Advanced atom-level representations for protein flexibility prediction utilizing graph neural networks
This work addresses the challenge of predicting protein dynamics for biological and drug applications, representing an incremental advance by applying existing GNN methods to a new atomic-level representation.
The authors tackled protein flexibility prediction by introducing atomic-level graph neural networks to predict B-factors from 3D structures, achieving a correlation coefficient of 0.71 on a test set of over 4k proteins, outperforming previous methods.
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.