.. default-domain:: py .. _running-simulations: Running Simulations ################### .. _a-first-example: A First Example *************** Let’s begin with our first example of an OpenMM script. It loads a PDB file called :file:`input.pdb` that defines a biomolecular system, parameterizes it using the Amber14 force field and TIP3P-FB water model, energy minimizes it, simulates it for 10,000 steps with a Langevin integrator, and saves a snapshot frame to a PDB file called :file:`output.pdb` every 1000 time steps. .. samepage:: :: from openmm.app import * from openmm import * from openmm.unit import * from sys import stdout pdb = PDBFile('input.pdb') forcefield = ForceField('amber14-all.xml', 'amber14/tip3pfb.xml') system = forcefield.createSystem(pdb.topology, nonbondedMethod=PME, nonbondedCutoff=1*nanometer, constraints=HBonds) integrator = LangevinMiddleIntegrator(300*kelvin, 1/picosecond, 0.004*picoseconds) simulation = Simulation(pdb.topology, system, integrator) simulation.context.setPositions(pdb.positions) simulation.minimizeEnergy() simulation.reporters.append(PDBReporter('output.pdb', 1000)) simulation.reporters.append(StateDataReporter(stdout, 1000, step=True, potentialEnergy=True, temperature=True)) simulation.step(10000) .. caption:: :autonumber:`Example,PDB example` You can find this script in the :file:`examples` folder of your OpenMM installation. It is called :file:`simulatePdb.py`. To execute it from a command line, go to your terminal/console/command prompt window (see Section :numref:`installing-openmm` on setting up the window to use OpenMM). Navigate to the :file:`examples` folder by typing :: cd where the typical directory is :file:`/usr/local/openmm/examples` on Linux and Mac machines and :file:`C:\\Program Files\\OpenMM\\examples` on Windows machines. Then type :: python simulatePdb.py You can name your own scripts whatever you want. Let’s go through the script line by line and see how it works. :: from openmm.app import * from openmm import * from openmm.unit import * from sys import stdout These lines are just telling the Python interpreter about some libraries we will be using. Don’t worry about exactly what they mean. Just include them at the start of your scripts. :: pdb = PDBFile('input.pdb') This line loads the PDB file from disk. (The :file:`input.pdb` file in the :file:`examples` directory contains the villin headpiece in explicit solvent.) More precisely, it creates a :class:`PDBFile` object, passes the file name :file:`input.pdb` to it as an argument, and assigns the object to a variable called :code:`pdb`\ . The :class:`PDBFile` object contains the information that was read from the file: the molecular topology and atom positions. Your file need not be called :file:`input.pdb`. Feel free to change this line to specify any file you want, though it must contain all of the atoms needed by the force field. (More information on how to add missing atoms and residues using OpenMM tools can be found in Chapter :numref:`model-building-and-editing`.) Make sure you include the single quotes around the file name. OpenMM also can load files in the newer PDBx/mmCIF format: just change :class:`PDBFile` to :class:`PDBxFile`. :: forcefield = ForceField('amber14-all.xml', 'amber14/tip3pfb.xml') This line specifies the force field to use for the simulation. Force fields are defined by XML files. OpenMM includes XML files defining lots of standard force fields (see Section :numref:`force-fields`). If you find you need to extend the repertoire of force fields available, you can find more information on how to create these XML files in Chapter :numref:`creating-force-fields`. In this case we load two of those files: :file:`amber14-all.xml`, which contains the Amber14 force field, and :file:`amber14/tip3pfb.xml`, which contains the TIP3P-FB water model. The :class:`ForceField` object is assigned to a variable called :code:`forcefield`\ . :: system = forcefield.createSystem(pdb.topology, nonbondedMethod=PME, nonbondedCutoff=1*nanometer, constraints=HBonds) This line combines the force field with the molecular topology loaded from the PDB file to create a complete mathematical description of the system we want to simulate. (More precisely, we invoke the :class:`ForceField` object’s :meth:`.createSystem` function. It creates a :class:`System` object, which we assign to the variable :code:`system`\ .) It specifies some additional options about how to do that: use particle mesh Ewald for the long range electrostatic interactions (:code:`nonbondedMethod=PME`\ ), use a 1 nm cutoff for the direct space interactions (\ :code:`nonbondedCutoff=1*nanometer`\ ), and constrain the length of all bonds that involve a hydrogen atom (\ :code:`constraints=HBonds`\ ). Note the way we specified the cutoff distance 1 nm using :code:`1*nanometer`: This is an example of the powerful units tracking and automatic conversion facility built into the OpenMM Python API that makes specifying unit-bearing quantities convenient and less error-prone. We could have equivalently specified :code:`10*angstrom` instead of :code:`1*nanometer` and achieved the same result. The units system will be described in more detail later, in Section :numref:`units-and-dimensional-analysis`. :: integrator = LangevinMiddleIntegrator(300*kelvin, 1/picosecond, 0.004*picoseconds) This line creates the integrator to use for advancing the equations of motion. It specifies a :class:`LangevinMiddleIntegrator`, which performs Langevin dynamics, and assigns it to a variable called :code:`integrator`\ . It also specifies the values of three parameters that are specific to Langevin dynamics: the simulation temperature (300 K), the friction coefficient (1 ps\ :sup:`-1`\ ), and the step size (0.004 ps). Lots of other integration methods are also available. For example, if you wanted to simulate the system at constant energy rather than constant temperature you would use a :code:`VerletIntegrator`\ . The available integration methods are listed in Section :numref:`integrators`. :: simulation = Simulation(pdb.topology, system, integrator) This line combines the molecular topology, system, and integrator to begin a new simulation. It creates a :class:`Simulation` object and assigns it to a variable called \ :code:`simulation`\ . A :class:`Simulation` object manages all the processes involved in running a simulation, such as advancing time and writing output. :: simulation.context.setPositions(pdb.positions) This line specifies the initial atom positions for the simulation: in this case, the positions that were loaded from the PDB file. :: simulation.minimizeEnergy() This line tells OpenMM to perform a local energy minimization. It is usually a good idea to do this at the start of a simulation, since the coordinates in the PDB file might produce very large forces. :: simulation.reporters.append(PDBReporter('output.pdb', 1000)) This line creates a “reporter” to generate output during the simulation, and adds it to the :class:`Simulation` object’s list of reporters. A :class:`PDBReporter` writes structures to a PDB file. We specify that the output file should be called :file:`output.pdb`, and that a structure should be written every 1000 time steps. :: simulation.reporters.append(StateDataReporter(stdout, 1000, step=True, potentialEnergy=True, temperature=True)) It can be useful to get regular status reports as a simulation runs so you can monitor its progress. This line adds another reporter to print out some basic information every 1000 time steps: the current step index, the potential energy of the system, and the temperature. We specify :code:`stdout` (not in quotes) as the output file, which means to write the results to the console. We also could have given a file name (in quotes), just as we did for the :class:`PDBReporter`, to write the information to a file. :: simulation.step(10000) Finally, we run the simulation, integrating the equations of motion for 10,000 time steps. Once it is finished, you can load the PDB file into any program you want for analysis and visualization (VMD_, PyMol_, AmberTools_, etc.). .. _VMD: http://www.ks.uiuc.edu/Research/vmd/ .. _PyMol: http://www.pymol.org .. _AmberTools: http://ambermd.org .. _using_amber_files: Using AMBER Files ***************** OpenMM can build a system in several different ways. One option, as shown above, is to start with a PDB file and then select a force field with which to model it. Alternatively, you can use AmberTools_ to model your system. In that case, you provide a :file:`prmtop` file and an :file:`inpcrd` file. OpenMM loads the files and creates a :class:`System` from them. This is illustrated in the following script. It can be found in OpenMM’s :file:`examples` folder with the name :file:`simulateAmber.py`. .. samepage:: :: from openmm.app import * from openmm import * from openmm.unit import * from