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Proc Natl Acad Sci U S A. 2017 Jul 11;114(28):E5494-E5503. doi: 10.1073/pnas.1621481114. Epub 2017 Jun 20.

Intrinsic map dynamics exploration for uncharted effective free-energy landscapes.

Author information

1
Energy Department, Politecnico di Torino, Turin 10129, Italy.
2
Department of Theoretical Biophysics, Max Planck Institute of Biophysics, 60438 Frankfurt am Main, Germany.
3
Department of Mathematics, Program in Applied Mathematics, Yale University, New Haven, CT 06510.
4
Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544.
5
Institute of Biophysics, Goethe University, 60438 Frankfurt am Main, Germany.
6
Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544; yannis@princeton.edu.
7
The Program in Applied & Computational Mathematics, Princeton University, Princeton, NJ 08544.
8
Institute for Advanced Study Technical University of Munich, 85748 Garching, Germany.

Abstract

We describe and implement a computer-assisted approach for accelerating the exploration of uncharted effective free-energy surfaces (FESs). More generally, the aim is the extraction of coarse-grained, macroscopic information from stochastic or atomistic simulations, such as molecular dynamics (MD). The approach functionally links the MD simulator with nonlinear manifold learning techniques. The added value comes from biasing the simulator toward unexplored phase-space regions by exploiting the smoothness of the gradually revealed intrinsic low-dimensional geometry of the FES.

KEYWORDS:

enhanced sampling methods; free-energy surface; machine learning; model reduction; protein folding

PMID:
28634293
PMCID:
PMC5514710
[Available on 2018-01-11]
DOI:
10.1073/pnas.1621481114

Conflict of interest statement

The authors declare no conflict of interest.

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