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README.md

README.md

Sample MD restraint plugin

image

This repository provides a complete and working implementation of a few GROMACS restraint potentials. It is intended as both a tutorial and as a template for implementing new custom restraint potentials.

Restraint potentials are implemented as "plugins" to GROMACS. The required GROMACS modifications are available at this GitHub repository

The plugin potentials are loaded and configured via Python and are compatible with the gmxapi Python package for MD simulation workflows.

For a quick start, consider pulling a recent Docker image that has already been configured for gmxapi and this plug-in.

Reference:

Irrgang, M. E., Hays, J. M., & Kasson, P. M. gmxapi: a high-level interface for advanced control and extension of molecular dynamics simulations. Bioinformatics 2018. DOI: 10.1093/bioinformatics/bty484

Repository Contents

This repository uses CMake to build and install a Python C++ extension package.

  • CMakeLists.txt, cmake/FindGROMACS.cmake, and src/CMakeLists.txt provide necessary CMake infrastructure. You should not need to edit these.
  • src/cpp contains a header and cpp file for each restraint potential built with this module. When adding new potentials, you will update CMakeLists.txt to create build targets. Use the existing potentials as examples.
  • src/pythonmodule/ contains CMakeLists.txt, export_plugin.h, and export_plugin.cpp. When you have written a new potential, you can add it to CMakeLists.txt and export_plugin.cpp. This is the code that produces the C++ extension for Python. HarmonicRestraint is a simple example that applies a Hooke's Law spring between two atoms. EnsembleHarmonic applies a more complicated potential and uses additional facilities provided by gmxapi.
  • src/pybind11 is just a copy of the Python bindings framework from the Pybind project (ref https://github.com/pybind/pybind11 ). It is used to wrap the C++ restraint code and give it a Python interface.
  • tests/ contains C++ and Python tests for the provided code. Update CMakeLists.txt to add your own, based on these examples. C++ unit tests use googletest. Python tests use the pytest. Refer to those respective projects for more about how they make test-writing easier.
  • examples contains a sample SLURM job script and restrained-ensemble.py gmxapi script that have been used to do restrained ensemble simulations. example.py and example.ipynb explore a toy alanine dipeptide system. strip_notebook.py is a helper script to remove extra output and state data from an iPython notebook before checking updates back into the repository.
  • Dockerfile is a recipe to build a Docker image from the root of the repository.

Docker quick-start

Pull the docker image and launch a container with port 8888 on the host mapped to port 8888 in the container. :

docker run --rm -ti -p 8888:8888 gmxapi/sample_restraint:devel

Note that the --rm option tells docker not to save any changes you make after launching the container. You can, however, download any changes you make to the notebook through the web interface. Refer to the Docker documentation for more options on managing containers.

You should then see something like the following, but with a different token for the URL. Open the URL in a browser on the same (host) machine to access the notebook server. Browse to sample_restraint and examples and then launch the example notebook for an interactive walk-through. Example output:

Execute the command: jupyter notebook
[I 15:26:07.683 NotebookApp] Writing notebook server cookie secret to /home/jovyan/.local/share/jupyter/runtime/notebook_cookie_secret
[W 15:26:08.184 NotebookApp] WARNING: The notebook server is listening on all IP addresses and not using encryption. This is not recommended.
[I 15:26:08.223 NotebookApp] JupyterLab alpha preview extension loaded from /opt/conda/lib/python3.6/site-packages/jupyterlab
[I 15:26:08.230 NotebookApp] Serving notebooks from local directory: /home/jovyan
[I 15:26:08.230 NotebookApp] 0 active kernels
[I 15:26:08.230 NotebookApp] The Jupyter Notebook is running at:
[I 15:26:08.230 NotebookApp] http://[all ip addresses on your system]:8888/?token=948d611453ea3f03ad406dc375bfc186c4315fa68c50e23d
[I 15:26:08.230 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
[C 15:26:08.231 NotebookApp]

    Copy/paste this URL into your browser when you connect for the first time,
    to login with a token:
        http://localhost:8888/?token=948d611453ea3f03ad406dc375bfc186c4315fa68c50e23d

The basics

This repository provides a potentially useful plugin, but also serves as documentation by example and as a template for developing GROMACS extension code in the gmxapi framework.

Build and install

To download, build, and install, you may need to first install wget, git, and/or cmake.

The plugin requires libgmxapi to build. See gromacs-gmxapi :

# install GROMACS. Instead of `master`, you can choose a specific release or the `devel` branch.
wget https://github.com/kassonlab/gromacs-gmxapi/archive/master.zip
unzip master.zip
cd gromacs-gmxapi-master
mkdir build
cd mkdir build
cmake .. -DCMAKE_INSTALL_PREFIX=$HOME/gromacs -DGMX_THREAD_MPI=ON
make install # use -j10 to build in parallel with 10 cores (or however many you have)
cd ../..

We use CMake to configure and build a C++ library and a Python module for interacting with it. After installing the modified GROMACS (see above), either source the GMXRC file provided with the GROMACS installation or provide the install location to CMake with the gmxapi_DIR environment variable.