sys import stdout inpcrd = AmberInpcrdFile('input.inpcrd') prmtop = AmberPrmtopFile('input.prmtop', periodicBoxVectors=inpcrd.boxVectors) system = prmtop.createSystem(nonbondedMethod=PME, nonbondedCutoff=1*nanometer, constraints=HBonds) integrator = LangevinMiddleIntegrator(300*kelvin, 1/picosecond, 0.004*picoseconds) simulation = Simulation(prmtop.topology, system, integrator) simulation.context.setPositions(inpcrd.positions) simulation.minimizeEnergy() simulation.reporters.append(PDBReporter('output.pdb', 1000)) simulation.reporters.append(StateDataReporter(stdout, 1000, step=True, potentialEnergy=True, temperature=True)) simulation.step(10000) .. caption:: :autonumber:`Example,AMBER example` This script is very similar to the previous one. There are just a few significant differences: :: inpcrd = AmberInpcrdFile('input.inpcrd') prmtop = AmberPrmtopFile('input.prmtop', periodicBoxVectors=inpcrd.boxVectors) In these lines, we load the :file:`inpcrd` file and :file:`prmtop` file. More precisely, we create :class:`AmberInpcrdFile` and :class:`AmberPrmtopFile` objects and assign them to the variables :code:`inpcrd` and :code:`prmtop`\ , respectively. As before, you can change these lines to specify any files you want. Be sure to include the single quotes around the file names. .. note:: The :class:`AmberPrmtopFile` reader provided by OpenMM only supports "new-style" :file:`prmtop` files introduced in AMBER 7. The AMBER distribution still contains a number of example files that are in the "old-style" :file:`prmtop` format. These "old-style" files will not run in OpenMM. Notice that when we load the :file:`prmtop` file we include the argument :code:`periodicBoxVectors=inpcrd.boxVectors`\ . AMBER stores information about the periodic box in the :file:`inpcrd` file. To let :class:`AmberPrmtopFile` create a :class:`Topology` object, we therefore need to tell it the periodic box vectors that were loaded from the :file:`inpcrd` file. You only need to do this if you are simulating a periodic system. For implicit solvent simulations, it usually can be omitted. .. note:: For historical reasons, :file:`prmtop` files also have the ability to store periodic box information, but it is deprecated. It is always better to get the box vectors from the :file:`inpcrd` file instead. Next, the :class:`System` object is created in a different way: :: system = prmtop.createSystem(nonbondedMethod=PME, nonbondedCutoff=1*nanometer, constraints=HBonds) In the previous section, we loaded the topology from a PDB file and then had the force field create a system based on it. In this case, we don’t need a force field; the :file:`prmtop` file already contains the force field parameters, so it can create the system directly. :: simulation = Simulation(prmtop.topology, system, integrator) simulation.context.setPositions(inpcrd.positions) Notice that we now get the topology from the :file:`prmtop` file and the atom positions from the :file:`inpcrd` file. In the previous section, both of these came from a PDB file, but AMBER puts the topology and positions in separate files. .. _using_gromacs_files: Using Gromacs Files ******************* A third option for creating your system is to use the Gromacs setup tools. They produce a :file:`gro` file containing the coordinates and a :file:`top` file containing the topology. OpenMM can load these exactly as it did the AMBER files. This is shown in the following script. It can be found in OpenMM’s :file:`examples` folder with the name :file:`simulateGromacs.py`. .. samepage:: :: from openmm.app import * from openmm import * from openmm.unit import * from sys import stdout gro = GromacsGroFile('input.gro') top = GromacsTopFile('input.top', periodicBoxVectors=gro.getPeriodicBoxVectors(), includeDir='/usr/local/gromacs/share/gromacs/top') system = top.createSystem(nonbondedMethod=PME, nonbondedCutoff=1*nanometer, constraints=HBonds) integrator = LangevinMiddleIntegrator(300*kelvin, 1/picosecond, 0.004*picoseconds) simulation = Simulation(top.topology, system, integrator) simulation.context.setPositions(gro.positions) simulation.minimizeEnergy() simulation.reporters.append(PDBReporter('output.pdb', 1000)) simulation.reporters.append(StateDataReporter(stdout, 1000, step=True, potentialEnergy=True, temperature=True)) simulation.step(10000) .. caption:: :autonumber:`Example,Gromacs example` This script is nearly identical to the previous one, just replacing :class:`AmberInpcrdFile` and :class:`AmberPrmtopFile` with :class:`GromacsGroFile` and :class:`GromacsTopFile`. As with AMBER files, when we create the :class:`GromacsTopFile` we specify :code:`periodicBoxVectors=gro.getPeriodicBoxVectors()` to tell it the periodic box vectors that were loaded from the :file:`gro` file. In addition, we specify :code:`includeDir='/usr/local/gromacs/share/gromacs/top'`\ . Unlike AMBER, which stores all the force field parameters directly in a :file:`prmtop` file, Gromacs just stores references to force field definition files that are installed with the Gromacs application. OpenMM needs to know where to find these files, so the :code:`includeDir` parameter specifies the directory containing them. If you omit this parameter, OpenMM will assume the default location :file:`/usr/local/gromacs/share/gromacs/top`, which is often where they are installed on Unix-like operating systems. So in :autonumref:`Example,Gromacs example` we actually could have omitted this parameter, but if the Gromacs files were installed in any other location, we would need to include it. .. _using-charmm-files: Using CHARMM Files ****************** Yet another option is to load files created by the CHARMM setup tools, or other compatible tools such as VMD. Those include a :file:`psf` file containing topology information, and an ordinary PDB file for the atomic coordinates. (Coordinates can also be loaded from CHARMM coordinate or restart files using the :class:`CharmmCrdFile` and :class:`CharmmRstFile` classes). In addition, you must provide a set of files containing the force field definition to use. This can involve several different files with varying formats and filename extensions such as :file:`par`, :file:`prm`, :file:`top`, :file:`rtf`, :file:`inp`, and :file:`str`. To do this, load all the definition files into a :class:`CharmmParameterSet` object, then include that object as the first parameter when you call :meth:`createSystem` on the :class:`CharmmPsfFile`. .. samepage:: :: from openmm.app import * from openmm import * from openmm.unit import * from sys import stdout, exit, stderr psf = CharmmPsfFile('input.psf') pdb = PDBFile('input.pdb') params = CharmmParameterSet('charmm22.rtf', 'charmm22.prm') system = psf.createSystem(params, nonbondedMethod=NoCutoff, nonbondedCutoff=1*nanometer, constraints=HBonds) integrator = LangevinMiddleIntegrator(300*kelvin, 1/picosecond, 0.004*picoseconds) simulation = Simulation(psf.topology, system, integrator) simulation.context.setPositions(pdb.positions) simulation.minimizeEnergy() simulation.reporters.append(PDBReporter('output.pdb', 1000)) simulation.reporters.append(StateDataReporter(stdout, 1000, step=True, potentialEnergy=True, temperature=True)) simulation.step(10000) .. caption:: :autonumber:`Example,CHARMM example` Note that both the CHARMM and XPLOR versions of the :file:`psf` file format are supported. .. _the-script-builder-application: The OpenMM-Setup Application **************************** One way to create your own scripts is to start with one of the examples given above and customize it to suit your needs, but there's an even easier option. OpenMM-Setup is a graphical application that walks you through the whole process of loading your input files and setting options. It then generates a complete script, and can even run it for you. .. figure:: ../../images/OpenMMSetup.png :align: center :width: 100% :autonumber:`Figure,openmm setup`: The OpenMM-Setup application To install OpenMM-Setup, open a command line terminal and type the following command :: conda install -c conda-forge openmm-setup You can then launch it by typing the command :: openmm-setup It will automatically open a window in your web browser displaying the user interface. OpenMM-Setup is far more than just a script generator. It can fix problems in your input files, add missing atoms, build membranes and water boxes, and much more. It is a very easy way to quickly do all necessary preparation and setup. We highly recommend it to all users of OpenMM, from novices to experts. .. _simulation-parameters: Simulation Parameters ********************* Now let’s consider lots of ways you might want to customize your script. Platforms ========= When creating a :class:`Simulation`, you can optionally tell it what :class:`Platform` to use. OpenMM includes four platforms: :class:`Reference`, :class:`CPU`, :class:`CUDA`, and :class:`OpenCL`. For a description of the differences between them, see Section :numref:`platforms`. There are three ways in which the :class:`Platform` can be chosen: 1. By default, OpenMM will try to select the fastest available :class:`Platform`. Usually its choice will be reasonable, but sometimes you may want to change it. 2. Alternatively, you can set the :envvar:`OPENMM_DEFAULT_PLATFORM` environment variable to the name of the :class:`Platform` to use. This overrides the default logic. 