Running OpenMM Simulation with AMBER 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/amber`` 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/amber`` directory of the ParmEd distribution: * ``ala5_gas.parm7`` * ``ala5_gas.rst7`` The following sample script (``simulate_amber_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, unit as u from parmed.openmm import StateDataReporter, NetCDFReporter # Load the Amber files print('Loading AMBER files...') ala5_gas = load_file('ala5_gas.parm7', 'ala5_gas.rst7') # Create the OpenMM system print('Creating OpenMM System') system = ala5_gas.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(ala5_gas.topology, system, integrator, platform, prop) # Set the particle positions sim.context.setPositions(ala5_gas.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( StateDataReporter(sys.stdout, 100, step=True, potentialEnergy=True, kineticEnergy=True, temperature=True) ) sim.reporters.append( NetCDFReporter('ala5_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 Amber files ~~~~~~~~~~~~~~~~~~~ In this stage, we simply instantiate the :class:`AmberParm ` object from the input topology and coordinate files. After this command, ``ala5_gas`` 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.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 ``ala5_gas`` object to our object. If you omit this step, you can get strange results or other errors like segmentation violations. These particle positions have been parsed from the input coordinate file, although if you had a PDB file you could use the OpenMM ``PDBFile`` object as a source of coordinates instead. 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 an ``StateDataReporter`` which will print out a summary of energies and temperatures every 100 steps. Unlike the ``StateDataReporter`` in OpenMM, this reporter prints values in the AKMA unit system (Angstrom, Kilocalorie per mole, and atomic mass units). 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: * ``ala2_solv.parm7`` * ``ala2_solv.rst7`` The following sample script (``simulate_amber_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, unit as u from parmed.openmm import StateDataReporter, NetCDFReporter # Load the Amber files print('Loading AMBER files...') ala2_solv = load_file('ala2_solv.parm7', 'ala2_solv.rst7') # Create the OpenMM system print('Creating OpenMM System') system = ala2_solv.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(ala2_solv.topology, system, integrator, platform, prop) # Set the particle positions sim.context.setPositions(ala2_solv.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( StateDataReporter(sys.stdout, 100, step=True, potentialEnergy=True, kineticEnergy=True, temperature=True, volume=True, density=True) ) sim.reporters.append(NetCDFReporter('ala2_solv.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 Amber files ~~~~~~~~~~~~~~~~~~~ In this stage, we simply instantiate the :class:`AmberParm ` object from the input topology and coordinate files. After this command, ``ala2_solv`` 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 ``ala5_gas`` object to our object. If you omit this step, you can get strange results or other errors like segmentation violations. These particle positions have been parsed from the input coordinate file, although if you had a PDB file you could use the OpenMM ``PDBFile`` object as a source of coordinates instead. 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 an :class:`StateDataReporter` which will print out a summary of energies and temperatures every 100 steps. Unlike the ``StateDataReporter`` in OpenMM, this reporter prints values in the AKMA unit system (Angstrom, Kilocalorie per mole, and atomic mass units). 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.