Markov State Modeling

Markov State Modeling (MSM) is  used to predict both stationary and kinetic quantities on long timescales using a set of atomistic molecular dynamics simulations that are individually much shorter, thus addressing the well-known sampling problem in molecular dynamics simulation.

Markov State Modeling can be performed using the following software tools:

• MSM Builder
• PyEmma

For examples of previously performed studies in which Markov State Modeling was the primary method used, see the following example cases:

• Markov state modeling reveals alternative unbinding pathways for peptide-MHC complexes.
• On the role of solvent in hydrophobic cavity-ligand recognition kinetics.
• Ligand Binding Rate Constants in Heme Proteins Using Markov State Models and Molecular Dynamics Simulations.
• Atomic resolution mechanism of ligand binding to a solvent inaccessible cavity in T4 lysozyme.
• Protein conformational plasticity and complex ligand-binding kinetics explored by atomistic simulations and Markov models.
• On-the-Fly Learning and Sampling of Ligand Binding by High-Throughput Molecular Simulations.
• Complete reconstruction of an enzyme-inhibitor binding process by molecular dynamics simulations.

Markov State Modeling was also used in the following examples:

• Multiscale Approach for Computing Gated Ligand Binding from Molecular Dynamics and Brownian Dynamics Simulations.
• Membrane-mediated ligand unbinding of the PK-11195 ligand from TSPO.
• An Integrated Markov State Model and Path Metadynamics Approach to Characterize Drug Binding Processes.
• How Effectively Can Adaptive Sampling Methods Capture Spontaneous Ligand Binding?
• Mapping the Ligand Binding Landscape.
• Computational Study of Protein-Ligand Unbinding for Enzyme Engineering.
• Unbinding Kinetics of a p38 MAP Kinase Type II Inhibitor from Metadynamics Simulations.
• Bridging Microscopic and Macroscopic Mechanisms of p53-MDM2 Binding with Kinetic Network Models.
• Protein-peptide association kinetics beyond the seconds timescale from atomistic simulations.