3. Finally, you can explicitly specify a :class:`Platform` object in your script when you create the :class:`Simulation`. The following lines specify to use the :class:`CUDA` platform: :: platform = Platform.getPlatformByName('CUDA') simulation = Simulation(prmtop.topology, system, integrator, platform) The platform name should be one of :code:`OpenCL`, :code:`CUDA`, :code:`CPU`, or :code:`Reference`. You also can specify platform-specific properties that customize how calculations should be done. See Chapter :numref:`platform-specific-properties` for details of the properties that each Platform supports. For example, the following lines specify to parallelize work across two different GPUs (CUDA devices 0 and 1), doing all computations in double precision: :: platform = Platform.getPlatformByName('CUDA') properties = {'DeviceIndex': '0,1', 'Precision': 'double'} simulation = Simulation(prmtop.topology, system, integrator, platform, properties) .. _force-fields: Force Fields ============ When you create a force field, you specify one or more XML files from which to load the force field definition. Most often, there will be one file to define the main force field, and possibly a second file to define the water model (either implicit or explicit). For example: :: forcefield = ForceField('amber14-all.xml', 'amber14/tip3pfb.xml') In some cases, one XML file may load several others. For example, :file:`amber14-all.xml` is really just a shortcut for loading several different files that together make up the AMBER14 force field. If you need finer grained control over which parameters are loaded, you can instead specify the component files individually. Be aware that some force fields and water models include "extra particles", such as lone pairs or Drude particles. Examples include the CHARMM polarizable force field and all of the 4 and 5 site water models. To use these force fields, you must first add the extra particles to the :class:`Topology`. See section :numref:`adding-or-removing-extra-particles` for details. The force fields described below are the ones that are bundled with OpenMM. Additional force fields are available online at https://github.com/choderalab/openmm-forcefields. Amber14 ------- The Amber14\ :cite:`Maier2015` force field is made up of various files that define parameters for proteins, DNA, RNA, lipids, water, and ions. .. tabularcolumns:: |l|L| =================================== ============================================ File Parameters =================================== ============================================ :file:`amber14/protein.ff14SB.xml` Protein (recommended) :file:`amber14/protein.ff15ipq.xml` Protein (alternative) :file:`amber14/DNA.OL15.xml` DNA (recommended) :file:`amber14/DNA.bsc1.xml` DNA (alternative) :file:`amber14/RNA.OL3.xml` RNA :file:`amber14/lipid17.xml` Lipid :file:`amber14/GLYCAM_06j-1.xml` Carbohydrates and glycosylated proteins\ :cite:`Kirschner2007` :file:`amber14/tip3p.xml` TIP3P water model\ :cite:`Jorgensen1983` and ions :file:`amber14/tip3pfb.xml` TIP3P-FB water model\ :cite:`Wang2014` and ions :file:`amber14/tip4pew.xml` TIP4P-Ew water model\ :cite:`Horn2004` and ions :file:`amber14/tip4pfb.xml` TIP4P-FB water model\ :cite:`Wang2014` and ions :file:`amber14/spce.xml` SPC/E water model\ :cite:`Berendsen1987` and ions :code:`amber14/opc.xml` OPC water model\ :cite:`Izadi2014` and ions :code:`amber14/opc3.xml` OPC3 water model\ :cite:`Izadi2016` and ions =================================== ============================================ As a convenience, the file :file:`amber14-all.xml` can be used as a shortcut to include :file:`amber14/protein.ff14SB.xml`, :file:`amber14/DNA.OL15.xml`, :file:`amber14/RNA.OL3.xml`, and :file:`amber14/lipid17.xml`. In most cases, you can simply include that file, plus one of the water models, such as :file:`amber14/tip3pfb.xml` for the TIP3P-FB water model and ions\ :cite:`Wang2014`: :: forcefield = ForceField('amber14-all.xml', 'amber14/tip3pfb.xml') GLYCAM is not included by default, since it is quite large. If your system contains carbohydrates, include that file as well: :: forcefield = ForceField('amber14-all.xml', 'amber14/tip3pfb.xml', 'amber14/GLYCAM_06j-1.xml') Be aware that GLYCAM works somewhat differently from most force fields. It uses its own nonstandard `naming convention `_ for carbohydrates, and requires your input file to follow it. If any residues have names different from what it expects, GLYCAM will be unable to assign parameters to them. .. tip:: The solvent model XML files included under the :file:`amber14/` directory include both water *and* ions compatible with that water model, so if you mistakenly specify :file:`tip3p.xml` instead of :file:`amber14/tip3p.xml`, you run the risk of having :class:`ForceField` throw an exception since :file:`tip3p.xml` will be missing parameters for ions in your system. The converted parameter sets come from the `AmberTools 17 release `_ and were converted using the `openmm-forcefields `_ package and `ParmEd `_. CHARMM36 -------- The CHARMM36\ :cite:`Best2012` force field provides parameters for proteins, DNA, RNA, lipids, carbohydrates, water, ions, and various small molecules (see `here `_ for full references). .. tabularcolumns:: |l|L| ================================= ============================================ File Parameters ================================= ============================================ :file:`charmm36.xml` Protein, DNA, RNA, lipids, carbohydrates, and small molecules :file:`charmm36/water.xml` Default CHARMM water model (a modified version of TIP3P\ :cite:`Jorgensen1983`) and ions :file:`charmm36/spce.xml` SPC/E water model\ :cite:`Berendsen1987` and ions :file:`charmm36/tip3p-pme-b.xml` TIP3P-PME-B water model\ :cite:`Price2004` and ions :file:`charmm36/tip3p-pme-f.xml` TIP3P-PME-F water model\ :cite:`Price2004` and ions :file:`charmm36/tip4pew.xml` TIP4P-Ew water model\ :cite:`Horn2004` and ions :file:`charmm36/tip4p2005.xml` TIP4P-2005 water model\ :cite:`Abascal2005` and ions :file:`charmm36/tip5p.xml` TIP5P water model\ :cite:`Mahoney2000` and ions :file:`charmm36/tip5pew.xml` TIP5P-Ew water model\ :cite:`Rick2004` and ions ================================= ============================================ The file :file:`charmm36.xml` bundles everything but the water and ions into a single file. In most cases, you can simply include that file, plus one of the water models, such as :file:`charmm36/water.xml`, which specifies the default CHARMM water model (a modified version of TIP3P\ :cite:`Jorgensen1983`) and ions: :: forcefield = ForceField('charmm36.xml', 'charmm36/water.xml') .. warning:: Drude polarizable sites and lone pairs are not yet supported by `ParmEd `_ and the CHARMM36 forcefields that depend on these features are not included in this port. To use the CHARMM 2019 polarizable force field\ :cite:`Lopes2013`, include the single file :file:`charmm_polar_2019.xml`. .. tip:: The solvent model XML files included under the :file:`charmm36/` directory include both water *and* ions compatible with that water model, so if you mistakenly specify :file:`tip3p.xml` instead of :file:`charmm36/water.xml`, you run the risk of having :class:`ForceField` raise an exception due to missing parameters for ions in your system. .. tip:: CHARMM makes extensive use of patches, which are automatically combined with residue templates to create an expanded library of patched residue templates by :class:`ForceField`. That means that patched residues, such as ``ACE`` and ``NME`` patched termini, must occur as a single residue in order for :class:`ForceField` to correctly match the residue template and apply parameters. Since these patched residues are not standard PDB residues, :class:`Modeller` does not know how to add hydrogens to these nonstandard residues, and your input topologies must already contain appropriate hydrogens. This can often cause problems when trying to read in PDB files from sources such as `CHARMM-GUI `_ that do not generate PDB files that comply with the `PDB standard `_. If you're using files from `CHARMM-GUI `_, it's easiest to load the PSF file directly, as discussed in Section :numref:`using-charmm-files`. .. tip:: Trying to read in PDB files from sources such as `CHARMM-GUI `_ that do not generate PDB files that comply with the `PDB standard `_ and omit ``CONECT`` records specifying