elem.oxygen
]
if len(atoms) == 3 and len(oindex) == 1:
hindex = list(set([0, 1, 2]) - set(oindex))
angles.addAngle(
atoms[hindex[0]].index,
atoms[oindex[0]].index,
atoms[hindex[1]].index,
1.824,
836.8,
)
else:
# Add angle terms for any hydroxyls.
for atom in residue.atoms():
index = atom.index
if (atom.element == elem.oxygen
and len(bondedTo[index]) == 2 and elem.hydrogen
in (a.element for a in bondedTo[index])):
angles.addAngle(
bondedTo[index][0].index,
index,
bondedTo[index][1].index,
1.894,
460.24,
)
if platform is None:
context = Context(system, VerletIntegrator(0.0))
else:
context = Context(system, VerletIntegrator(0.0), platform)
context.setPositions(newPositions)
LocalEnergyMinimizer.minimize(context, 1.0, 50)
self.topology = newTopology
self.positions = context.getState(getPositions=True).getPositions()
del context
return actualVariants
class GradientDescent():
_explorer = None
_initialized = False
_context = None
_integrator = None
_initial_step_size = Quantity(0.01, u.angstroms)
_tolerance = Quantity(1.0, u.kilojoules_per_mole)
def __init__(self, explorer):
self._explorer=explorer
def _initialize(self):
system = self._explorer.context.getSystem()
platform = self._explorer.context.getPlatform()
properties = {}
if platform.getName()=='CUDA':
properties['CudaPrecision'] = 'mixed'
self._integrator = GradientDescentMinimizationIntegrator(initial_step_size=self._initial_step_size)
self._context = Context(system, self._integrator, platform, properties)
self._initialized = True
def set_parameters(self, tolerance=Quantity(1.0, u.kilojoules_per_mole),
initial_step_size=Quantity(0.01, u.angstroms)):
if not self._initialized:
self._initialize()
self._tolerance = tolerance.in_units_of(u.kilojoules_per_mole)
self._initial_step_size = initial_step_size.in_units_of(u.nanometers)
self._integrator.setGlobalVariableByName('step_size', self._initial_step_size._value)
def replicate_parameters(self, explorer):
tolerance = explorer.quench.gradient_descent._tolerance
initial_step_size = explorer.quench.gradient_descent._initial_step_size
self.set_parameters(tolerance, initial_step_size)
def _set_coordinates(self, coordinates):
self._context.setPositions(coordinates)
def _get_coordinates(self):
return self._context.getState(getPositions=True).getPositions(asNumpy=True)
def _coordinates_to_explorer(self):
self._explorer.set_coordinates(self._get_coordinates())
def _coordinates_from_explorer(self):
self._set_coordinates(self._explorer.get_coordinates())
def run(self, steps=0):
if not self._initialized:
self._initialize()
self._coordinates_from_explorer()
try:
if steps == 0:
delta=np.infty
while delta > self._tolerance._value:
self._integrator.step(50)
delta=self._integrator.getGlobalVariableByName('delta_energy')
else:
integrator.step(steps)
self._coordinates_to_explorer()
except Exception as e:
if str(e) == 'Particle coordinate is nan':
print('NaN encountered in gradient descent minimizer; falling back to L-BFGS after resetting positions')
self._explorer.quench.l_bfgs()
else:
raise e
self._initialize()
def __call__(self, *args, **kwargs):
return self.run(*args, **kwargs)
def addHydrogens(self, forcefield, pH=7.0, variants=None, platform=None):
"""Add missing hydrogens to the model.
Some residues can exist in multiple forms depending on the pH and properties of the local environment. These
variants differ in the presence or absence of particular hydrogens. In particular, the following variants
are supported:
Aspartic acid:
ASH: Neutral form with a hydrogen on one of the delta oxygens
ASP: Negatively charged form without a hydrogen on either delta oxygen
Cysteine:
CYS: Neutral form with a hydrogen on the sulfur
CYX: No hydrogen on the sulfur (either negatively charged, or part of a disulfide bond)
Glutamic acid:
GLH: Neutral form with a hydrogen on one of the epsilon oxygens
GLU: Negatively charged form without a hydrogen on either epsilon oxygen
Histidine:
HID: Neutral form with a hydrogen on the ND1 atom
HIE: Neutral form with a hydrogen on the NE2 atom
HIP: Positively charged form with hydrogens on both ND1 and NE2
Lysine:
LYN: Neutral form with two hydrogens on the zeta nitrogen
LYS: Positively charged form with three hydrogens on the zeta nitrogen
The variant to use for each residue is determined by the following rules:
1. The most common variant at the specified pH is selected.
2. Any Cysteine that participates in a disulfide bond uses the CYX variant regardless of pH.
3. For a neutral Histidine residue, the HID or HIE variant is selected based on which one forms a better hydrogen bond.
You can override these rules by explicitly specifying a variant for any residue. Also keep in mind that this
function will only add hydrogens. It will never remove ones that are already present in the model, regardless
of the specified pH.
