VN-EGNN: E(3)-Equivariant Graph Neural Networks with Virtual Nodes
  Enhance Protein Binding Site Identification
Florian Sestak Lisa Schneckenreiter Johannes Brandstetter Sepp Hochreiter Andreas Mayr Günter Klambauer
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.