VN-EGNN: E(3)- and SE(3)-Equivariant Graph Neural Networks with Virtual Nodes Enhance Protein Binding Site Identification
Keywords: Binding site, protein, equivariance, graph neural network, message passing
TL;DR: We present an equivariant graph neural net approach that includes virtual nodes for strongly improved binding site identification
Abstract: Being able to identify regions within or around proteins, to which ligands can potentially bind, is an essential step in developing 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 a lack of expressiveness capable of modeling higher-order 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 entities 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.
Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)
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Submission Number: 9899
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