Definitions for standard amino acids and nucleotides are built in. You can call loadHydrogenDefinitions() to load
additional definitions for other residue types.
Parameters:
- forcefield (ForceField) the ForceField to use for determining the positions of hydrogens
- pH (float=7.0) the pH based on which to select variants
- variants (list=None) an optional list of variants to use. If this is specified, its length must equal the number
of residues in the model. variants[i] is the name of the variant to use for residue i (indexed starting at 0).
If an element is None, the standard rules will be followed to select a variant for that residue.
- platform (Platform=None) the Platform to use when computing the hydrogen atom positions. If this is None,
the default Platform will be used.
Returns: a list of what variant was actually selected for each residue, in the same format as the variants parameter
"""
# Check the list of variants.
residues = list(self.topology.residues())
if variants is not None:
if len(variants) != len(residues):
raise ValueError("The length of the variants list must equal the number of residues")
else:
variants = [None]*len(residues)
actualVariants = [None]*len(residues)
# Load the residue specifications.
if not Modeller._hasLoadedStandardHydrogens:
Modeller.loadHydrogenDefinitions(os.path.join(os.path.dirname(__file__), 'data', 'hydrogens.xml'))
# Make a list of atoms bonded to each atom.
bonded = {}
for atom in self.topology.atoms():
bonded[atom] = []
for atom1, atom2 in self.topology.bonds():
bonded[atom1].append(atom2)
bonded[atom2].append(atom1)
# Define a function that decides whether a set of atoms form a hydrogen bond, using fairly tolerant criteria.
def isHbond(d, h, a):
if norm(d-a) > 0.35*nanometer:
return False
deltaDH = h-d
deltaHA = a-h
deltaDH /= norm(deltaDH)
deltaHA /= norm(deltaHA)
return acos(dot(deltaDH, deltaHA)) < 50*degree
# Loop over residues.
newTopology = Topology()
newTopology.setUnitCellDimensions(deepcopy(self.topology.getUnitCellDimensions()))
newAtoms = {}
newPositions = []*nanometer
newIndices = []
acceptors = [atom for atom in self.topology.atoms() if atom.element in (elem.oxygen, elem.nitrogen)]
for chain in self.topology.chains():
newChain = newTopology.addChain()
for residue in chain.residues():
newResidue = newTopology.addResidue(residue.name, newChain)
isNTerminal = (residue == chain._residues[0])
isCTerminal = (residue == chain._residues[-1])
if residue.name in Modeller._residueHydrogens:
# Add hydrogens. First select which variant to use.
spec = Modeller._residueHydrogens[residue.name]
variant = variants[residue.index]
if variant is None:
if residue.name == 'CYS':
# If this is part of a disulfide, use CYX.
sulfur = [atom for atom in residue.atoms() if atom.element == elem.sulfur]
if len(sulfur) == 1 and any((atom.residue != residue for atom in bonded[sulfur[0]])):
variant = 'CYX'
if residue.name == 'HIS' and pH > 6.5:
# See if either nitrogen already has a hydrogen attached.
nd1 = [atom for atom in residue.atoms() if atom.name == 'ND1']
ne2 = [atom for atom in residue.atoms() if atom.name == 'NE2']
if len(nd1) != 1 or len(ne2) != 1:
raise ValueError('HIS residue (%d) has the wrong set of atoms' % residue.index)
nd1 = nd1[0]
ne2 = ne2[0]
nd1HasHydrogen = any((atom.element == elem.hydrogen for atom in bonded[nd1]))
ne2HasHydrogen = any((atom.element == elem.hydrogen for atom in bonded[ne2]))
if nd1HasHydrogen and ne2HasHydrogen:
variant = 'HIP'
elif nd1HasHydrogen:
variant = 'HID'
elif ne2HasHydrogen:
variant = 'HIE'
else:
# Estimate the hydrogen positions.
nd1Pos = self.positions[nd1.index]
ne2Pos = self.positions[ne2.index]
hd1Delta = Vec3(0, 0, 0)*nanometer
for other in bonded[nd1]:
hd1Delta += nd1Pos-self.positions[other.index]
hd1Delta *= 0.1*nanometer/norm(hd1Delta)
hd1Pos = nd1Pos+hd1Delta
he2Delta = Vec3(0, 0, 0)*nanometer
for other in bonded[ne2]:
he2Delta += ne2Pos-self.positions[other.index]
he2Delta *= 0.1*nanometer/norm(he2Delta)
he2Pos = ne2Pos+he2Delta
# See whether either hydrogen would form a hydrogen bond.
nd1IsBonded = False
ne2IsBonded = False
for acceptor in acceptors:
if acceptor.residue != residue:
acceptorPos = self.positions[acceptor.index]
if isHbond(nd1Pos, hd1Pos, acceptorPos):
nd1IsBonded = True
break
if isHbond(ne2Pos, he2Pos, acceptorPos):
ne2IsBonded = True
if ne2IsBonded and not nd1IsBonded:
variant = 'HIE'
else:
variant = 'HID'
elif residue.name == 'HIS':
variant = 'HIP'
if variant is not None and variant not in spec.variants:
raise ValueError('Illegal variant for %s residue: %s' % (residue.name, variant))
actualVariants[residue.index] = variant
# Make a list of hydrogens that should be present in the residue.
parents = [atom for atom in residue.atoms() if atom.element != elem.hydrogen]
parentNames = [atom.name for atom in parents]
hydrogens = [h for h in spec.hydrogens if (variant is None and pH <= h.maxph) or (h.variants is None and pH <= h.maxph) or (h.variants is not None and variant in h.variants)]
hydrogens = [h for h in hydrogens if h.terminal is None or (isNTerminal and h.terminal == 'N') or (isCTerminal and h.terminal == 'C')]
hydrogens = [h for h in hydrogens if h.parent in parentNames]
# Loop over atoms in the residue, adding them to the new topology along with required hydrogens.
for parent in residue.atoms():
# Add the atom.
newAtom = newTopology.addAtom(parent.name, parent.element, newResidue)
newAtoms[parent] = newAtom
newPositions.append(deepcopy(self.positions[parent.index]))
if parent in parents:
# Match expected hydrogens with existing ones and find which ones need to be added.
existing = [atom for atom in bonded[parent] if atom.element == elem.hydrogen]
expected = [h for h in hydrogens if h.parent == parent.name]
if len(existing) < len(expected):
# Try to match up existing hydrogens to expected ones.
matches = []
for e in existing:
match = [h for h in expected if h.name == e.name]
if len(match) > 0:
matches.append(match[0])
expected.remove(match[0])
else:
matches.append(None)
# If any hydrogens couldn't be matched by name, just match them arbitrarily.
for i in range(len(matches)):
if matches[i] is None:
matches[i] = expected[-1]
expected.remove(expected[-1])
# Add the missing hydrogens.
for h in expected:
newH = newTopology.addAtom(h.name, elem.hydrogen, newResidue)
newIndices.append(newH.index)
delta = Vec3(0, 0, 0)*nanometer
if len(bonded[parent]) > 0:
for other in bonded[parent]:
delta += self.positions[parent.index]-self.positions[other.index]
else:
delta = Vec3(random.random(), random.random(), random.random())*nanometer
delta *= 0.1*nanometer/norm(delta)
delta += 0.05*Vec3(random.random(), random.random(), random.random())*nanometer
delta *= 0.1*nanometer/norm(delta)
newPositions.append(self.positions[parent.index]+delta)
newTopology.addBond(newAtom, newH)
else:
# Just copy over the residue.
for atom in residue.atoms():
newAtom = newTopology.addAtom(atom.name, atom.element, newResidue)
newAtoms[atom] = newAtom
newPositions.append(deepcopy(self.positions[atom.index]))
for bond in self.topology.bonds():
if bond[0] in newAtoms and bond[1] in newAtoms:
newTopology.addBond(newAtoms[bond[0]], newAtoms[bond[1]])
# The hydrogens were added at random positions. Now use the ForceField to fix them up.
system = forcefield.createSystem(newTopology, rigidWater=False)
atoms = list(newTopology.atoms())
for i in range(system.getNumParticles()):
if atoms[i].element != elem.hydrogen:
# This is a heavy atom, so make it immobile.
system.setParticleMass(i, 0)
if platform is None:
context = Context(system, VerletIntegrator(0.0))
else:
context = Context(system, VerletIntegrator(0.0), platform)
context.setPositions(newPositions)
LocalEnergyMinimizer.minimize(context)
self.topology = newTopology
self.positions = context.getState(getPositions=True).getPositions()
return actualVariants
class FIRE():
_explorer = None
_initialized = False
_context = None
_integrator = None
_timestep = Quantity(0.001, u.picoseconds)
_tolerance = None
_alpha = 0.1
_dt_max = Quantity(0.01, u.picoseconds)
_f_inc = 1.1
_f_dec = 0.5
_f_alpha = 0.99
_N_min = 5
def __init__(self, explorer):
self._explorer = explorer
def _initialize(self):
system = self._explorer.context.getSystem()
platform = self._explorer.context.getPlatform()
properties = {}
if platform.getName() == 'CUDA':
properties['CudaPrecision'] = 'mixed'
self._integrator = FIREMinimizationIntegrator(
timestep=self._timestep,
tolerance=self._tolerance,
alpha=self._alpha,
dt_max=self._dt_max,
f_inc=self._f_inc,
f_dec=self._f_dec,
f_alpha=self._f_alpha,
N_min=self._N_min)
self._context = Context(system, self._integrator, platform, properties)
self._initialized = True
def set_parameters(self,
timestep=Quantity(1.0, u.femtoseconds),
tolerance=None,
alpha=0.1,
dt_max=Quantity(10.0, u.femtoseconds),
f_inc=1.1,
f_dec=0.5,
f_alpha=0.99,
N_min=5):
self._timestep = timestep.in_units_of(u.picoseconds)
self._tolerance = tolerance
self._alpha = alpha
self._dt_max = dt_max.in_units_of(u.picoseconds)
self._f_inc = f_inc
self._f_dec = f_dec
self._f_alpha = f_alpha
self._N_min = N_min
self._initialize()
def replicate_parameters(self, explorer):
timestep = explorer.quench.fire._timestep
tolerance = explorer.quench.fire._tolerance
alpha = explorer.quench.fire._alpha
dt_max = explorer.quench.fire._dt_max
f_inc = explorer.quench.fire._f_inc
f_dec = explorer.quench.fire._f_dec
f_alpha = explorer.quench.fire._f_alpha
N_min = explorer.quench.fire._N_min
self.set_parameters(timestep, tolerance, alpha, dt_max, f_inc, f_dec,
f_alpha, N_min)
def _set_coordinates(self, coordinates):
self._context.setPositions(coordinates)
def _get_coordinates(self):
return self._context.getState(getPositions=True).getPositions(
asNumpy=True)
def _coordinates_to_explorer(self):
self._explorer.set_coordinates(self._get_coordinates())
def _coordinates_from_explorer(self):
self._set_coordinates(self._explorer.get_coordinates())
def run(self, steps=0):
if not self._initialized:
self._initialize()
self._coordinates_from_explorer()
try:
if steps == 0:
while self._integrator.getGlobalVariableByName(
'converged') < 1:
self._integrator.step(50)
else:
self._integrator.step(steps)
self._coordinates_to_explorer()
except Exception as e:
if str(e) == 'Particle coordinate is nan':
print(
'NaN encountered in FIRE minimizer; falling back to L-BFGS after resetting positions'
)
self._explorer.quench.l_bfgs()
else:
raise e
self._initialize()
def __call__(self, *args, **kwargs):
return self.run(*args, **kwargs)
class Explorer():
topology = None
context = None
pbc = False
n_atoms = 0
n_dof = 0
md = None
quench = None
move = None
def __init__(self, topology=None, system=None, pbc=False, platform='CUDA'):
from .md import MD
from .quench import Quench
from .move import Move
from .distance import Distance
from .acceptance import Acceptance
if topology is None:
raise ValueError('topology is needed')
if system is None:
raise ValueError('system is needed')
integrator = LangevinIntegrator(0 * u.kelvin, 1.0 / u.picoseconds,
2.0 * u.femtoseconds)
#integrator.setConstraintTolerance(0.00001)
if platform == 'CUDA':
platform = Platform.getPlatformByName('CUDA')
properties = {'CudaPrecision': 'mixed'}
elif platform == 'CPU':
platform = Platform.getPlatformByName('CPU')
properties = {}
self.topology = topology
self.context = Context(system, integrator, platform, properties)
self.n_atoms = msm.get(self.context, target='system', n_atoms=True)
self.n_dof = 0
for i in range(system.getNumParticles()):
if system.getParticleMass(i) > 0 * u.dalton:
self.n_dof += 3
for i in range(system.getNumConstraints()):
p1, p2, distance = system.getConstraintParameters(i)
if system.getParticleMass(
p1) > 0 * u.dalton or system.getParticleMass(
p2) > 0 * u.dalton:
self.n_dof -= 1
if any(
