Running OpenMM Simulation with GROMACS Files

There are popular ways to model biomolecular systems. Because solvation effects are often (always?) critically important to biological function, we need some way to model the solvent. The two popular approaches are to employ a continuum model with a dielectric constant equal to that of the bulk solvent or to model the solvent models directly in your system. These two approaches are termed _implicit_ and _explicit_ solvation, respectively.

The next 2 sections present examples using a Generalized Born implicit solvent model and explicit solvent based on the TIP3P water model. All of the files and examples here are included in the examples/gromacs directory of the ParmEd release.

Generalized Born

For the purposes of this example, we are using an alanine pentapeptide. You can find the following files that you will need for this demonstration in the examples/gromacs directory of the ParmEd distribution:

  • dhfr_gb.top

  • dhfr_gb.gro

The following sample script (simulate_gromacs_gb.py in the ParmEd distribution) will set up and run the simulation using OpenMM:

#!/usr/bin/env python
from __future__ import division, print_function

import sys

# OpenMM Imports
import simtk.openmm as mm
import simtk.openmm.app as app

# ParmEd Imports
from parmed import load_file
from parmed.openmm.reporters import NetCDFReporter
from parmed import unit as u

# Load the Gromacs files
print('Loading Gromacs files...')
top = load_file('dhfr_gas.top')
gro = load_file('dhfr_gas.gro')

# Create the OpenMM system
print('Creating OpenMM System')
system = top.createSystem(nonbondedMethod=app.NoCutoff,
                          constraints=app.HBonds, implicitSolvent=app.GBn2,
                          implicitSolventSaltConc=0.1*u.moles/u.liter,
)

# Create the integrator to do Langevin dynamics
integrator = mm.LangevinIntegrator(
                        300*u.kelvin,       # Temperature of heat bath
                        1.0/u.picoseconds,  # Friction coefficient
                        2.0*u.femtoseconds, # Time step
)

# Define the platform to use; CUDA, OpenCL, CPU, or Reference. Or do not specify
# the platform to use the default (fastest) platform
platform = mm.Platform.getPlatformByName('CUDA')
prop = dict(CudaPrecision='mixed') # Use mixed single/double precision

# Create the Simulation object
sim = app.Simulation(top.topology, system, integrator, platform, prop)

# Set the particle positions
sim.context.setPositions(gro.positions)

# Minimize the energy
print('Minimizing energy')
sim.minimizeEnergy(maxIterations=500)

# Set up the reporters to report energies and coordinates every 100 steps
sim.reporters.append(
        app.StateDataReporter(sys.stdout, 100, step=True, potentialEnergy=True,
                              kineticEnergy=True, temperature=True)
)
sim.reporters.append(
        NetCDFReporter('dhfr_gb.nc', 100, crds=True)
)

# Run dynamics
print('Running dynamics')
sim.step(10000)

Now I’ll dissect the script to help you understand what is happening at each step. We will divide the script into the sections following the print statements that announce when each stage begins.

Loading Gromacs files

In this stage, we simply load the Gromacs topology file, using automatic file-type detection. This will instantiate the GromacsTopologyFile object from the input topology and coordinate files. After this command, top will contain a full description of every particle, the parameters defining their interactions, and their positions.

Create the OpenMM System

This command creates an OpenMM System object from the information stored in top. It contains multiple Force instances for the bonds, angles, periodic torsions, and nonbonded (electrostatic and van der Waals) interactions. It is in this function that we define the potential parameters we want to use. In this example, we have chosen the default values for each parameter except the ones specified. In particular:

  • nonbondedMethod=app.NoCutoff indicates we do not want to use a cutoff for nonbonded interactions. If you wanted to use a cutoff, you could use app.CutoffNonPeriodic instead (since this system does not use periodic boundary conditions)

  • constraints=app.HBonds indicates we want to constrain all bonds in which at least one atom is a Hydrogen (i.e., SHAKE or SETTLE for water). Other options are None (no constraints), app.AllBonds, or app.HAngles. For the most part, these are self-explanatory, but it is worth noting that app.HAngles constrains all bonds and the distance between the 1-3 pairs of angles in which one of those atoms is a hydrogen.

