Skip to content

Latest commit

 

History

History
89 lines (66 loc) · 4.4 KB

README.md

File metadata and controls

89 lines (66 loc) · 4.4 KB

OpenMM - Gaussian Accelerated Molecular Dynamics (GaMD) module

Gaussian Accelerated Molecular Dynamics (GaMD) is a biomolecular enhanced sampling method that works by adding a harmonic boost potential to smoothen the system potential energy surface. By constructing a boost potential that follows Gaussian distribution, accurate reweighting of the GaMD simulations is achieved using cumulant expansion to the second order. GaMD has been demonstrated on three biomolecular model systems: alanine dipeptide, a set of three RNA tetraloops, and the ligand rbt203 binding to HIV-1 Tar RNA. Without the need to set predefined reaction coordinates, GaMD enables unconstrained enhanced sampling of these biomolecules. Furthermore, the free energy profiles obtained from reweighting of the GaMD simulations allow us to identify distinct low energy states of the biomolecules and characterize the protein folding and ligand binding pathways quantitatively.

Installation

  1. You will need to start by installing Anaconda Python 3.x.
  2. Next, install OpenMM using the instructions found in the OpenMM User Guide - Section 2.2 Installing OpenMM.
  3. You'll need the AmberTools for doing the post MD analysis. You can get these tools by executing the following command:
    conda install -c conda-forge ambertools
    
  4. You'll need the PyReweighting scripts, which can be cloned from the PyReweighting Git Repository. (NOTE: If you are doing development on the GaMD module itself and want to use the test scripts, the PyReweighting project directory should be added to your path, so that the scripts can find it.)
  5. Clone and Install this package:
    git clone https://github.com/MiaoLab20/gamd-openmm.git
    cd gamd-openmm
    setup.py install
    
  6. The command gamdRunner can either be copied into your user bin directory or you can updated your PATH variable to include the location fo the gamd-openmm directory, if you would like to use the gamdRunner for running your simulations.

Testing (Optional)

You may also optionally run tests: setup.py test

Run

You can run gamd by providing your own configuration file to the gamdRunner program like the example here.

gamdRunner xml configuration-file.xml

We've created the repository gamd-openmm-examples which contains examples (data files and configuration files) and instructions you can use to validate your gamd installation. This project can also help you learn how to use the available command line options to the gamdRunner and about some of the options for the configuration file.

NOTE: The gamdRunner and the gamd-openmm code currently only supports running the conventional md, equilibration, and production stages as a part of a single execution.

Important Options and Hints

  • The gamdRunner program can be run with the '-h' argument to see all available options. Please see link to RTD here for a detailed description of programs and options.

Status

We have implemented the upper and lower bound versions of the following types of gamd boosts:

  • dihedral
  • total
  • dual total/dihedral
  • non-bonded
  • dual non-bonded/dihedral

Questions

Please direct questions on usage to the gamd-discuss mailing list. Please make sure in your question to mention that you are using the OpenMM version of GaMD, since questions for the Amber and NAMD versions also go here. Please leave GitHub issues for code/documentation problems or feature requests.

Authors and Contributors

The following people have contributed directly to the coding and validation efforts of GaMD-OpenMM (listed an alphabetical order of last name). Thanks also to everyone who has helped or will help improve this project by providing feedback, bug reports, or other comments.

  • Matthew Copeland
  • Hung Do
  • Keya Joshi
  • Yinglong Miao
  • Lane Votapka
  • Jinan Wang

Citing GaMD-OpenMM

If you use GaMD-OpenMM, please cite the following paper:

  • Copeland, M.M., Do, HN, Votapka, L., Joshi, K., Wang, J., Amaro, R., and Miao, Y.* (2022) Gaussian accelerated molecular dynamics in OpenMM. Journal of Physical Chemistry B, 126(31): 5810–5820.