bonds between residues (such as cysteines) or include ``CONECT`` records specifying non-chemical ``H-H`` bonds in waters can cause issues with the detection and parameter assignment for disulfide bonds. Make sure the files you read in comply with the appropriate standards regarding additional bonds and nonstandard residue definitions. If you're using files from `CHARMM-GUI `_, it's easiest to load the PSF file directly, as discussed in Section :numref:`using-charmm-files`. The converted parameter sets come from the `CHARMM36 July 2017 update `_ and were converted using the `openmm-forcefields `_ package and `parmed `_. Implicit Solvent ---------------- The Amber and CHARMM force fields described above can be used with any of the Generalized Born implicit solvent models from AMBER. To use them, include an extra file when creating the ForceField. For example, :: forcefield = ForceField('amber14-all.xml', 'implicit/gbn2.xml') .. tabularcolumns:: |l|L| ========================== ================================================================================================================================== File Implicit Solvent Model ========================== ================================================================================================================================== :file:`implicit/hct.xml` Hawkins-Cramer-Truhlar GBSA model\ :cite:`Hawkins1995` (corresponds to igb=1 in AMBER) :file:`implicit/obc1.xml` Onufriev-Bashford-Case GBSA model\ :cite:`Onufriev2004` using the GB\ :sup:`OBC`\ I parameters (corresponds to igb=2 in AMBER). :file:`implicit/obc2.xml` Onufriev-Bashford-Case GBSA model\ :cite:`Onufriev2004` using the GB\ :sup:`OBC`\ II parameters (corresponds to igb=5 in AMBER). :file:`implicit/gbn.xml` GBn solvation model\ :cite:`Mongan2007` (corresponds to igb=7 in AMBER). :file:`implicit/gbn2.xml` GBn2 solvation model\ :cite:`Nguyen2013` (corresponds to igb=8 in AMBER). ========================== ================================================================================================================================== The only nonbonded methods that are supported with implicit solvent are :code:`NoCutoff` (the default), :code:`CutoffNonPeriodic`, and :code:`CutoffPeriodic.` If you choose to use a nonbonded cutoff with implicit solvent, it is usually best to set the cutoff distance larger than is typical with explicit solvent. A cutoff of 2 nm gives good results in most cases. Periodic boundary conditions are not usually used with implicit solvent. In fact, the lack of need for periodicity and the artifacts it creates is one of the advantages of implicit solvent. The option is still offered, since it could be useful in some unusual situations. You can further control the solvation model in a few ways. First, you can specify the dielectric constants to use for the solute and solvent: :: system = forcefield.createSystem(topology, soluteDielectric=1.0, solventDielectric=80.0) If they are not specified, the solute and solvent dielectric constants default to 1.0 and 78.5, respectively. You also can model the effect of a non-zero salt concentration by specifying the Debye-Huckel screening parameter\ :cite:`Srinivasan1999`: :: system = forcefield.createSystem(topology, implicitSolventKappa=1.0/nanometer) The screening parameter can be calculated as .. math:: \kappa = 367.434915 \sqrt{\frac{I}{\epsilon T}} where :math:`I` is the ionic strength in moles/liter, :math:`\epsilon` is the solvent dielectric constant, and :math:`T` is the temperature in Kelvin. AMOEBA ------ The AMOEBA polarizable force field provides parameters for proteins, nucleic acids, water, and ions. .. tabularcolumns:: |l|L| ============================= ================================================================================ File Parameters ============================= ================================================================================ :file:`amoeba2018.xml` AMOEBA 2018\ :cite:`Shi2013` :file:`amoeba2018_gk.xml` Generalized Kirkwood solvation model\ :cite:`Schnieders2007` for use with AMOEBA 2018 force field :file:`amoeba2013.xml` AMOEBA 2013. This force field is deprecated. It is recommended to use AMOEBA 2018 instead. :file:`amoeba2013_gk.xml` Generalized Kirkwood solvation model for use with AMOEBA 2013 force field :file:`amoeba2009.xml` AMOEBA 2009\ :cite:`Ren2002`. This force field is deprecated. It is recommended to use AMOEBA 2018 instead. :file:`amoeba2009_gk.xml` Generalized Kirkwood solvation model for use with AMOEBA 2009 force field ============================= ================================================================================ For explicit solvent simulations, just include the single file :file:`amoeba2018.xml`. AMOEBA also supports implicit solvent using a Generalized Kirkwood model. To enable it, also include :file:`amoeba2018_gk.xml`. The older AMOEBA 2009 and 2013 force fields are provided only for backward compatibility, and are not recommended for most simulations. CHARMM Polarizable Force Field ------------------------------ To use the CHARMM 2019 polarizable force field\ :cite:`Lopes2013`, include the single file :file:`charmm_polar_2019.xml`. It includes parameters for proteins, lipids, water, and ions. When using this force field, remember to add extra particles to the :class:`Topology` as described in section :numref:`adding-or-removing-extra-particles`. This force field also requires that you use one of the special integrators that supports Drude particles. The options are DrudeLangevinIntegrator, DrudeNoseHooverIntegrator, and DrudeSCFIntegrator. Older Force Fields ------------------ OpenMM includes several older force fields as well. For most simulations, the newer force fields described above are preferred over any of these, but they are still useful for reproducing older results. .. tabularcolumns:: |l|L| ============================= ================================================================================ File Force Field ============================= ================================================================================ :code:`amber96.xml` Amber96\ :cite:`Kollman1997` :code:`amber99sb.xml` Amber99\ :cite:`Wang2000` with modified backbone torsions\ :cite:`Hornak2006` :code:`amber99sbildn.xml` Amber99SB plus improved side chain torsions\ :cite:`Lindorff-Larsen2010` :code:`amber99sbnmr.xml` Amber99SB with modifications to fit NMR data\ :cite:`Li2010` :code:`amber03.xml` Amber03\ :cite:`Duan2003` :code:`amber10.xml` Amber10 (documented in the AmberTools_ manual as `ff10`) :code:`charmm_polar_2013.xml` 2013 version of the CHARMM polarizable force field\ :cite:`Lopes2013` ============================= ================================================================================ Several of these force fields support implicit solvent. To enable it, also include the corresponding OBC file. .. tabularcolumns:: |l|L| ========================= ================================================================================================= File Implicit Solvation Model ========================= ================================================================================================= :code:`amber96_obc.xml` GBSA-OBC solvation model\ :cite:`Onufriev2004` for use with Amber96 force field :code:`amber99_obc.xml` GBSA-OBC solvation model for use with Amber99 force field and its variants :code:`amber03_obc.xml` GBSA-OBC solvation model for use with Amber03 force field :code:`amber10_obc.xml` GBSA-OBC solvation model for use with Amber10 force field ========================= ================================================================================================= Note that the GBSA-OBC parameters in these files are those used in TINKER.