Auto-WHATMD : Automated Wasserstein-based High-dimensional feature extraction Analysis of Trajectories from Molecular Dynamics

Abstract

Comparing multiple protein systems with variation such as different binding ligands or mutations, and understanding their effects is one of the objectives in molecular dynamics simulations. Representation of these systems by a few features enables quantitative comparison. However, because molecular dynamics simulation trajectories are high-dimensional spatiotemporal data, selection of key features relies on domain expertise, sometimes introducing arbitrary assumptions. Here, we present an approach that uses the optimal transport distance to compare high-dimensional trajectory data, and employs simulated annealing to identify the residues that best distinguish multiple systems. We term this algorithm auto-WHATMD (automated Wasserstein-based High-dimensional feature extraction Analysis for Trajectories of Molecular Dynamics). We applied auto-WHATMD to multiple protein-ligand systems of bromodomain 4 with different ligands, identifying the most discriminative residues in the loop region. Moreover, even a few selected residues were sufficient to capture the correlation with ligand-binding affinities, indicating that auto-WHATMD effectively prioritizes the most informative residues. Our approach can be used to efficiently determine key residues and design features for multiple analogous systems.