title: MSMBuilder: Statistical Models for Biomolecular Dynamics.
abstract: MSMBuilder is a software package for building statistical models of high-dimensional time-series data. It is designed with a particular focus on the analysis of atomistic simulations of biomolecular dynamics such as protein folding and conformational change. MSMBuilder is named for its ability to construct Markov state models (MSMs), a class of models that has gained favor among computational biophysicists. In addition to both well-established and newer MSM methods, the package includes complementary algorithms for understanding time-series data such as hidden Markov models and time-structure based independent component analysis. MSMBuilder boasts an easy to use command-line interface, as well as clear and consistent abstractions through its Python application programming interface. MSMBuilder was developed with careful consideration for compatibility with the broader machine learning community by following the design of scikit-learn. The package is used primarily by practitioners of molecular dynamics, but is just as applicable to other computational or experimental time-series measurements.
authors.affiliation: Department of Chemistry, Stanford University, Stanford, California.
authors.forename: Matthew P
authors.initials: MP
authors.lastname: Harrigan
authors.affiliation: Department of Chemistry, Stanford University, Stanford, California.
authors.forename: Mohammad M
authors.initials: MM
authors.lastname: Sultan
authors.affiliation: Program in Biophysics, Stanford University, Stanford, California.
authors.forename: Carlos X
authors.initials: CX
authors.lastname: Hernández
authors.affiliation: Department of Chemistry, Stanford University, Stanford, California.
authors.forename: Brooke E
authors.initials: BE
authors.lastname: Husic
authors.affiliation: Department of Chemistry, Stanford University, Stanford, California.
authors.forename: Peter
authors.initials: P
authors.lastname: Eastman
authors.affiliation: Department of Chemistry, Stanford University, Stanford, California.
authors.forename: Christian R
authors.initials: CR
authors.lastname: Schwantes
authors.affiliation: Memorial Sloan Kettering Cancer Center, New York, New York.
authors.forename: Kyle A
authors.initials: KA
authors.lastname: Beauchamp
authors.affiliation: Department of Chemistry, Stanford University, Stanford, California. Electronic address: robert.mcgibbon@deshawresearch.com.
authors.forename: Robert T
authors.initials: RT
authors.lastname: McGibbon
authors.affiliation: Department of Chemistry, Stanford University, Stanford, California; Program in Biophysics, Stanford University, Stanford, California; Department of Computer Science, Stanford University, Stanford, California; Department of Structural Biology, Stanford University, Stanford, California. Electronic address: pande@stanford.edu.
authors.forename: Vijay S
authors.initials: VS
authors.lastname: Pande
mesh_terms: D000081247:CSK Tyrosine-Protein Kinase; D008390:Markov Chains; D015233:Models, Statistical; D056004:Molecular Dynamics Simulation; D011487:Protein Conformation; D012984:Software; D019061:src-Family Kinases / Q000737:chemistry / Q000378:metabolism
chemical_list: D000081247:CSK Tyrosine-Protein Kinase; D019061:src-Family Kinases