\ :cite:`Tinker` They are designed for use with Amber force fields, but they are different from the parameters found in the AMBER application. Water Models ------------ The following files define popular water models. They can be used with force fields that do not provide their own water models. When using Amber14 or CHARMM36, use the water files included with those force fields instead, since they also include ion parameters. .. tabularcolumns:: |l|L| =================== ============================================ File Water Model =================== ============================================ :code:`tip3p.xml` TIP3P water model\ :cite:`Jorgensen1983` :code:`tip3pfb.xml` TIP3P-FB water model\ :cite:`Wang2014` :code:`tip4pew.xml` TIP4P-Ew water model\ :cite:`Horn2004` :code:`tip4pfb.xml` TIP4P-FB water model\ :cite:`Wang2014` :code:`tip5p.xml` TIP5P water model\ :cite:`Mahoney2000` :code:`spce.xml` SPC/E water model\ :cite:`Berendsen1987` :code:`swm4ndp.xml` SWM4-NDP water model\ :cite:`Lamoureux2006` :code:`opc.xml` OPC water model\ :cite:`Izadi2014` :code:`opc3.xml` OPC3 water model\ :cite:`Izadi2016` =================== ============================================ .. _small-molecule-parameters: Small molecule parameters ========================= The OpenMM force fields above include pregenerated templates for biopolymers and solvents. If your system instead contain small molecules, it is often necessary to generate these parameters on the fly. There are two options for doing this within the OpenMM ``app`` ecosystem: Small molecule residue template generators ------------------------------------------ One approach is to use residue template generators for small molecules from the openmmforcefields_ conda package. You can install this via conda with: .. code-block:: bash $ conda install -c conda-forge openmmforcefields You can then add a small molecule residue template generator using the Open Force Field Initiative small molecule force fields using the following example: :: # Create an OpenFF Molecule object for benzene from SMILES from openff.toolkit.topology import Molecule molecule = Molecule.from_smiles('c1ccccc1') # Create the SMIRNOFF template generator with the default installed force field (openff-1.0.0) from openmmforcefields.generators import SMIRNOFFTemplateGenerator smirnoff = SMIRNOFFTemplateGenerator(molecules=molecule) # Create an OpenMM ForceField object with AMBER ff14SB and TIP3P with compatible ions from openmm.app import ForceField forcefield = ForceField('amber/protein.ff14SB.xml', 'amber/tip3p_standard.xml', 'amber/tip3p_HFE_multivalent.xml') # Register the SMIRNOFF template generator forcefield.registerTemplateGenerator(smirnoff.generator) Alternatively, you can use the older `AMBER GAFF small molecule force field `_: :: # Create an OpenFF Molecule object for benzene from SMILES from openff.toolkit.topology import Molecule molecule = Molecule.from_smiles('c1ccccc1') # Create the GAFF template generator from openmmforcefields.generators import GAFFTemplateGenerator gaff = GAFFTemplateGenerator(molecules=molecule) # Create an OpenMM ForceField object with AMBER ff14SB and TIP3P with compatible ions from openmm.app import ForceField forcefield = ForceField('amber/protein.ff14SB.xml', 'amber/tip3p_standard.xml', 'amber/tip3p_HFE_multivalent.xml') # Register the GAFF template generator forcefield.registerTemplateGenerator(gaff.generator) # You can now parameterize an OpenMM Topology object that contains the specified molecule. # forcefield will load the appropriate GAFF parameters when needed, and antechamber # will be used to generate small molecule parameters on the fly. from openmm.app import PDBFile pdbfile = PDBFile('t4-lysozyme-L99A-with-benzene.pdb') system = forcefield.createSystem(pdbfile.topology) More documentation can be found on the openmmforcefields_ page. Managing force fields with ``SystemGenerator`` ---------------------------------------------- As an alternative to explicitly registering template generators, the openmmforcefields_ package provides a ``SystemGenerator`` facility to simplify biopolymer and small molecule force field management. To use this, you can simply specify the small molecule force field you want to use: :: # Define the keyword arguments to feed to ForceField from openmm import unit, app forcefield_kwargs = { 'constraints' : app.HBonds, 'rigidWater' : True, 'removeCMMotion' : False, 'hydrogenMass' : 4*unit.amu } # Initialize a SystemGenerator using GAFF from openmmforcefields.generators import SystemGenerator system_generator = SystemGenerator(forcefields=['amber/ff14SB.xml', 'amber/tip3p_standard.xml'], small_molecule_forcefield='gaff-2.11', forcefield_kwargs=forcefield_kwargs, cache='db.json') # Create an OpenMM System from an OpenMM Topology object system = system_generator.create_system(openmm_topology) # Alternatively, create an OpenMM System from an OpenMM Topology object and a list of OpenFF Molecule objects molecules = Molecule.from_file('molecules.sdf', file_format='sdf') system = system_generator.create_system(openmm_topology, molecules=molecules) The ``SystemGenerator`` will match any instances of the molecules found in ``molecules.sdf`` to those that appear in ``topology``. Note that the protonation and tautomeric states must match exactly between the ``molecules`` read and those appearing in the Topology. See the openmmforcefields_ documentation for more details. .. _openmmforcefields: http://github.com/openmm/openmmforcefields AMBER Implicit Solvent ====================== When creating a system from a prmtop file you do not specify force field files, so you need a different way to tell it to use implicit solvent. This is done with the :code:`implicitSolvent` parameter: :: system = prmtop.createSystem(implicitSolvent=OBC2) OpenMM supports all of the Generalized Born models used by AMBER. Here are the allowed values for :code:`implicitSolvent`\ : .. tabularcolumns:: |l|L| ============= ================================================================================================================================== Value Meaning ============= ================================================================================================================================== :code:`None` No implicit solvent is used. :code:`HCT` Hawkins-Cramer-Truhlar GBSA model\ :cite:`Hawkins1995` (corresponds to igb=1 in AMBER) :code:`OBC1` Onufriev-Bashford-Case GBSA model\ :cite:`Onufriev2004` using the GB\ :sup:`OBC`\ I parameters (corresponds to igb=2 in AMBER). :code:`OBC2` Onufriev-Bashford-Case GBSA model\ :cite:`Onufriev2004` using the GB\ :sup:`OBC`\ II parameters (corresponds to igb=5 in AMBER). This is the same model used by the GBSA-OBC files described in Section :numref:`force-fields`. :code:`GBn` GBn solvation model\ :cite:`Mongan2007` (corresponds to igb=7 in AMBER). :code:`GBn2` GBn2 solvation model\ :cite:`Nguyen2013` (corresponds to igb=8 in AMBER). ============= ================================================================================================================================== You can further control the solvation model in a few ways. First, you can specify the dielectric constants to use for the solute and solvent: :: system = prmtop.createSystem(implicitSolvent=OBC2, soluteDielectric=1.0, solventDielectric=80.0) If they are not specified, the solute and solvent dielectrics default to 1.0 and 78.5, respectively. These values were chosen for consistency with AMBER, and are slightly different from those used elsewhere in OpenMM: when building a system from a force field, the solvent dielectric defaults to 78.3. You also can model the effect of a non-zero salt concentration by specifying the Debye-Huckel screening parameter\ :cite:`Srinivasan1999`: :: system = prmtop.createSystem(implicitSolvent=OBC2, implicitSolventKappa=1.0/nanometer) Nonbonded Interactions ====================== When creating the system (either from a force field or a prmtop file), you can specify options about how nonbonded interactions should be treated: :: system = prmtop.createSystem(nonbondedMethod=PME, nonbondedCutoff=1*nanometer) The :code:`nonbondedMethod` parameter can have any of the following values: .. tabularcolumns:: |l|L| ========================= =========================================================================================================================================================================================================================================== Value Meaning ========================= =========================================================================================================================================================================================================================================== :code:`NoCutoff` No cutoff is applied. :code:`CutoffNonPeriodic` The reaction field method is used to eliminate all interactions beyond a cutoff distance. Not valid for AMOEBA. :code:`CutoffPeriodic` The reaction field method is used to eliminate all interactions beyond a cutoff distance. Periodic boundary conditions are applied, so each atom interacts only with the nearest periodic copy of every other atom. Not valid for AMOEBA. :code:`Ewald` Periodic boundary conditions are applied. Ewald summation is used to compute long range Coulomb interactions. (This option is rarely used, since PME is much faster for all but the smallest systems.) Not valid for AMOEBA. :code:`PME` Periodic boundary conditions are applied. The Particle Mesh Ewald method is used to compute long range Coulomb interactions. :code:`LJPME` Periodic boundary conditions are applied. The Particle Mesh Ewald method is used to compute long range interactions for both Coulomb and Lennard-Jones. ========================= =========================================================================================================================================================================================================================================== When