Markov State Models of Molecular Simulations to Study Protein Folding and Dynamics

Abstract

Proteins are essential units of life that govern several functions. Understanding their behavior is closely related to their conformations, native folds, and change in conformations. Thus, the dynamic information of protein becomes essential to understand its properties at the molecular level. The molecular dynamics (MD) simulation approach provides atomistic-level dynamic information about proteins. However, more extended or complex MD simulations of protein are challenging to analyze and to gather meaningful confirmation from several snapshots of the dynamic system. To achieve it, i.e., analyzing MD simulation data, Markov State Model (MSM) is a powerful tool that has a statistical background. It represents the MD simulation system as a combination of finite memoryless states, i.e., states that are not dependent on prior states and transition probability among such states. MSM applications have grown from peptides to membrane protein simulations. The present book chapter sheds light on MD simulation’s role in protein dynamics and why MSM is required. The brief theoretical aspects of MSM techniques are demonstrated. Lastly, the chapter discusses the application of MSM in different protein folding and dynamics.