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.
Highlights
Recent advances in both parallelizable computational software and the development of highly efficient supercomputers have extended the time scale accessible to atomistic molecular dynamics (MD) of biomolecules with explicit solvent representations to simulations up to the order of milliseconds [1,2,3]
We present a new approach to automatically identify relevant metastable and transition states along the available reaction coordinates describing a molecular process
The short-lived state is an optimally constructed transition state, revealed automatically by our algorithm applied to analytical Markov models for arbitrary free-energy functions
Summary
Recent advances in both parallelizable computational software and the development of highly efficient supercomputers have extended the time scale accessible to atomistic molecular dynamics (MD) of biomolecules with explicit solvent representations to simulations up to the order of milliseconds [1,2,3]. We propose a method for the automatic identification and analysis of both MS and TS conformational regions, by aiming to optimally construct MSMs via the slowest relaxation time using a set of discretized RCs. The use of key RCs has long been a successful approach in many of the major enhanced sampling methods [35,36,37,38], as a basis for a more intuitive understanding of the data [39]. This approach enables users to find and analyze the minimal required number of MSs relevant to the kinetics of the underlying dynamic process
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