Introduction

Author:Kota Kasahara

What is mDCC?

The multi-modal dynamic cross correlation (mDCC) is a method for analyzing trajectories generated by molecular dynamics (MD) simulations. The mDCC was developed by KASAHARA Kota, FUKUDA Ikuo, and NAKAMURA Haruki, at Institute for Protein Reasearch, Osaka University. See the original manuscript for details: A Novel Approach of Dynamic Cross Correlation Analysis on Molecular Dynamics Simulations and Its Application to Ets1 Dimer-DNA Complex. 2014 PLoS ONE 9:e112419 [Kasahara_2014].

[Kasahara_2014]Kasahara, K., Fukuda, I., & Nakamura, H. (2014). A Novel Approach of Dynamic Cross Correlation Analysis on Molecular Dynamics Simulations and Its Application to Ets1 Dimer-DNA Complex. PLoS ONE, 9(11), e112419. http://doi.org/10.1371/journal.pone.0112419

mDCC tools

This tool kit includes ...

  • Programs
    • mdcc_learn
      • Detection of modes of atomic motions
    • mdcc_assign
      • Calculation of probability density functions for each atom at each time step
    • python scripts
  • This document
  • Sample files for the tutorial
    • A trajectory file written in the Gromacs .trr format
    • .bash files to execute analysis programs
    • Configuration files as input of the programs

Installation

The path to the home directory of the mDCC tools should be set as the shell variable ${MDCCTOOLS}. For example,:

export MDCCTOOLS=${HOME}/local/mdcctools

This tool kit includes two C++ programs (mdcc_learn and mdcc_assign) and some python scripts. The C++ programs need to be compiled.

The additional information of installation of dependencies are described in the Appendix.

mdcc_learn

mdcc_learn program performs a pattern recognition on a spatial distribution of atomic coordinates in a trajectory.

The source codes of mdcc_learn are placed in ${MDCCTOOLS}/src/mdcc_learn directory.

mdcc_learn requires LAPACK library. The name of LAPACK library and path to the library file should be specified in the Makefile.

PATH_LAPACKLIB = ${HOME}/lib

LAPACKLIB = -llapack

To build mdcc_learn, execute the make command and move the generated binary to ${MDCCTOOLS}/bin directory:

make
mv mdcc_learn ../../bin

mdcc_assign

mdcc_assign program calculates the probability density for each data point of atomic coordinates in a trajectory on the basis of the results of mdcc_learn program.

This program requires LAPACK and BOOST libraries. The name and path of LAPACK library file and the path of BOOST include files should be specified in the Makefile:

PATH_LAPACKLIB = ${HOME}/lib
LAPACKLIB = -llapack
BOOSTINC = $(HOME)/include

To build mdcc_assign, execute the make command and move the generated binary to ${MDCCTOOLS}/bin directory:

make
mv mdcc_assign ../../bin

Python scripts

Many python scripts are located in ${MDCCTOOLS}/bin directory. They are written for python2.7 and requires the libraries:

  • numpy
  • scipy
  • mdanalysis
  • networkx

They should be installed in paths in ${PYTHONPATH} environment variable.

All these libraries can be obtained by using easy_install command.

Other programs for tutorial

mDCC tools output the results as tab-separated text or binary files. In order to visualize the data, some analyses tools are useful. In the tutorial, SQLite3, R, and Cytoscape are used. However, users can apply any other software, such as gnuplot and graphvis.

For R software, the three libraries are used.

  • reshape
  • ggplot
  • plyr

They can be installed with install.packages() command in the R shell.

License

mDCC_tools is distributed under GPL ver.3 liscense.