MasterMSM

MasterMSM constructs Markov state models (MSMs) from molecular dynamics (MD) simulation trajectories using a chemical master equation formulation to extract long-timescale kinetics from short-time microscopic observations.

Key Features:

• Implementation: Implemented as a Python package for constructing and analyzing MSMs from MD trajectories.
• Master Equation Formulation: Uses the chemical master equation as the foundational framework for model construction.
• Transition Rate Estimation: Provides algorithms to estimate transition rates from short-time transition data.
• Core-set State Assignment: Includes methods to define core-sets for assigning states in the MSM.
• Committors and Fluxes: Computes committor probabilities and fluxes on the resulting transition networks.
• Sensitivity Analysis: Performs sensitivity analyses on transition networks to assess parameter and model robustness.
• Equilibrium and Dynamical Properties: Extracts equilibrium and dynamical observables for comparison with experimental measurements.

Scientific Applications:

• Long-timescale kinetics inference: Infers long-timescale kinetic rates and state-to-state transitions from short MD trajectories.
• Biomolecular transition analysis: Characterizes biomolecular transitions and pathways relevant to molecular interactions and conformational changes.
• Mechanistic interpretation: Elucidates mechanisms of molecular processes by combining MSM-derived committors, fluxes, and rate estimates.
• Comparison with experiments: Enables systematic comparison of MSM-derived equilibrium and dynamical properties with experimental data.

Methodology:

Constructs MSMs using short-time transition data from MD trajectories within a chemical master equation framework and applies algorithms for transition rate estimation, core-set definition for state assignment, committor and flux computation, and sensitivity analysis of transition networks.