RIP-MD: a tool to study residue interaction networks in protein molecular dynamics

Author:

Contreras-Riquelme Sebastián123,Garate Jose-Antonio4,Perez-Acle Tomas14ORCID,Martin Alberto J.M.3ORCID

Affiliation:

1. Computational Biology Laboratory (DLab), Fundacion Ciencia & Vida, Santiago, Chile

2. Facultad de Ciencias de la Vida, Universidad Andrés Bello, Santiago, Chile

3. Network Biology Laboratory, Centro de Genómica y Bioinformática, Facultad de Ciencias, Universidad Mayor, Santiago, Chile

4. Centro Interdisciplinario de Neurociencia de Valparaíso, Valparaíso, Chile

Abstract

Protein structure is not static; residues undergo conformational rearrangements and, in doing so, create, stabilize or break non-covalent interactions. Molecular dynamics (MD) is a technique used to simulate these movements with atomic resolution. However, given the data-intensive nature of the technique, gathering relevant information from MD simulations is a complex and time consuming process requiring several computational tools to perform these analyses. Among different approaches, the study of residue interaction networks (RINs) has proven to facilitate the study of protein structures. In a RIN, nodes represent amino-acid residues and the connections between them depict non-covalent interactions. Here, we describe residue interaction networks in protein molecular dynamics (RIP-MD), a visual molecular dynamics (VMD) plugin to facilitate the study of RINs using trajectories obtained from MD simulations of proteins. Our software generates RINs from MD trajectory files. The non-covalent interactions defined by RIP-MD include H-bonds, salt bridges, VdWs, cation-π, π–π, Arginine–Arginine, and Coulomb interactions. In addition, RIP-MD also computes interactions based on distances between Cαs and disulfide bridges. The results of the analysis are shown in an user friendly interface. Moreover, the user can take advantage of the VMD visualization capacities, whereby through some effortless steps, it is possible to select and visualize interactions described for a single, several or all residues in a MD trajectory. Network and descriptive table files are also generated, allowing their further study in other specialized platforms. Our method was written in python in a parallelized fashion. This characteristic allows the analysis of large systems impossible to handle otherwise. RIP-MD is available at http://www.dlab.cl/ripmd.

Funder

Programa de Apoyo a Centros con Financiamiento Basal AFB 17004 to Fundación Ciencia Vida

ICM-Economia project to Instituto Milenio Centro Interdisciplinario de Neurociencias de Valparaiso (CINV)

FONDECYT projects

US Air Force Office of Scientific Research

Beca de Asistencia Academica from Universidad Nacional Andres Bello to Sebastian Contreras-Riquelme

Chilean National Laboratory for High-Performance Computing (NLHPC)

Publisher

PeerJ

Subject

General Agricultural and Biological Sciences,General Biochemistry, Genetics and Molecular Biology,General Medicine,General Neuroscience

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