Learning Protein Dynamics with Metastable Switching Systems

TL;DR

This paper presents a novel machine learning method using metastable switching systems to accurately model protein dynamics from molecular simulation data, improving temporal coherence and transition path sampling.

Contribution

It introduces a physically-inspired metastable switching linear dynamical system with an EM algorithm and a Frank-Wolfe based solver, advancing protein dynamics modeling.

Findings

Enhanced temporal coherence over traditional models

Accurate transition path sampling for Src-kinase

Effective modeling of large molecular datasets

Abstract

We introduce a machine learning approach for extracting fine-grained representations of protein evolution from molecular dynamics datasets. Metastable switching linear dynamical systems extend standard switching models with a physically-inspired stability constraint. This constraint enables the learning of nuanced representations of protein dynamics that closely match physical reality. We derive an EM algorithm for learning, where the E-step extends the forward-backward algorithm for HMMs and the M-step requires the solution of large biconvex optimization problems. We construct an approximate semidefinite program solver based on the Frank-Wolfe algorithm and use it to solve the M-step. We apply our EM algorithm to learn accurate dynamics from large simulation datasets for the opioid peptide met-enkephalin and the proto-oncogene Src-kinase. Our learned models demonstrate significant improvements in temporal coherence over HMMs and standard switching models for met-enkephalin, and sample transition paths (possibly useful in rational drug design) for Src-kinase.