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.
Availability and implementation: 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.
Supplementary information: Supplementary data are available at Bioinformatics online.
© The Author 2016. Published by Oxford University Press.