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REVO: Resampling of Ensembles by Variation Optimization

preprint
revised on 17.04.2019 and posted on 18.04.2019 by Nazanin Donyapour, Nicole Roussey, Alex Dickson
Conventional molecular dynamics simulations are incapable of sampling many important interactions in biomolecular systems due to their high dimensionality and rough energy landscapes. To observe rare events and calculate transition rates in these systems, enhanced sampling is a necessity. In particular, the study of ligand-protein interactions necessitates a diverse ensemble of protein conformations and transition states, and for many systems this occurs on prohibitively long timescales. Previous strategies such as WExplore that can be used to determine these types of ensembles are hindered by problems related to the regioning of conformational space. Here we propose a novel, regionless, enhanced sampling method that is based on the weighted ensemble framework. In this method, a value referred to as “trajectory variation” is optimized after each cycle through cloning and merging operations. This method allows for a more consistent measurement of observables and broader sampling resulting in the efficient exploration of previously unexplored conformations. We demonstrate the performance of this algorithm with the N-dimensional random walk and the unbinding of the trypsin-benzamidine system. The system is analyzed using conformation space networks, the residence time of benzamidine is confirmed, and a new unbinding pathway for the trypsin-benzamidine system is found. We expect that REVO will be a useful general tool to broadly explore free energy landscapes.

Funding

Revealing the Ligand Binding Landscape with Advanced Molecular Simulation Methods

National Institute of General Medical Sciences

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History

Email Address of Submitting Author

alexrd@msu.edu

Institution

Michigan State University

Country

United States

ORCID For Submitting Author

0000-0002-9640-1380

Declaration of Conflict of Interest

None

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