Advanced atom-level representations for protein flexibility prediction utilizing graph neural networks

TL;DR

This paper introduces a novel approach using graph neural networks at the atomic level to predict protein flexibility, significantly improving accuracy over previous residue-level methods.

Contribution

It is the first to apply GNNs at the atomic level for protein representation learning and B-factor prediction, advancing the understanding of protein dynamics.

Findings

Meta-GNN achieves a correlation coefficient of 0.71

Outperforms previous residue-level methods

Demonstrates potential for broader protein analysis tasks

Abstract

Protein dynamics play a crucial role in many biological processes and drug interactions. However, measuring, and simulating protein dynamics is challenging and time-consuming. While machine learning holds promise in deciphering the determinants of protein dynamics from structural information, most existing methods for protein representation learning operate at the residue level, ignoring the finer details of atomic interactions. In this work, we propose for the first time to use graph neural networks (GNNs) to learn protein representations at the atomic level and predict B-factors from protein 3D structures. The B-factor reflects the atomic displacement of atoms in proteins, and can serve as a surrogate for protein flexibility. We compared different GNN architectures to assess their performance. The Meta-GNN model achieves a correlation coefficient of 0.71 on a large and diverse test set of over 4k proteins (17M atoms) from the Protein Data Bank (PDB), outperforming previous methods by a large margin. Our work demonstrates the potential of representations learned by GNNs for protein flexibility prediction and other related tasks.