Harrigan Matthew P, Sultan Mohammad M, Hernández Carlos X, Husic Brooke E, Eastman Peter, Schwantes Christian R, Beauchamp Kyle A, McGibbon Robert T, Pande Vijay S
Department of Chemistry, Stanford University, Stanford, California.
Program in Biophysics, Stanford University, Stanford, California.
Biophys J. 2017 Jan 10;112(1):10-15. doi: 10.1016/j.bpj.2016.10.042.
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.
MSMBuilder是一个用于构建高维时间序列数据统计模型的软件包。它的设计特别侧重于对生物分子动力学的原子模拟进行分析,如蛋白质折叠和构象变化。MSMBuilder因其构建马尔可夫状态模型(MSM)的能力而得名,这类模型在计算生物物理学家中颇受青睐。除了成熟的和较新的MSM方法外,该软件包还包括用于理解时间序列数据的互补算法,如隐马尔可夫模型和基于时间结构的独立成分分析。MSMBuilder拥有易于使用的命令行界面,并且通过其Python应用程序编程接口具有清晰一致的抽象。MSMBuilder在开发过程中充分考虑了与更广泛的机器学习社区的兼容性,遵循了scikit-learn的设计。该软件包主要由分子动力学从业者使用,但同样适用于其他计算或实验时间序列测量。