Hierarchical geometric deep learning enables scalable analysis of molecular dynamics

TL;DR

This paper introduces a hierarchical geometric deep learning method that efficiently analyzes large-scale molecular dynamics simulations by aggregating local information, enabling detailed and scalable analysis of complex biomolecular systems.

Contribution

The authors develop a hierarchical GNN approach that reduces memory and runtime for analyzing large biomolecular simulations without losing atomic detail.

Findings

Enables analysis of protein-nucleic acid complexes with thousands of residues on single GPUs.

Improves performance and interpretability over existing methods for systems with hundreds of residues.

Allows detailed analysis of long-range interactions in large biomolecular systems.

Abstract

Molecular dynamics simulations can generate atomically detailed trajectories of complex systems, but analyzing these dynamics can be challenging when systems lack well-established quantitative descriptors (features). Graph neural networks (GNNs) in which messages are passed between nodes that represent atoms that are spatial neighbors promise to obviate manual feature engineering, but the use of GNNs with biomolecular systems of more than a few hundred residues has been limited in the context of analyzing dynamics by both difficulties in capturing the details of long-range interactions with message passing and the memory and runtime requirements associated with large graphs. Here, we show how local information can be aggregated to reduce memory and runtime requirements without sacrificing atomic detail. We demonstrate that this approach opens the door to analyzing simulations of protein-nucleic acid complexes with thousands of residues on single GPUs within minutes. For systems with hundreds of residues, for which there are sufficient data to make quantitative comparisons, we show that the approach improves performance and interpretability.