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MDCC_tools: characterizing multi-modal atomic motions in molecular dynamics trajectories.

ABSTRACT: We previously reported the multi-modal Dynamic Cross Correlation (mDCC) method for analyzing molecular dynamics trajectories. This method quantifies the correlation coefficients of atomic motions with complex multi-modal behaviors by using a Bayesian-based pattern recognition technique that can effectively capture transiently formed, unstable interactions. Here, we present an open source toolkit for performing the mDCC analysis, including pattern recognitions, complex network analyses and visualizations. We include a tutorial document that thoroughly explains how to apply this toolkit for an analysis, using the example trajectory of the 100?ns simulation of an engineered endothelin-1 peptide dimer.The source code is available for free at http://www.protein.osaka-u.ac.jp/rcsfp/pi/mdcctools/, implemented in C?++ and Python, and supported on Linux.kota.kasahara@protein.osaka-u.ac.jpSupplementary data are available at Bioinformatics online.

SUBMITTER: Kasahara K

PROVIDER: S-EPMC4978922 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

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mDCC_tools: characterizing multi-modal atomic motions in molecular dynamics trajectories.

Kasahara Kota K Mohan Neetha N Fukuda Ikuo I Nakamura Haruki H

Bioinformatics (Oxford, England) 20160407 16

<h4>Unlabelled</h4>We previously reported the multi-modal Dynamic Cross Correlation (mDCC) method for analyzing molecular dynamics trajectories. This method quantifies the correlation coefficients of atomic motions with complex multi-modal behaviors by using a Bayesian-based pattern recognition technique that can effectively capture transiently formed, unstable interactions. Here, we present an open source toolkit for performing the mDCC analysis, including pattern recognitions, complex network ...[more]

PMID: 27153575

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