Identification and Analysis of Transition and Metastable Markov States

Abstract

We present a new method that enables the identification and analysis of both transition and metastable conformational states from atomistic or coarse-grained molecular dynamics (MD) trajectories. Our algorithm is presented and studied by using both analytical and actual examples from MD simulations of the helix-forming peptide Ala5, and of a larger system, the epidermal growth factor receptor (EGFR) protein. In all cases, our method identifies automatically the corresponding transition states and metastable conformations in an optimal way, with the input of a set of relevant coordinates, by capturing accurately the intrinsic slowest relaxation rate. Our approach provides a general and easy to implement analysis method that provides unique insight into the molecular mechanism and the rare but crucial rate limiting conformational pathways occurring in complex dynamical systems such as molecular trajectories.

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• Caliber based spectral gap optimization of order parameters (SGOOP) for sampling complex molecular systems
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Cited by in corpus (6)

• Special Topic: Markov Models of Molecular Kinetics
• Characterizing metastable states with the help of machine learning
• Correlation Functions, Mean First Passage Times and the Kemeny Constant
• Towards a Benchmark for Markov State Models: The Folding of HP35
• Spectral Map for Slow Collective Variables, Markovian Dynamics, and Transition State Ensembles
• Large Scale Training of Graph Neural Networks for Optimal Markov-Chain Partitioning Using the Kemeny Constant