Predicting reaction pathways and accelerating protein conformation sampling with machine learning.

Abstract

It is challenging to sample molecular processes with large activation energies using molecular dynamics simulations. Current enhanced sampling methodologies rely on the identification of appropriate reaction coordinates that describe reaction pathways in the free energy surface (FES). A new method, log-probability estimation via invertible neural networks for enhanced sampling (LINES), has been developed for identifying reaction coordinates and facilitating the sampling. The iterative scheme employs a normalizing flow machine learning model to learn the underlying FES in a molecular system with data generated from molecular dynamics and then applies a gradient-based optimization method to identify reaction coordinates. The model has been rigorously validated through the prediction of a water-based reaction coordinate that improves the rate of reversible binding for a β-cyclodextrin/cyclobutanol system. In addition, LINES has also been successfully applied towards the study of unfolding/refolding of chignolin, a small peptide with a stable folded conformation. Most recently, LINES has been implemented to extract the binding sites from the predicted reaction coordinates for ligand binding with human T-cell immunoglobulin and mucin domain containing protein-3 (TIM3). TIM3-ligand binding plays key roles in the immune system. In all examples, LINES demonstrates a robust approach for reaction coordinate identification that can accurately capture characteristics of a high-dimensional FES and then predict reaction pathways between different metastable states in the FES.