Order parameter prediction from molecular dynamics simulations in proteins

TL;DR

This paper introduces a novel method combining non-linear time series analysis and dimensionality reduction to identify order parameters that improve sampling of large conformational changes in proteins during molecular dynamics simulations.

Contribution

It presents a new approach to determine order parameters from molecular dynamics data, enhancing the sampling of intermediate states in protein conformational transitions.

Findings

Method effectively identifies order parameters connecting distinct protein states.

Potential to dramatically improve sampling efficiency in molecular dynamics.

Applicable to complex energy landscapes with high free energy barriers.

Abstract

A molecular understanding of how protein function is related to protein structure will require 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 novel way 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 dramatically enhance sampling of intermediate states.