Provide extensible software for building Markov State Models.
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)
For more information, including the latest releases, see our website at
MSMBuilder 1.0 releasedDec 14, 2010
We've released MSMBuilder 1.0 and are already working hard on version 2.0.
Greg Bowman awarded the 2010 Kuhn Paradigm Shift AwardMar 29, 2010
The Thomas Kuhn Paradigm Shift Award is given by the American Chemical Society (ACS) in honor of researchers who have changed the way we look at scientific problems.
Greg Bowman was one of five researchers in the world competing for the award at the March 2010 ACS meeting.
MSMBuilder for analyzing dynamics releasedApr 20, 2009
MSMBuilder is an open-source software package for automatically constructing Markov State Models (MSMs) for stochastic processes, mapping out the metastable states of a molecule (or other system) and the transition rates between them.