type(system.getForce(i)) == CMMotionRemover
for i in range(system.getNumForces())):
self.n_dof -= 3
self.pbc = pbc
if self.pbc:
raise NotImplementedError
self.md = MD(self)
self.quench = Quench(self)
self.move = Move(self)
self.distance = Distance(self)
self.acceptance = Acceptance(self)
def _copy(self):
topology = self.topology
coordinates = self.get_coordinates()
system = self.context.getSystem()
platform = self.context.getPlatform().getName()
pbc = self.pbc
tmp_explorer = Explorer(topology, system, pbc, platform)
tmp_explorer.set_coordinates(coordinates)
for ii, jj in vars(tmp_explorer.md).items():
if not ii.startswith('_'):
if jj._initialized:
jj.replicate_parameters(self)
for ii, jj in vars(tmp_explorer.quench).items():
if not ii.startswith('_'):
if jj._initialized:
jj.replicate_parameters(self)
for ii, jj in vars(tmp_explorer.move).items():
if not ii.startswith('_'):
if jj._initialized:
jj.replicate_parameters(self)
return tmp_explorer
def replicate(self, times=1):
from copy import deepcopy
if times == 1:
output = self._copy()
else:
output = [self._copy() for ii in range(times)]
return output
def set_coordinates(self, coordinates):
self.context.setPositions(coordinates)
def get_coordinates(self):
return self.context.getState(getPositions=True).getPositions(
asNumpy=True)
def set_velocities(self, velocities):
self.context.setVelocities(velocities)
def set_velocities_to_temperature(self, temperature):
self.context.setVelocitiesToTemperature(temperature)
def get_velocities(self):
return self.context.getState(getVelocities=True).getVelocities(
asNumpy=True)
def get_temperature(self):
return (2 * self.context.getState(getEnergy=True).getKineticEnergy() /
(self.n_dof * u.MOLAR_GAS_CONSTANT_R)).in_units_of(u.kelvin)
def get_potential_energy(self):
energy = self.context.getState(getEnergy=True).getPotentialEnergy()
return energy
def get_potential_energy_gradient(self):
gradient = -self.context.getState(getForces=True).getForces(
asNumpy=True)
gradient = gradient.ravel() * gradient.unit
return gradient
def get_potential_energy_hessian(self,
mass_weighted=False,
symmetric=True):
"""OpenMM single frame hessian evaluation
Since OpenMM doesnot provide a Hessian evaluation method, we used finite difference on forces
from: https://leeping.github.io/forcebalance/doc/html/api/openmmio_8py_source.html
Returns
-------
hessian: np.array with shape 3N x 3N, N = number of "real" atoms
The result hessian matrix.
The row indices are fx0, fy0, fz0, fx1, fy1, ...
The column indices are x0, y0, z0, x1, y1, ..
The unit is kilojoule / (nanometer^2 * mole * dalton) => 10^24 s^-2
"""
n_dof = self.n_atoms * 3
pos = self.get_coordinates()
hessian = np.empty(
(n_dof, n_dof),
dtype=float) * u.kilojoules_per_mole / (u.nanometers**2)
# finite difference step size
diff = 0.0001 * u.nanometer
coef = 1.0 / (2.0 * diff) # 1/2h
for i in range(self.n_atoms):
# loop over the x, y, z coordinates
for j in range(3):
# plus perturbation
pos[i][j] += diff
self.set_coordinates(pos)
grad_plus = self.get_potential_energy_gradient()
# minus perturbation
pos[i][j] -= 2 * diff
self.set_coordinates(pos)
grad_minus = self.get_potential_energy_gradient()
# set the perturbation back to zero
pos[i][j] += diff
# fill one row of the hessian matrix
hessian[i * 3 + j] = (grad_plus - grad_minus) * coef
if mass_weighted:
mass = np.array([
self.context.getSystem().getParticleMass(k).value_in_unit(
u.dalton) for k in range(self.n_atoms)
]) * u.dalton
mass_weight = 1.0 / np.sqrt(mass) * (mass.unit**-0.5)
mass_weight = np.repeat(mass_weight, 3) * mass_weight.unit
hessian = np.multiply(
hessian, mass_weight) * hessian.unit * mass_weight.unit
hessian = np.multiply(
hessian,
mass_weight[:, np.newaxis]) * hessian.unit * mass_weight.unit
# make hessian symmetric by averaging upper right and lower left
if symmetric:
hessian += hessian.T * hessian.unit
hessian *= 0.5
# recover the original position
self.set_coordinates(pos)
return hessian
class Langevin():
_explorer = None
_initialized = False
_context = None
_integrator = None
_timestep = Quantity(value=2.0, unit=u.femtosecond)
_temperature = Quantity(value=298.0, unit=u.kelvin)
_collision_rate = Quantity(value=1.0, unit=1.0 / u.picosecond)
def __init__(self, explorer):
self._explorer = explorer
def _initialize(self):
system = self._explorer.context.getSystem()
