Markov (state) models (MSMs) and related approaches are coarse-grained models of the molecular dynamics consisting of distinct molecular structures / conformations, and the transition rates between them. Markov models can systematically reconcile simulation data from either a few long or many short simulations and allow us to analyze the essential metastable structures, thermodynamics, and kinetics of the molecular system under investigation. However, the estimation, validation, and analysis of such models is far from trivial and involves sophisticated and often numerically sensitive methods.
PyEMMA 2 provides accurate and efficient algorithms for kinetic model construction and analysis. PyEMMA is a python package, runs under all common OSes and can be used through Python user scripts or interactively in IPython notebooks. Its functionalities include:
- Read all commonly used MD input formats (powered by mdtraj).
- Featurize your trajectories, i.e. transform Cartesian coordinates into dihedrals, distances, contact maps or custom features.
- Find the slowest collective coordinates (reaction coordinates) using time-lagged independent component analysis (TICA).
- Cluster and discretize the state space.
- Process data either in memory or streaming mode - that way you can work even with very large / many trajectories and big molecular systems.
- Estimate and validate Markov state models (MSMs). Computer their statistical distribution and uncertainty using Bayesian MSMs.
- Computing long-lived (metastable) states and structures with Perron-cluster cluster analysis (PCCA)
- Perform systematic coarse-graining of MSMs to transition models with few states.
- Estimate hidden Markov Models (HMM) and Bayesian HMMs (powered by bhmm).
- Take advantage of extensive analysis options for MSMs and HMMs, e.g. calculate committor probabilities, mean first passage times, transition rates, experimental expectation values and time-correlation functions (powered by msmtools).
- Explore mechanisms of protein folding, protein-ligand association or conformational changes using Transition Path Theory (TPT).
- Plot and visualize your results in paper-ready form.
Installation, Documentation and Tutorials: http://pyemma.org
Post issues / participate in development: https://github.com/markovmodel/pyemma
PyEMMA Paper: M. K. Scherer, B. Trendelkamp-Schroer, F. Paul, G. Pérez-Hernandez, M. Hoffmann, N. Plattner, C. Wehmeyer, J.-H. Prinz and F. Noé: PyEMMA 2: A software package for estimation, validation and analysis of Markov models. Journal of Chemical Theory and Computation 11, 5525,5542 (2015)