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Journal Article

Variational Identification of Markovian Transition States


Covino,  Roberto
Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max Planck Society;


Hummer,  Gerhard
Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max Planck Society;
Institute of Biophysics, Goethe University Frankfurt, 60438 Frankfurt am Main, Germany;

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Martini, L., Covino, R., Hummer, G., Buchete, N.-V., & Rosta, E. (2017). Variational Identification of Markovian Transition States. Physical Review X, 7(3): 031060. doi:10.1103/PhysRevX.7.031060.

Cite as: https://hdl.handle.net/21.11116/0000-0001-2782-0
We present a method that enables the identification and analysis of conformational Markovian transition states from atomistic or coarse-grained molecular dynamics (MD) trajectories. Our algorithm is presented by using both analytical models and examples from MD simulations of the benchmark system helix-forming peptide Ala5, and of larger, biomedically important systems: the 15-lipoxygenase-2 enzyme (15-LOX-2), the epidermal growth factor receptor (EGFR) protein, and the Mga2 fungal transcription factor. The analysis of 15-LOX-2 uses data generated exclusively from biased umbrella sampling simulations carried out at the hybrid ab initio density functional theory (DFT) quantum mechanics/molecular mechanics (QM/MM) level of theory. In all cases, our method automatically identifies the corresponding transition states and metastable conformations in a variationally optimal way, with the input of a set of relevant coordinates, by accurately reproducing the intrinsic slowest relaxation rate of each system. Our approach offers a general yet easy-to-implement analysis method that provides unique insight into the molecular mechanism and the rare but crucial (i.e., rate-limiting) transition states occurring along conformational transition paths in complex dynamical systems such as molecular trajectories.