platform = self._explorer.context.getPlatform()
properties = {}
if platform.getName() == 'CUDA':
properties['CudaPrecision'] = 'mixed'
self._integrator = LangevinIntegrator(self._temperature,
self._collision_rate,
self._timestep)
self._context = Context(system, self._integrator, platform, properties)
self._initialized = True
def set_parameters(self,
temperature=Quantity(value=298.0, unit=u.kelvin),
collision_rate=Quantity(value=1.0,
unit=1.0 / u.picosecond),
timestep=Quantity(value=2.0, unit=u.femtosecond)):
self._timestep = timestep
self._temperature = temperature
self._collision_rate = collision_rate
if self._initialized:
self._integrator.setFriction(
self._collision_rate.value_in_unit(u.picosecond**-1))
self._integrator.setTemperature(
self._temperature.value_in_unit(u.kelvin))
self._integrator.setStepSize(
self._timestep.value_in_unit(u.picoseconds))
else:
self._initialize()
def get_parameters(self):
parameters = {
'timestep': self._timestep,
'temperature': self._temperature,
'collision_rate': self._collision_rate
}
return parameters
def replicate_parameters(self, explorer):
timestep = explorer.md.langevin._timestep
temperature = explorer.md.langevin._temperature
collision_rate = explorer.md.langevin._collision_rate
self.set_paramters(temperature, collision_rate, timestep)
def _set_coordinates(self, coordinates):
self._context.setPositions(coordinates)
def _get_coordinates(self):
return self._context.getState(getPositions=True).getPositions(
asNumpy=True)
def _set_velocities(self, velocities):
self._context.setVelocities(velocities)
def _get_velocities(self):
return self._context.getState(getVelocities=True).getVelocities(
asNumpy=True)
def _coordinates_to_explorer(self):
self._explorer.set_coordinates(self._get_coordinates())
def _coordinates_from_explorer(self):
self._set_coordinates(self._explorer.get_coordinates())
def _velocities_to_explorer(self):
self._explorer.set_velocities(self._get_velocities())
def _velocities_from_explorer(self):
self._set_velocities(self._explorer.get_velocities())
def get_time(self):
return self._context.getState().getTime()
def run(self, steps=0):
if not self._initialized:
self._initialize()
self._coordinates_from_explorer()
self._velocities_from_explorer()
self._integrator.step(steps)
self._coordinates_to_explorer()
self._velocities_to_explorer()
def __call__(self, *args, **kwargs):
return self.run(*args, **kwargs)
def addHydrogens(self, forcefield=None, pH=None, variants=None, platform=None):
"""Add missing hydrogens to the model.
This function automatically changes compatible residues into their constant-pH variant if no variant is specified.:
Aspartic acid:
AS4: Form with a 2 hydrogens on each one of the delta oxygens (syn,anti)
It has 5 titration states.
Alternative:
AS2: Has 2 hydrogens (syn, anti) on one of the delta oxygens
It has 3 titration states.
Cysteine:
CYS: Neutral form with a hydrogen on the sulfur
CYX: No hydrogen on the sulfur (either negatively charged, or part of a disulfide bond)
Glutamic acid:
GL4: Form with a 2 hydrogens on each one of the epsilon oxygens (syn,anti)
It has 5 titration states.
Histidine:
HIP: Positively charged form with hydrogens on both ND1 and NE2
It has 3 titration states.
The variant to use for each residue is determined by the following rules:
1. Any Cysteine that participates in a disulfide bond uses the CYX variant regardless of pH.
2. Other residues are all set to maximally protonated state, which can be updated using a proton drive
You can override these rules by explicitly specifying a variant for any residue. To do that, provide a list for the
'variants' parameter, and set the corresponding element to the name of the variant to use.
A special case is when the model already contains a hydrogen that should not be present in the desired variant.
If you explicitly specify a variant using the 'variants' parameter, the residue will be modified to match the
desired variant, removing hydrogens if necessary. On the other hand, for residues whose variant is selected
automatically, this function will only add hydrogens. It will never remove ones that are already present in the
model.
Definitions for standard amino acids and nucleotides are built in. You can call loadHydrogenDefinitions() to load
additional definitions for other residue types.