  • implicitSolvent=app.GBn2 indicates we want to use the second GBneck model described in Nguyen et al., J. Chem. Theory Comput., 2014 9(4) p. 2020-2034. Other options are app.HCT, app.OBC1, app.OBC2, and app.GBn. These correspond to values of 1, 2, 5, 7 (and 8 for app.GBn2) to the igb variable in AMBER input files.

  • implicitSolventSaltConc=0.1*u.liters/u.mole indicates we want to model a ca. 0.1 molar solution of monovalent ions using a Debye screening model.

Create the integrator to do Langevin Dynamics

In this stage we specify an integrator. Common choices are LangevinIntegrator (as we’ve chosen here) to do simulations in the NVT ensemble and VerletIntegrator that allows us to do simulations either at constant energy or temperature if using the AndersenThermostat. In this example, we’ve chosen the Langevin integrator with a target temperature of 300 K, a friction coefficient of 1/ps and a time step of 2 fs.

Define the platform

In this stage, we define the platform we want to use. In this example, we have chosen the CUDA platform, but this may not be available on every machine since it only runs on NVidia GPU hardware. Other choices are OpenCL (which will run on a variety of GPUs including those made by AMD/ATI and CPUs), CPU (which is an optimized version that runs natively on CPUs), and Reference (often quite slow).

The properties can be set for each platform. In this case, we specified that we wanted to use a mixed precision model (a good compromise between speed and precision).

Create the Simulation object

This step creates a Simulation object that will be used to run the actual simulations. If we wanted OpenMM to simply pick the fastest platform for us (rather than specify one directly), we could omit the platform and prop arguments.

Set the particle positions

This stage is very important. In this step, we set the particle positions stored in the gro object to our object. If you omit this step, you will be warned that positions were not set. These particle positions have been parsed from the GRO coordinate file, but any supported format will automatically be loaded by that command (including PDB, PDBx/mmCIF, etc.).

Minimize the energy

This stage performs a basic energy minimization to relax particle positions. This particular invocation will perform at most 500 iterations.

Set up the reporters

This stage defines reporters that will “report” on the status of the simulation periodically throughout the simulation. The first is the StateDataReporter which will print out a summary of energies and temperatures every 100 steps. This reporter directs the printout to standard output (the screen), sys.stdout can be replaced with a different file-like object or a file name.

The second reporter is a NetCDF trajectory reporter, which is written in the Amber NetCDF format. You can also use the native DCDReporter reporter in OpenMM to print DCD-format trajectories.

Running dynamics

This is the stage that actually runs the MD. In this case, we are running 10,000 steps of MD. The wiki page with “Common recipes” has information regarding running a long simulation in chunks.

Explicit Solvent

For the purposes of this example, we are using an alanine dipeptide solvated in a box of water. You can find the following files that you will need for this demonstration in the examples/amber directory of the ParmEd distribution:

  • dhfr_pme.top

  • dhfr_pme.gro

The following sample script (simulate_gromacs_pme.py in the ParmEd distribution) will set up and run the simulation using OpenMM:

#!/usr/bin/env python
from __future__ import division, print_function

import sys

# OpenMM Imports
import simtk.openmm as mm
import simtk.openmm.app as app

# ParmEd Imports
from parmed import load_file
from parmed.openmm.reporters import NetCDFReporter
from parmed import unit as u

# Load the Gromacs files
print('Loading Gromacs files...')
top = load_file('dhfr_pme.top')
gro = load_file('dhfr_pme.gro')

# Transfer the unit cell information from the GRO file to the top object
top.box = gro.box[:]

# Create the OpenMM system
print('Creating OpenMM System')
system = top.createSystem(nonbondedMethod=app.PME,
                          nonbondedCutoff=8.0*u.angstroms,
                          constraints=app.HBonds,
)

# Create the integrator to do Langevin dynamics
integrator = mm.LangevinIntegrator(
                        300*u.kelvin,       # Temperature of heat bath
                        1.0/u.picoseconds,  # Friction coefficient
                        2.0*u.femtoseconds, # Time step
)

# Define the platform to use; CUDA, OpenCL, CPU, or Reference. Or do not specify
# the platform to use the default (fastest) platform
platform = mm.Platform.getPlatformByName('CUDA')
prop = dict(CudaPrecision='mixed') # Use mixed single/double precision

