Towards the prediction of order parameters from molecular dynamics simulations in proteins.

The Journal of Chemical Physics
Juan R Perilla, Thomas B Woolf

Abstract

A molecular understanding of how protein function is related to protein structure requires an ability to understand large conformational changes between multiple states. Unfortunately these states are often separated by high free energy barriers and within a complex energy landscape. This makes it very difficult to reliably connect, for example by all-atom molecular dynamics calculations, the states, their energies, and the pathways between them. A major issue needed to improve sampling on the intermediate states is an order parameter--a reduced descriptor for the major subset of degrees of freedom--that can be used to aid sampling for the large conformational change. We present a method to combine information from molecular dynamics using non-linear time series and dimensionality reduction, in order to quantitatively determine an order parameter connecting two large-scale conformationally distinct protein states. This new method suggests an implementation for molecular dynamics calculations that may be used to enhance sampling of intermediate states.

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Citations

Feb 25, 2014·The Journal of Chemical Physics·Matthias U BohnerJohannes Kästner
Jul 16, 2014·Journal of Chemical Theory and Computation·Mahmoud Moradi, Emad Tajkhorshid
Aug 26, 2016·Biophysical Journal·Samuel DemharterPeter Minary
Nov 14, 2013·Journal of Chemical Theory and Computation·Yifei Qi, Wonpil Im
Apr 1, 2017·Future Medicinal Chemistry·Giuseppe Deganutti, Stefano Moro
May 21, 2015·Journal of Computational Chemistry·Raimondas Galvelis, Yuji Sugita

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Methods Mentioned

BETA
PCA
NMR

Software Mentioned

CHARMM
CHARMM27FF
DIMS

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