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MSMBuilder

eagle-i ID

http://shared.eagle-i.net/i/0000013a-4ad5-6f1a-5291-c33f80000000

Resource Type

  1. Software

Properties

  1. Resource Description
    MSMBuilder is an open source software package for automating the construction and analysis of Markov state models (MSMs). It is primarily written in the python programming language with C extensions for the most time consuming routines. MSMs are a powerful means of modeling the structure and dynamics of molecular systems, like proteins. An MSM is essentially a map of the conformational space a molecule explores. Such models consist of a set of states and a matrix of transition probabilities (or, equivalently, transition rates) between each pair of states. Intuitively, the states in an MSM can be thought of as corresponding to local minima in the free energy landscape that ultimately determines a molecules structure and dynamics. MSMBuilder includes tools for - Constructing an MSM from a set of computer simulations (typically molecular dynamics simulations in standard formats like xtc, dcd, and pdb) - Validating statistical properties of MSMs - Mimicking various experimental protocols to allow a quantitative comparison with experiments - Driving efficient simulations via adaptive sampling (which decides where new simulations should be run to minimize statistical uncertainty in a model) The source code is available under the Downloads link to the left. Installation should take no more than 30 minutes. The video embedded below provides a more thorough introduction to the motivation for and use of MSMBuilder. Keywords: Free energy landscape, Markov state model
  2. Contact
    Bowman, Greg
  3. Contact
    Beauchamp, Kyle
  4. Contact
    Lane, Thomas
  5. Contact
    McGibbon, Robert
  6. Used by
    University of California, Berkeley
  7. Operating System
    Any
  8. Operating System
    Linux
  9. Operating System
    Mac OS X
  10. Software license
    Open source software license
 
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Copyright © 2016 by the President and Fellows of Harvard College
The eagle-i Consortium is supported by NIH Grant #5U24RR029825-02 / Copyright 2016