Variational Identification of Markovian Transition States

Files in This Item:
File Description SizeFormat 
martini.buchete.2017.PhysRevX.pdf1.92 MBAdobe PDFDownload
Title: Variational Identification of Markovian Transition States
Authors: Martini, Linda
Kells, Adam
Covino, Roberto
Buchete, Nicolae-Viorel
et al.
Permanent link: http://hdl.handle.net/10197/9195
Date: 28-Sep-2017
Abstract: 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 identifies automatically 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) conformational pathways occurring in complex dynamical systems such as molecular trajectories.
Funding Details: Irish Research Council
Type of material: Journal Article
Publisher: American Physical Society
Copyright (published version): 2017 the Authors
Keywords: Chemical physics;Computational physics
DOI: 10.1103/PhysRevX.7.031060
Language: en
Status of Item: Peer reviewed
Appears in Collections:Physics Research Collection

Show full item record

SCOPUSTM   
Citations 50

5
Last Week
2
Last month
checked on Jun 23, 2018

Google ScholarTM

Check

Altmetric


This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.