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


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.

SUBMITTER: Irrgang ME 

PROVIDER: S-EPMC6223363 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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

Irrgang M Eric ME   Hays Jennifer M JM   Kasson Peter M PM  

Bioinformatics (Oxford, England) 20181101 22


<h4>Summary</h4>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  ...[more]

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