Literature DB >> 29912282

gmxapi: a high-level interface for advanced control and extension of molecular dynamics simulations.

M Eric Irrgang1,2, Jennifer M Hays1,2, Peter M Kasson1,2.   

Abstract

Summary: Molecular dynamics simulations have found use in a wide variety of biomolecular applications, from protein folding kinetics to computational drug design to refinement of molecular structures. Two areas where users and developers frequently need to extend the built-in capabilities of most software packages are implementing custom interactions, for instance biases derived from experimental data, and running ensembles of simulations. We present a Python high-level interface for the popular simulation package GROMACS that i) allows custom potential functions without modifying the simulation package code, ii) maintains the optimized performance of GROMACS and iii) presents an abstract interface to building and executing computational graphs that allows transparent low-level optimization of data flow and task placement. Minimal dependencies make this integrated API for the GROMACS simulation engine simple, portable and maintainable. We demonstrate this API for experimentally-driven refinement of protein conformational ensembles. Availability and implementation: LGPLv2.1 source and instructions are available at https://github.com/kassonlab/gmxapi. Supplementary information: Supplementary data are available at Bioinformatics online.

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Year:  2018        PMID: 29912282      PMCID: PMC6223363          DOI: 10.1093/bioinformatics/bty484

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  4 in total

1.  Scalable molecular dynamics with NAMD.

Authors:  James C Phillips; Rosemary Braun; Wei Wang; James Gumbart; Emad Tajkhorshid; Elizabeth Villa; Christophe Chipot; Robert D Skeel; Laxmikant Kalé; Klaus Schulten
Journal:  J Comput Chem       Date:  2005-12       Impact factor: 3.376

2.  GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit.

Authors:  Sander Pronk; Szilárd Páll; Roland Schulz; Per Larsson; Pär Bjelkmar; Rossen Apostolov; Michael R Shirts; Jeremy C Smith; Peter M Kasson; David van der Spoel; Berk Hess; Erik Lindahl
Journal:  Bioinformatics       Date:  2013-02-13       Impact factor: 6.937

3.  OpenMM 4: A Reusable, Extensible, Hardware Independent Library for High Performance Molecular Simulation.

Authors:  Peter Eastman; Mark S Friedrichs; John D Chodera; Randall J Radmer; Christopher M Bruns; Joy P Ku; Kyle A Beauchamp; Thomas J Lane; Lee-Ping Wang; Diwakar Shukla; Tony Tye; Mike Houston; Timo Stich; Christoph Klein; Michael R Shirts; Vijay S Pande
Journal:  J Chem Theory Comput       Date:  2012-10-18       Impact factor: 6.006

4.  Restrained-ensemble molecular dynamics simulations based on distance histograms from double electron-electron resonance spectroscopy.

Authors:  Benoît Roux; Shahidul M Islam
Journal:  J Phys Chem B       Date:  2013-04-11       Impact factor: 2.991

  4 in total
  2 in total

1.  Hybrid Refinement of Heterogeneous Conformational Ensembles Using Spectroscopic Data.

Authors:  Jennifer M Hays; David S Cafiso; Peter M Kasson
Journal:  J Phys Chem Lett       Date:  2019-06-07       Impact factor: 6.475

2.  gmxapi: A GROMACS-native Python interface for molecular dynamics with ensemble and plugin support.

Authors:  M Eric Irrgang; Caroline Davis; Peter M Kasson
Journal:  PLoS Comput Biol       Date:  2022-02-14       Impact factor: 4.475

  2 in total

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