using any method other than :code:`NoCutoff`\ , you should also specify a cutoff distance. Be sure to specify units, as shown in the examples above. For example, :code:`nonbondedCutoff=1.5*nanometers` or :code:`nonbondedCutoff=12*angstroms` are legal values. When using :code:`Ewald`, :code:`PME`, or :code:`LJPME`\ , you can optionally specify an error tolerance for the force computation. For example: :: system = prmtop.createSystem(nonbondedMethod=PME, nonbondedCutoff=1*nanometer, ewaldErrorTolerance=0.00001) The error tolerance is roughly equal to the fractional error in the forces due to truncating the Ewald summation. If you do not specify it, a default value of 0.0005 is used. Another optional parameter when using a cutoff is :code:`switchDistance`. This causes Lennard-Jones interactions to smoothly go to zero over some finite range, rather than being sharply truncated at the cutoff distance. This can improve energy conservation. To use it, specify a distance at which the interactions should start being reduced. For example: :: system = prmtop.createSystem(nonbondedMethod=PME, nonbondedCutoff=1*nanometer, switchDistance=0.9*nanometer) Nonbonded Forces for AMOEBA --------------------------- For the AMOEBA force field, the valid values for the :code:`nonbondedMethod` are :code:`NoCutoff` and :code:`PME`\ . The other nonbonded methods, :code:`CutoffNonPeriodic`\ , :code:`CutoffPeriodic`\ , and :code:`Ewald` are unavailable for this force field. For implicit solvent runs using AMOEBA, only the :code:`nonbondedMethod` option :code:`NoCutoff` is available. Lennard-Jones Interaction Cutoff Value ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ In addition, for the AMOEBA force field a cutoff for the Lennard-Jones interaction independent of the value used for the electrostatic interactions may be specified using the keyword :code:`vdwCutoff`\ . :: system = forcefield.createSystem(nonbondedMethod=PME, nonbondedCutoff=1*nanometer, ewaldErrorTolerance=0.00001, vdwCutoff=1.2*nanometer) If :code:`vdwCutoff` is not specified, then the value of :code:`nonbondedCutoff` is used for the Lennard-Jones interactions. Specifying the Polarization Method ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ When using the AMOEBA force field, OpenMM allows the induced dipoles to be calculated in any of three different ways. The slowest but potentially most accurate method is to iterate the calculation until the dipoles converge to a specified tolerance. To select this, specify :code:`polarization='mutual'`. Use the :code:`mutualInducedTargetEpsilon` option to select the tolerance; for most situations, a value of 0.00001 works well. Alternatively you can specify :code:`polarization='extrapolated'`. This uses an analytic approximation :cite:`Simmonett2015` to estimate what the fully converged dipoles will be without actually continuing the calculation to convergence. In many cases this can be significantly faster with only a small loss in accuracy. Finally, you can specify :code:`polarization='direct'` to use the direct polarization approximation, in which induced dipoles depend only on the fixed multipoles, not on other induced dipoles. This is even faster, but it produces very different forces from mutual polarization, so it should only be used with force fields that have been specifically parameterized for use with this approximation. Here are examples of using each method: :: system = forcefield.createSystem(nonbondedMethod=PME, nonbondedCutoff=1*nanometer, vdwCutoff=1.2*nanometer, polarization='mutual', mutualInducedTargetEpsilon=0.00001) system = forcefield.createSystem(nonbondedMethod=PME, nonbondedCutoff=1*nanometer, vdwCutoff=1.2*nanometer, polarization='extrapolated') system = forcefield.createSystem(nonbondedMethod=PME, nonbondedCutoff=1*nanometer, vdwCutoff=1.2*nanometer, polarization='direct') Implicit Solvent and Solute Dielectrics ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ For implicit solvent simulations using the AMOEBA force field, the :file:`amoeba2013_gk.xml` file should be included in the initialization of the force field: :: forcefield = ForceField('amoeba2009.xml', 'amoeba2009_gk.xml') Only the :code:`nonbondedMethod` option :code:`NoCutoff` is available for implicit solvent runs using AMOEBA. In addition, the solvent and solute dielectric values can be specified for implicit solvent simulations: :: system=forcefield.createSystem(nonbondedMethod=NoCutoff, soluteDielectric=2.0, solventDielectric=80.0) The default values are 1.0 for the solute dielectric and 78.3 for the solvent dielectric. Constraints =========== When creating the system (either from a force field or an AMBER :file:`prmtop` file), you can optionally tell OpenMM to constrain certain bond lengths and angles. For example, :: system = prmtop.createSystem(nonbondedMethod=NoCutoff, constraints=HBonds) The :code:`constraints` parameter can have any of the following values: .. tabularcolumns:: |l|L| ================ ============================================================================================================================================= Value Meaning ================ ============================================================================================================================================= :code:`None` No constraints are applied. This is the default value. :code:`HBonds` The lengths of all bonds that involve a hydrogen atom are constrained. :code:`AllBonds` The lengths of all bonds are constrained. :code:`HAngles` The lengths of all bonds are constrained. In addition, all angles of the form H-X-H or H-O-X (where X is an arbitrary atom) are constrained. ================ ============================================================================================================================================= The main reason to use constraints is that it allows one to use a larger integration time step. With no constraints, one is typically limited to a time step of about 1 fs for typical biomolecular force fields like AMBER or CHARMM. With :code:`HBonds` constraints, this can be increased to about 2 fs for Verlet dynamics, or about 4 fs for Langevin dynamics. With :code:`HAngles`\ , it can sometimes be increased even further. Regardless of the value of this parameter, OpenMM makes water molecules completely rigid, constraining both their bond lengths and angles. You can disable this behavior with the :code:`rigidWater` parameter: :: system = prmtop.createSystem(nonbondedMethod=NoCutoff, constraints=None, rigidWater=False) Be aware that flexible water may require you to further reduce the integration step size, typically to about 0.5 fs. .. note:: The AMOEBA forcefield is designed to be used without constraints, so by default OpenMM makes AMOEBA water flexible. You can still force it to be rigid by specifying :code:`rigidWater=True`. Heavy Hydrogens =============== When creating the system (either from a force field or an AMBER :file:`prmtop` file), you can optionally tell OpenMM to increase the mass of hydrogen atoms. For example, :: system = prmtop.createSystem(hydrogenMass=4*amu) This applies only to hydrogens that are bonded to heavy atoms, and any mass added to the hydrogen is subtracted from the heavy atom. This keeps their total mass constant while slowing down the fast motions of hydrogens. When combined with constraints (typically :code:`constraints=AllBonds`\ ), this often allows a further increase in integration step size. .. _integrators: Integrators =========== OpenMM offers a choice of several different integration methods. You select which one to use by creating an integrator object of the appropriate type. Detailed descriptions of all these integrators can be found in Chapter :numref:`integrators-theory`. In addition to these built in integrators, lots of others are available as part of the `OpenMMTools `_ package. Langevin Middle Integrator -------------------------- In the examples of the previous sections, we used Langevin integration: :: integrator = LangevinMiddleIntegrator(300*kelvin, 1/picosecond, 0.004*picoseconds) The three parameter values in this line are the simulation temperature (300 K), the friction coefficient (1 ps\ :sup:`-1`\ ), and the step size (0.004 ps). You are free to change these to whatever values you want. Be sure to specify units on all values. For example, the step size could be written either as :code:`0.004*picoseconds` or :code:`4*femtoseconds`\ . They are exactly equivalent. Note that :code:`LangevinMiddleIntegrator` is a leapfrog integrator, so the velocities are offset by half a time step from the positions. Langevin Integrator ------------------- :code:`LangevinIntegrator` is very similar to :code:`LangevinMiddleIntegrator`, but it uses a different discretization of the Langevin equation. :code:`LangevinMiddleIntegrator` tends to produce more