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 molecule’s 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.
Purpose/Synopsis: Provide extensible software for building Markov State Models.
Audience: Computational researchers interested in using kinetic information to cluster their data and build Markov State Models.
Long Term Goals and Related Uses: Allow users to easily build Markov State Models to describe the free energy landscape defined by their data.
Currently, we are working on the following features: - improved clustering algorithms - faster code - new trajectory readers for other molecular dynamics packages - an entirely python based interface
SimTK, the Simulation Toolkit, is a part of the Simbios project funded
by the National Institutes of Health
through the NIH Roadmap for Medical Research, Grant U54 GM072970. Information
on the National Centers for
Biomedical Computing can be obtained