VN-EGNN: E(3)-Equivariant Graph Neural Networks with Virtual Nodes Enhance Protein Binding Site Identification

TL;DR

This paper introduces VN-EGNN, an advanced E(3)-equivariant graph neural network with virtual nodes, significantly improving the accuracy of protein binding site identification by modeling hidden geometric features.

Contribution

The paper proposes a novel extension of EGNNs with virtual nodes and extended message passing, enhancing binding site prediction performance.

Findings

Sets new state-of-the-art on COACH420, HOLO4K, and PDBbind2020 datasets.

Improves binding site center localization accuracy.

Demonstrates the effectiveness of virtual nodes in GNNs for biological tasks.

Abstract

Being able to identify regions within or around proteins, to which ligands can potentially bind, is an essential step to develop new drugs. Binding site identification methods can now profit from the availability of large amounts of 3D structures in protein structure databases or from AlphaFold predictions. Current binding site identification methods heavily rely on graph neural networks (GNNs), usually designed to output E(3)-equivariant predictions. Such methods turned out to be very beneficial for physics-related tasks like binding energy or motion trajectory prediction. However, the performance of GNNs at binding site identification is still limited potentially due to the lack of dedicated nodes that model hidden geometric entities, such as binding pockets. In this work, we extend E(n)-Equivariant Graph Neural Networks (EGNNs) by adding virtual nodes and applying an extended message passing scheme. The virtual nodes in these graphs are dedicated quantities to learn representations of binding sites, which leads to improved predictive performance. In our experiments, we show that our proposed method VN-EGNN sets a new state-of-the-art at locating binding site centers on COACH420, HOLO4K and PDBbind2020.