accurate configurational sampling, and therefore is preferred for most applications. Also note that :code:`LangevinIntegrator`\ , like :code:`LangevinMiddleIntegrator`\ , is a leapfrog integrator, so the velocities are offset by half a time step from the positions. Nosé-Hoover Integrator ---------------------- The :code:`NoseHooverIntegrator` uses the same "middle" leapfrog propagation algorithm as :code:`LangevinMiddleIntegrator`, but replaces the stochastic temperature control with a velocity scaling algorithm that produces more accurate transport properties :cite:`Basconi2013`. This velocity scaling results from propagating a chain of extra variables, which slightly reduces the computational efficiency with respect to :code:`LangevinMiddleIntegrator`. The thermostated integrator is minimally created with syntax analogous to the :code:`LangevinMiddleIntegrator` example above:: NoseHooverIntegrator integrator(300*kelvin, 1/picosecond, 0.004*picoseconds); The first argument specifies the target temperature. The second specifies the frequency of interaction with the heat bath: a lower value interacts minimally, yielding the microcanonical ensemble in the limit of a zero frequency, while a larger frequency will perturb the system greater, keeping it closer to the target temperature. The third argument is the integration timestep that, like the other arguments, must be specified with units. For initial equilibration to the target temperature, a larger interaction frequency is recommended, *e.g.* 25 ps\ :sup:`-1`. This integrator supports lots of other options, including the ability to couple different parts of the system to thermostats at different temperatures. See the API documentation for details. Leapfrog Verlet Integrator -------------------------- A leapfrog Verlet integrator can be used for running constant energy dynamics. The command for this is: :: integrator = VerletIntegrator(0.002*picoseconds) The only option is the step size. Brownian Integrator ------------------- Brownian (diffusive) dynamics can be used by specifying the following: :: integrator = BrownianIntegrator(300*kelvin, 1/picosecond, 0.002*picoseconds) The parameters are the same as for Langevin dynamics: temperature (300 K), friction coefficient (1 ps\ :sup:`-1`\ ), and step size (0.002 ps). Variable Time Step Langevin Integrator -------------------------------------- A variable time step Langevin integrator continuously adjusts its step size to keep the integration error below a specified tolerance. In some cases, this can allow you to use a larger average step size than would be possible with a fixed step size integrator. It also is very useful in cases where you do not know in advance what step size will be stable, such as when first equilibrating a system. You create this integrator with the following command: :: integrator = VariableLangevinIntegrator(300*kelvin, 1/picosecond, 0.001) In place of a step size, you specify an integration error tolerance (0.001 in this example). It is best not to think of this value as having any absolute meaning. Just think of it as an adjustable parameter that affects the step size and integration accuracy. Smaller values will produce a smaller average step size. You should try different values to find the largest one that produces a trajectory sufficiently accurate for your purposes. Variable Time Step Leapfrog Verlet Integrator --------------------------------------------- A variable time step leapfrog Verlet integrator works similarly to the variable time step Langevin integrator in that it continuously adjusts its step size to keep the integration error below a specified tolerance. The command for this integrator is: :: integrator = VariableVerletIntegrator(0.001) The parameter is the integration error tolerance (0.001), whose meaning is the same as for the Langevin integrator. Multiple Time Step Integrator ----------------------------- The :class:`MTSIntegrator` class implements the rRESPA multiple time step algorithm\ :cite:`Tuckerman1992`. This allows some forces in the system to be evaluated more frequently than others. For details on how to use it, consult the API documentation. Multiple Time Step Langevin Integrator -------------------------------------- :class:`MTSLangevinIntegrator` is similar to :class:`MTSIntegrator`, but it uses the Langevin method to perform constant temperature dynamics. For details on how to use it, consult the API documentation. Compound Integrator ------------------- The :class:`CompoundIntegrator` class is useful for cases where you want to use multiple integration algorithms within a single simulation. It allows you to create multiple integrators, then switch back and forth between them. For details on how to use it, consult the API documentation. Temperature Coupling ==================== If you want to run a simulation at constant temperature, using a Langevin integrator (as shown in the examples above) is usually the best way to do it. OpenMM does provide an alternative, however: you can use a Verlet integrator, then add an Andersen thermostat to your system to provide temperature coupling. To do this, we can add an :class:`AndersenThermostat` object to the :class:`System` as shown below. :: ... system = prmtop.createSystem(nonbondedMethod=PME, nonbondedCutoff=1*nanometer, constraints=HBonds) system.addForce(AndersenThermostat(300*kelvin, 1/picosecond)) integrator = VerletIntegrator(0.002*picoseconds) ... The two parameters of the Andersen thermostat are the temperature (300 K) and collision frequency (1 ps\ :sup:`-1`\ ). Pressure Coupling ================= All the examples so far have been constant volume simulations. If you want to run at constant pressure instead, add a Monte Carlo barostat to your system. You do this exactly the same way you added the Andersen thermostat in the previous section: :: ... system = prmtop.createSystem(nonbondedMethod=PME, nonbondedCutoff=1*nanometer, constraints=HBonds) system.addForce(MonteCarloBarostat(1*bar, 300*kelvin)) integrator = LangevinMiddleIntegrator(300*kelvin, 1/picosecond, 0.004*picoseconds) ... The parameters of the Monte Carlo barostat are the pressure (1 bar) and temperature (300 K). The barostat assumes the simulation is being run at constant temperature, but it does not itself do anything to regulate the temperature. .. warning:: It is therefore critical that you always use it along with a Langevin integrator or Andersen thermostat, and that you specify the same temperature for both the barostat and the integrator or thermostat. Otherwise, you will get incorrect results. There also is an anisotropic barostat that scales each axis of the periodic box independently, allowing it to change shape. When using the anisotropic barostat, you can specify a different pressure for each axis. The following line applies a pressure of 1 bar along the X and Y axes, but a pressure of 2 bar along the Z axis: :: system.addForce(MonteCarloAnisotropicBarostat((1, 1, 2)*bar, 300*kelvin)) Another feature of the anisotropic barostat is that it can be applied to only certain axes of the periodic box, keeping the size of the other axes fixed. This is done by passing three additional parameters that specify whether the barostat should be applied to each axis. The following line specifies that the X and Z axes of the periodic box should not be scaled, so only the Y axis can change size. :: system.addForce(MonteCarloAnisotropicBarostat((1, 1, 1)*bar, 300*kelvin, False, True, False)) There is a third barostat designed specifically for simulations of membranes. It assumes the membrane lies in the XY plane, and treats the X and Y axes of the box differently from the Z axis. It also applies a uniform surface tension in the plane of the membrane. The following line adds a membrane barostat that applies a pressure of 1 bar and a surface tension of 200 bar*nm. It specifies that the X and Y axes are treated isotropically while the Z axis is free to change independently. :: system.addForce(MonteCarloMembraneBarostat(1*bar, 200*bar*nanometer, MonteCarloMembraneBarostat.XYIsotropic, MonteCarloMembraneBarostat.ZFree, 300*kelvin)) See the API documentation for details about the allowed parameter values and their meanings. Energy Minimization =================== As seen in the examples, performing a local energy minimization takes a single line in the script: :: simulation.minimizeEnergy() In most cases, that is all you need. There are two optional parameters you can specify if you want further control over the minimization. First, you can specify a tolerance for when the energy should be considered to have converged: :: simulation.minimizeEnergy(tolerance=5*kilojoule/mole) If you do not specify this parameter, a default tolerance of 10 kJ/mole is used. Second, you can specify a maximum number of iterations: :: simulation.minimizeEnergy(maxIterations=100) The