# Create the Simulation object
sim = app.Simulation(top.topology, system, integrator, platform, prop)

# Set the particle positions
sim.context.setPositions(gro.positions)

# Minimize the energy
print('Minimizing energy')
sim.minimizeEnergy(maxIterations=500)

# Set up the reporters to report energies and coordinates every 100 steps
sim.reporters.append(
        app.StateDataReporter(sys.stdout, 100, step=True, potentialEnergy=True,
                              kineticEnergy=True, temperature=True, volume=True,
                              density=True)
)
sim.reporters.append(NetCDFReporter('dhfr_pme.nc', 100, crds=True))

# Run dynamics
print('Running dynamics')
sim.step(10000)

Now we’ll dissect the script to help you understand what is happening at each step. We will divide the script into the sections following the print statements that announce when each stage begins.

Loading Gromacs files

In this stage, we simply load the Gromacs topology file, using automatic file-type detection. This will instantiate the GromacsTopologyFile object from the input topology and coordinate files. After this command, top will contain a full description of every particle, the parameters defining their interactions, and their positions.

Create the OpenMM system

This command creates an OpenMM System object from the information stored in ala5_gas. It contains multiple Force instances for the bonds, angles, periodic torsions, and nonbonded (electrostatic and van der Waals) interactions. It is in this function that we define the potential parameters we want to use. In this example, we have chosen the default values for each parameter except the ones specified. In particular:

  • nonbondedMethod=app.PME indicates we want to use the Particle Mesh Ewald method to compute the full-range electrostatics.

  • nonbondedCutoff=8.0*u.angstroms indicates we want to use an 8 Angstrom cutoff for the Lennard-Jones interaction (as well as the direct-space part of the Ewald sum).

  • constraints=app.HBonds indicates that we want to constrain all bonds in which at least one atom is hydrogen

If there are any other force objects you want to define, they can be added to the system after this step (like, for instance, positional restraints to a reference structure).

Create the integrator to do Langevin Dynamics

In this stage we specify an integrator. Common choices are LangevinIntegrator (as we’ve chosen here) to do simulations in the NVT ensemble and VerletIntegrator that allows us to do simulations either at constant energy or temperature if using the AndersenThermostat. In this example, we’ve chosen the Langevin integrator with a target temperature of 300 K, a friction coefficient of 1/ps and a time step of 2 fs.

Define the platform

In this stage, we define the platform we want to use. In this example we have chosen the CUDA platform, but this may not be available on every machine since it only runs on NVidia GPU hardware. Other choices are OpenCL (which will run on a variety of GPUs including those made by AMD/ATI and CPUs), CPU (which is an optimized version that runs natively on CPUs), and Reference (often quite slow).

The properties can be set for each platform. In this case, we specified that we wanted to use a mixed precision model (a good compromise between speed and precision).

Create the Simulation object

This step creates a Simulation object that will be used to run the actual simulations. If we wanted OpenMM to simply pick the fastest platform for us (rather than specify one directly), we could omit the platform and prop arguments.

Set the particle positions

This stage is very important. In this step, we set the particle positions stored in the gro object to our object. If you omit this step, you will be warned that positions were not set. These particle positions have been parsed from the GRO coordinate file, but any supported format will automatically be loaded by that command (including PDB, PDBx/mmCIF, etc.).

Minimize the energy

This stage performs a basic energy minimization to relax particle positions. This particular invocation will perform at most 500 iterations.

Set up the reporters

This stage defines reporters that will “report” on the status of the simulation periodically throughout the simulation. The first is the StateDataReporter which will print out a summary of energies and temperatures every 100 steps. This reporter directs the printout to standard output (the screen), sys.stdout can be replaced with a different file-like object or a file name.

The second reporter is a NetCDF trajectory reporter, which is written in the Amber NetCDF format. You can also use the native DCDReporter reporter in OpenMM to print DCD-format trajectories.

Running dynamics

This is the stage that actually runs the MD. In this case, we are running 10,000 steps of MD. The wiki page with “Common recipes” has information regarding running a long simulation in chunks.