Accelerating all-atom simulations and gaining mechanistic understanding of biophysical systems through State Predictive Information Bottleneck

TL;DR

This paper introduces the use of the State Predictive Information Bottleneck (SPIB) method to learn reaction coordinates for molecular dynamics, significantly accelerating simulations and providing mechanistic insights into biophysical processes.

Contribution

The paper demonstrates how SPIB can learn reaction coordinates from under-sampled data, enabling over 40-fold acceleration in simulating complex biophysical systems.

Findings

Achieved over 40x acceleration in molecular dynamics simulations.

SPIB learned reaction coordinates reveal mechanistic insights.

Successfully applied to chirality transitions and molecule permeation.

Abstract

An effective implementation of enhanced sampling algorithms for molecular dynamics simulations requires a priori knowledge of the approximate reaction coordinate describing the relevant mechanisms in the system. Here we demonstrate how the artificial intelligence based recent State Predictive Information Bottleneck (SPIB) approach can learn such a reaction coordinate as a deep neural network even from under-sampled trajectories. We demonstrate its usefulness by achieving more than 40 magnitudes of acceleration in simulating two test-piece biophysical systems through well-tempered metadynamics performed by biasing along the SPIB learned reaction coordinate. These include left- to right- handed chirality transitions in a synthetic protein (Aib)_9, and permeation of a small, asymmetric molecule benzoic acid through a synthetic, symmetric phospholipid bilayer. In addition to significantly accelerating the dynamics and achieving back-and-forth movement between different metastable states, the SPIB based reaction coordinate gives mechanistic insight into the processes driving these two important problems.