minimizer will exit once the specified number of iterations is reached, even if the energy has not yet converged. If you do not specify this parameter, the minimizer will continue until convergence is reached, no matter how many iterations it takes. These options are independent. You can specify both if you want: :: simulation.minimizeEnergy(tolerance=0.1*kilojoule/mole, maxIterations=500) Removing Center of Mass Motion ============================== By default, :class:`System` objects created with the OpenMM application tools add a :class:`CMMotionRemover` that removes all center of mass motion at every time step so the system as a whole does not drift with time. This is almost always what you want. In rare situations, you may want to allow the system to drift with time. You can do this by specifying the :code:`removeCMMotion` parameter when you create the System: :: system = forcefield.createSystem(pdb.topology, nonbondedMethod=NoCutoff, removeCMMotion=False) Writing Trajectories ==================== OpenMM can save simulation trajectories to disk in four formats: PDB_, `PDBx/mmCIF`_, DCD_ and XTC_. All of these are widely supported formats, so you should be able to read them into most analysis and visualization programs. .. _PDB: http://www.wwpdb.org/documentation/format33/v3.3.html .. _PDBx/mmCIF: http://mmcif.wwpdb.org .. _DCD: http://www.ks.uiuc.edu/Research/vmd/plugins/molfile/dcdplugin.html .. _XTC: https://manual.gromacs.org/archive/5.0.4/online/xtc.html To save a trajectory, just add a “reporter” to the simulation, as shown in the example scripts above: :: simulation.reporters.append(PDBReporter('output.pdb', 1000)) The two parameters of the :class:`PDBReporter` are the output filename and how often (in number of time steps) output structures should be written. To use PDBx/mmCIF, DCD or XTC format, just replace :class:`PDBReporter` with :class:`PDBxReporter`, :class:`DCDReporter` or :class:`XTCReporter`. The parameters represent the same values: :: simulation.reporters.append(DCDReporter('output.dcd', 1000)) Recording Other Data ==================== In addition to saving a trajectory, you may want to record other information over the course of a simulation, such as the potential energy or temperature. OpenMM provides a reporter for this purpose also. Create a :class:`StateDataReporter` and add it to the simulation: :: simulation.reporters.append(StateDataReporter('data.csv', 1000, time=True, kineticEnergy=True, potentialEnergy=True)) The first two parameters are the output filename and how often (in number of time steps) values should be written. The remaining arguments specify what values should be written at each report. The available options are :code:`step` (the index of the current time step), :code:`time`\ , :code:`progress` (what percentage of the simulation has completed), :code:`remainingTime` (an estimate of how long it will take the simulation to complete), :code:`potentialEnergy`\ , :code:`kineticEnergy`\ , :code:`totalEnergy`\ , :code:`temperature`\ , :code:`volume` (the volume of the periodic box), :code:`density` (the total system mass divided by the volume of the periodic box), and :code:`speed` (an estimate of how quickly the simulation is running). If you include either the :code:`progress` or :code:`remainingTime` option, you must also include the :code:`totalSteps` parameter to specify the total number of time steps that will be included in the simulation. One line is written to the file for each report containing the requested values. By default the values are written in comma-separated-value (CSV) format. You can use the :code:`separator` parameter to choose a different separator. For example, the following line will cause values to be separated by spaces instead of commas: :: simulation.reporters.append(StateDataReporter('data.txt', 1000, progress=True, temperature=True, totalSteps=10000, separator=' ')) Saving Simulation Progress and Results ========================================== There are three built-in ways to save the results of your simulation in OpenMM (additional methods can be written yourself or imported through other packages like mdtraj or parmed). If you are simply interested in saving the structure, you can write it out as a PDB file using :code:`PDBFile.writeFile()`. You can see an example of this in the modeller section :numref:`saving-the-results`. If you are hoping to save more information than just positions, you can use :code:`simulation.saveState()`. This will save the entire state of the simulation, including positions, velocities, box dimensions and much more in an XML file. This same file can be loaded back into OpenMM and used to continue the simulation. Importantly, because this file is a text file, it can be transfered between different platforms and different versions of OpenMM. A potential downside to this approach is that state files are often quite large, and may not fit all use cases. Here's an example of how to use it: :: simulation.saveState('output.xml') To load the simulation back in: :: simulation.loadState('output.xml') There is a third way to save your simulation, known as a checkpoint file, which will save the entire simulation as a binary file. It will allow you to exactly continue a simulation if the need arises (though whether the simulation is deterministic depends on platform and methods, see :numref:`platform-specific-properties-determinism`). There are important caveats to this approach, however. This binary can only be used to restart simulations on machines with the same hardware and the same OpenMM version as the one that saved it. Therefore, it should only be used when it's clear that won't be an issue. :: simulation.saveCheckpoint('state.chk') And can be loaded back in like this: :: simulation.loadCheckpoint('state.chk') Finally, OpenMM comes with a built-in reporter for saving checkpoints, the :class:`CheckpointReporter`, which can be helpful in restarting simulations that failed unexpectedly or due to outside reasons (e.g. server crash). To save a checkpoint file every 5,000 steps, for example: :: simulation.reporters.append(CheckpointReporter('checkpnt.chk', 5000)) Note that the checkpoint reporter will overwrite the last checkpoint file. Enhanced Sampling Methods ========================= In many situations, the goal of a simulation is to sample the range of configurations accessible to a system. It does not matter whether the simulation represents a single, physically realistic trajectory, only whether it produces a correct distribution of states. In this case, a variety of methods can be used to sample configuration space much more quickly and efficiently than a single physical trajectory would. These are known as enhanced sampling methods. OpenMM offers several that you can choose from. They are briefly described here. Consult the API documentation for more detailed descriptions and example code. Simulated Tempering ------------------- Simulated tempering\ :cite:`Marinari1992` involves making frequent changes to the temperature of a simulation. At high temperatures, it can quickly cross energy barriers and explore a wide range of configurations. At lower temperatures, it more thoroughly explores each local region of configuration space. This is a powerful method to speed up sampling when you do not know in advance what motions you want to sample. Simply specify the range of temperatures to simulate and the algorithm handles everything for you mostly automatically. Metadynamics ------------ Metadynamics\ :cite:`Barducci2008` is used when you do know in advance what motions you want to sample. You specify one or more collective variables, and the algorithm adds a biasing potential to make the simulation explore a wide range of values for those variables. It does this by periodically adding "bumps" to the biasing potential at the current values of the collective variables. This encourages the simulation to move away from regions it has already explored and sample a wide range of values. At the end of the simulation, the biasing potential can be used to calculate the free energy of the system as a function of the collective variables. Accelerated Molecular Dynamics (aMD) ------------------------------------ aMD\ :cite:`Hamelberg2007` is another method that can be used when you do not know in advance what motions you want to accelerate. It alters the potential energy surface by adding a "boost" potential whenever the potential energy is below a threshold. This makes local minima shallower and allows more frequent transitions between them. The boost can be applied to the total potential energy, to just a subset of interactions (typically the dihedral torsions), or both. There are separate integrator classes for each of these options: :class:`AMDIntegrator`, :class:`AMDForceGroupIntegrator`, and :class:`DualAMDIntegrator`.