Parameters
----------
forcefield : ForceField=None
the ForceField to use for determining the positions of hydrogens.
If this is None, positions will be picked which are generally
reasonable but not optimized for any particular ForceField.
pH : None,
Kept for compatibility reasons. Has no effect.
variants : list=None
an optional list of variants to use. If this is specified, its
length must equal the number of residues in the model. variants[i]
is the name of the variant to use for residue i (indexed starting at
0). If an element is None, the standard rules will be followed to
select a variant for that residue.
platform : Platform=None
the Platform to use when computing the hydrogen atom positions. If
this is None, the default Platform will be used.
Returns
-------
list
a list of what variant was actually selected for each residue,
in the same format as the variants parameter
Notes
-----
This function does not use a pH specification. The argument is kept for compatibility reasons.
"""
# Check the list of variants.
if pH is not None:
print("Ignored pH argument provided for constant-pH residues.")
residues = list(self.topology.residues())
if variants is not None:
if len(variants) != len(residues):
raise ValueError(
"The length of the variants list must equal the number of residues"
)
else:
variants = [None] * len(residues)
actualVariants = [None] * len(residues)
# Load the residue specifications.
if not Modeller._hasLoadedStandardHydrogens:
Modeller.loadHydrogenDefinitions(
os.path.join(
os.path.dirname(__file__), "data", "hydrogens-amber10-constph.xml"
)
)
# Make a list of atoms bonded to each atom.
bonded = {}
for atom in self.topology.atoms():
bonded[atom] = []
for atom1, atom2 in self.topology.bonds():
bonded[atom1].append(atom2)
bonded[atom2].append(atom1)
# Define a function that decides whether a set of atoms form a hydrogen bond, using fairly tolerant criteria.
def isHbond(d, h, a):
if norm(d - a) > 0.35 * nanometer:
return False
deltaDH = h - d
deltaHA = a - h
deltaDH /= norm(deltaDH)
deltaHA /= norm(deltaHA)
return acos(dot(deltaDH, deltaHA)) < 50 * degree
# Loop over residues.
newTopology = Topology()
newTopology.setPeriodicBoxVectors(self.topology.getPeriodicBoxVectors())
newAtoms = {}
newPositions = [] * nanometer
newIndices = []
acceptors = [
atom
for atom in self.topology.atoms()
if atom.element in (elem.oxygen, elem.nitrogen)
]
for chain in self.topology.chains():
newChain = newTopology.addChain(chain.id)
for residue in chain.residues():
newResidue = newTopology.addResidue(residue.name, newChain, residue.id)
isNTerminal = residue == chain._residues[0]
isCTerminal = residue == chain._residues[-1]
if residue.name in Modeller._residueHydrogens:
# Add hydrogens. First select which variant to use.
spec = Modeller._residueHydrogens[residue.name]
variant = variants[residue.index]
if variant is None:
if residue.name == "CYS":
# If this is part of a disulfide, use CYX.
sulfur = [
atom
for atom in residue.atoms()
if atom.element == elem.sulfur
]
if len(sulfur) == 1 and any(
(atom.residue != residue for atom in bonded[sulfur[0]])
):
variant = "CYX"
if residue.name == "HIS":
variant = "HIP"
if residue.name == "GLU":
variant = "GL4"
if residue.name == "ASP":
variant = "AS4"
if variant is not None and variant not in spec.variants:
raise ValueError(
"Illegal variant for %s residue: %s"
% (residue.name, variant)
)
actualVariants[residue.index] = variant
removeExtraHydrogens = variants[residue.index] is not None
# Make a list of hydrogens that should be present in the residue.
parents = [
atom
for atom in residue.atoms()
if atom.element != elem.hydrogen
]
parentNames = [atom.name for atom in parents]
hydrogens = [
h
for h in spec.hydrogens
if (variant is None)
or (h.variants is None)
or (h.variants is not None and variant in h.variants)
]
hydrogens = [
h
for h in hydrogens
if h.terminal is None
or (isNTerminal and h.terminal == "N")
or (isCTerminal and h.terminal == "C")
]
hydrogens = [h for h in hydrogens if h.parent in parentNames]
# Loop over atoms in the residue, adding them to the new topology along with required hydrogens.
for parent in residue.atoms():
# Check whether this is a hydrogen that should be removed.
if (
removeExtraHydrogens
and parent.element == elem.hydrogen
and not any(parent.name == h.name for h in hydrogens)
):
