Data-driven Computation of Molecular Reaction Coordinates

TL;DR

This paper introduces a new data-driven numerical method for computing molecular reaction coordinates, linking transition path theory and Markov models, and demonstrates its effectiveness on a small protein system.

Contribution

It extends a recent reaction coordinate computation method to practical use in chemistry, enabling global calculation from standard simulation data.

Findings

Accurately approximates long-term system behavior

Works with common simulation data like trajectories and point clouds

Demonstrated on a small protein system

Abstract

The identification of meaningful reaction coordinates plays a key role in the study of complex molecular systems whose essential dynamics is characterized by rare or slow transition events. In a recent publication, precise defining characteristics of such reaction coordinates were identified and linked to the existence of a so-called transition manifold. This theory gives rise to a novel numerical method for the pointwise computation of reaction coordinates that relies on short parallel MD simulations only, but yields accurate approximation of the long time behavior of the system under consideration. This article presents an extension of the method towards practical applicability in computational chemistry. It links the newly defined reaction coordinates to concepts from transition path theory and Markov state model building. The main result is an alternative computational scheme that allows for a global computation of reaction coordinates based on commonly available types of simulation data, such as single long molecular trajectories, or the push-forward of arbitrary canonically-distributed point clouds. It is based on a Galerkin approximation of the transition manifold reaction coordinates, that can be tuned to individual requirements by the choice of the Galerkin ansatz functions. Moreover, we propose a ready-to-implement variant of the new scheme, that computes data-fitted, mesh-free ansatz functions directly from the available simulation data. The efficacy of the new method is demonstrated on a small protein system.