Fast Sampling of Protein Conformational Dynamics

TL;DR

This paper introduces a new method for efficiently isolating low-frequency vibrations in proteins, enabling enhanced sampling of conformational transitions and faster generation of conformational ensembles for protein dynamics studies.

Contribution

The authors adapted a vibration analysis approach to isolate low-frequency modes in proteins, improving sampling efficiency for conformational changes.

Findings

Method reliably isolates low-frequency vibrations in proteins.

Enhanced sampling produces conformational ensembles on high throughput timescales.

Approach is reproducible across diverse protein structures.

Abstract

Protein function does not solely depend on structure but often relies on dynamical transitions between distinct conformations. Despite this fact, our ability to characterize or predict protein dynamics is substantially less developed compared to state-of-the-art protein structure prediction. Molecular simulations provide unique opportunities to study protein dynamics, but the timescales associated with conformational changes generate substantial challenges. Enhanced sampling algorithms with collective variables can greatly reduce the computational cost of sampling slow processes. However, defining collective variables suitable to enhance sampling of protein conformational transitions is non-trivial. Low-frequency vibrations have long been considered as promising candidates for collective variable but their identification so far relied on assumptions inherently invalid at low frequencies. We recently introduced an analysis of molecular vibrations that does not rely on such approximations and remains accurate at low frequencies. Here, we modified this approach to efficiently isolate low-frequency vibrations in proteins and applied it to a set of five proteins of varying complexity. We demonstrate that our approach is not only highly reproducible but results in collective variables that consistently enhance sampling of protein conformational transitions and associated free energy surfaces on timescales compatible with high throughput applications. This enables the efficient generation of protein conformational ensembles, which will be key for future prediction algorithms aiming beyond static protein structures.