As with gmxapi, we recommend installing and using this code in a Python virtual environment. (See the documentation for your gmxapi distribution or http://gmxapi.readthedocs.io/en/latest/install.html ) Accordingly, if you choose to install the plugin rather than just to use it out of its build directory, consider whether you want to have to set your PYTHONPATH environment variable or where you can install it that Python will find it. You can explicitly set the installation location by setting -DGMXPLUGIN_INSTALL_PATH=/path/to/install/directory or you can let CMake determine an appropriate location automatically for your Python interpreter. If you have administrative privileges (such as when running on a desktop computer) or if you are using a Python virtual environment (recommended), you don't need to specify anything additional. If you are an unprivileged user (such as on a shared machine) and are not in a Python virtual environment, set -DGMXPLUGIN_USER_INSTALL=ON to install into the "user" Python packages directory in your home directory. (Equivalent to the --user option to pip)

If you have multiple Python installations or just want to be unambiguous, provide CMake with the Python interpreter you wish to use (the same as you are using for gmxapi) with -DPYTHON_EXECUTABLE=/path/to/python. For instance, if you have both Python 3.x and Python 2.7, but you plan to use Python 2.7, use -DPYTHON_EXECUTABLE=`which python2` or -DPYTHON_EXECUTABLE=`which python` (if python points to the Python 2 interpreter). :

# build sample restraint
git clone https://github.com/kassonlab/sample_restraint.git
# optionally, check out the development branch
# pushd sample_restraint ; git checkout devel ; popd
# perform an out-of-source build
mkdir build
cd build
# Get the GROMACS environment settings
source $HOME/gromacs/bin/GMXRC
# Configure the build environment with CMake
cmake ../sample_restraint
# or
# cmake ../sample_restraint -DGMXPLUGIN_INSTALL_PATH=/path/to/install/directory
# or
# cmake ../sample_restraint -DGMXPLUGIN_USER_INSTALL=ON -DPYTHON_EXECUTABLE=`which python`
make
# run C++ tests
make test
# optionally, install
make install

If you choose not to install the plugin module, you can tell Python where to find it by setting your PYTHONPATH environment variable. For instance, while still in the build directory:

export PYTHONPATH=`pwd`/src/pythonmodule

The Python module gmx is required for testing. See gmxapi

Running

The examples directory contains some sample scripts for running gmxapi workflows using the restraint potential samples in this repository. You may also find tests/test_binding.py informative.

For a basic walk-through with a toy system, launch a Jupyter notebook server and navigate to examples/example.py

What's going on

This sample project builds several C++ libraries with names such as harmonicpotential. The actual filename will be something like libharmonicpotential.so or harmonicpotential.dll or something depending on your operating system. These libraries are used to build a Python module named myplugin.

When setting up a workflow, a Python script provides gmxapi with parameters and a factory function for a plugin restraint potential. This Python interface is defined in src/pythonmodule/export_plugin.cpp. When a Session is launched, an C++ object that performs restraint force calculations is created and given to the GROMACS library. During each MD step, part of the MD force evaluation includes a call to the calculations performed by the restraint. For the pair restraints demonstrated here, GROMACS provides relative coordinates of two atomic sites to the calculation code in the plugin. If multiple restrained pairs are needed, multiple restraints are attached to the simulation. Coordination across an ensemble of simulations is possible using resources provided by the Session.

Fundamentally, a new restraint potential is implemented by creating a class that provides a calculate() method and using wrappers to give it interfaces to GROMACS and to Python. C++ wrappers allow the basic class implementing the potential to be presented to the GROMACS library in a way that can be used to evaluate forces during a simulation. Other C++ template code wraps the potential in a portable way so that it can be passed to GROMACS through a Python interface and to receive parameters from the Python interpreter. Pybind11 syntax in export_plugin.cpp provides the code to actually expose the plugin as a class in a Python module that is compatible with the gmx package provided in the gmxapi project.

By version 0.1.0, additional wrappers and boilerplate code will be migrated out of the files that define the calculate() methods. Until then, some amount of copy-and-paste or editing is necessary to implement a new potential. Refer to src/cpp/harmonicpotential.h and to src/cpp/harmonicpotential.cpp for a documented example of a simple pair restraint. A more complex example is found in the ensemblepotential files. The code in src/cpp is sufficient to produce testable object code, but the Python module is exported in src/pythonmodule/export_plugin.cpp. If you add additional source files for a new potential, you will need to update src/cpp/CMakeLists.txt as well.

Python tests

For the Python-level testing, you will need pytest and gmxapi. We recommend setting up a Python virtual environment as described at https://github.com/kassonlab/gmxapi

You will also need a functioning MPI installation and the mpi4py package.

Python tests can be run from the root directory of the repository after building. Assuming you built in a subdirecory of the repository named build (as above):

PYTHONPATH=build/src/pythonmodule/ python -m pytest tests

This command causes the directory named tests to be explored for Python files with names like test_*.py or *_test.py. Matching files will be imported and any functions with similarly obvious names will be run and errors reported. In particular, assert statements will be evaluated to perform individual tests. See also https://docs.pytest.org/en/latest/goodpractices.html#test-discovery

The tests assume that the package is already installed or is available on the default Python path (such as by setting the PYTHONPATH environment variable). If you just run pytest with no arguments, it will discover and try to run tests from elsewhere in the repository that were not intended, and they will fail.

To run the full set of tests for the ensemble workflow features, first make sure that you have an MPI-capable environment and mpi4py installed. Refer to http://mpi4py.readthedocs.io/en/stable/ and https://github.com/kassonlab/gmxapi for more information.

The ensemble tests assume that 2 ranks are available. After installing the plugin, run (for example):

mpiexec -n 2 python -m mpi4py -m pytest

If you do not have MPI set up for your system, you could build a docker image using the Dockerfile in this repository.

docker build -t samplerestraint . Dockerfile
docker run --cpus 2 --rm -ti samplerestraint bash -c \
    "cd /home/jovyan/sample_restraint/tests && 
    mpiexec -n 2 python -m mpi4py -m pytest"

To test with a pre-built image from our docker hub repository, do

docker run --cpus 2 --rm -ti gmxapi/sample_restraint bash -c \
        "cd /home/jovyan/sample_restraint/tests && 
        mpiexec -n 2 python -m mpi4py -m pytest"
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