continue
# Add the atom.
newAtom = newTopology.addAtom(
parent.name, parent.element, newResidue
)
newAtoms[parent] = newAtom
newPositions.append(deepcopy(self.positions[parent.index]))
if parent in parents:
# Match expected hydrogens with existing ones and find which ones need to be added.
existing = [
atom
for atom in bonded[parent]
if atom.element == elem.hydrogen
]
expected = [h for h in hydrogens if h.parent == parent.name]
if len(existing) < len(expected):
# Try to match up existing hydrogens to expected ones.
matches = []
for e in existing:
match = [h for h in expected if h.name == e.name]
if len(match) > 0:
matches.append(match[0])
expected.remove(match[0])
else:
matches.append(None)
# If any hydrogens couldn't be matched by name, just match them arbitrarily.
for i in range(len(matches)):
if matches[i] is None:
matches[i] = expected[-1]
expected.remove(expected[-1])
# Add the missing hydrogens.
for h in expected:
newH = newTopology.addAtom(
h.name, elem.hydrogen, newResidue
)
newIndices.append(newH.index)
delta = Vec3(0, 0, 0) * nanometer
if len(bonded[parent]) > 0:
for other in bonded[parent]:
delta += (
self.positions[parent.index]
- self.positions[other.index]
)
else:
delta = (
Vec3(
random.random(),
random.random(),
random.random(),
)
* nanometer
)
delta *= 0.1 * nanometer / norm(delta)
delta += (
0.05
* Vec3(
random.random(),
random.random(),
random.random(),
)
* nanometer
)
delta *= 0.1 * nanometer / norm(delta)
newPositions.append(
self.positions[parent.index] + delta
)
newTopology.addBond(newAtom, newH)
else:
# Just copy over the residue.
for atom in residue.atoms():
newAtom = newTopology.addAtom(
atom.name, atom.element, newResidue
)
newAtoms[atom] = newAtom
newPositions.append(deepcopy(self.positions[atom.index]))
for bond in self.topology.bonds():
if bond[0] in newAtoms and bond[1] in newAtoms:
newTopology.addBond(newAtoms[bond[0]], newAtoms[bond[1]])
# The hydrogens were added at random positions. Now perform an energy minimization to fix them up.
if forcefield is not None:
# Use the ForceField the user specified.
system = forcefield.createSystem(newTopology, rigidWater=False)
atoms = list(newTopology.atoms())
for i in range(system.getNumParticles()):
if atoms[i].element != elem.hydrogen:
# This is a heavy atom, so make it immobile.
system.setParticleMass(i, 0)
else:
# Create a System that restrains the distance of each hydrogen from its parent atom
# and causes hydrogens to spread out evenly.
system = System()
nonbonded = CustomNonbondedForce("100/((r/0.1)^4+1)")
bonds = HarmonicBondForce()
angles = HarmonicAngleForce()
system.addForce(nonbonded)
system.addForce(bonds)
system.addForce(angles)
bondedTo = []
for atom in newTopology.atoms():
nonbonded.addParticle([])
if atom.element != elem.hydrogen:
system.addParticle(0.0)
else:
system.addParticle(1.0)
bondedTo.append([])
for atom1, atom2 in newTopology.bonds():
if atom1.element == elem.hydrogen or atom2.element == elem.hydrogen:
bonds.addBond(atom1.index, atom2.index, 0.1, 100_000.0)
bondedTo[atom1.index].append(atom2)
bondedTo[atom2.index].append(atom1)
for residue in newTopology.residues():
if residue.name == "HOH":
# Add an angle term to make the water geometry correct.
atoms = list(residue.atoms())
oindex = [
i for i in range(len(atoms)) if atoms[i].element == elem.oxygen
]
if len(atoms) == 3 and len(oindex) == 1:
hindex = list(set([0, 1, 2]) - set(oindex))
angles.addAngle(
atoms[hindex[0]].index,
atoms[oindex[0]].index,
atoms[hindex[1]].index,
1.824,
836.8,
)
else:
# Add angle terms for any hydroxyls.
for atom in residue.atoms():
index = atom.index
if (
atom.element == elem.oxygen
and len(bondedTo[index]) == 2
and elem.hydrogen in (a.element for a in bondedTo[index])
):
angles.addAngle(
bondedTo[index][0].index,
index,
bondedTo[index][1].index,
1.894,
460.24,
)
if platform is None:
context = Context(system, VerletIntegrator(0.0))
else:
context = Context(system, VerletIntegrator(0.0), platform)
context.setPositions(newPositions)
LocalEnergyMinimizer.minimize(context, 1.0, 50)
self.topology = newTopology
self.positions = context.getState(getPositions=True).getPositions()
del context
return actualVariants
