  Protein-protein interactions are of great importance in biochemical
processes. Accurate prediction of protein-protein interaction sites (PPIs) is
crucial for our understanding of biological mechanism. Although numerous
approaches have been developed recently and achieved gratifying results, there
are still two limitations: (1) Most existing models have excavated a number of
useful input features, but failed to take coevolutionary features into account,
which could provide clues for inter-residue relationships; (2) The
attention-based models only allocate attention weights for neighboring
residues, instead of doing it globally, which may limit the model's prediction
performance since some residues being far away from the target residues might
also matter.
  We propose a coevolution-enhanced global attention neural network, a
sequence-based deep learning model for PPIs prediction, called CoGANPPIS.
Specifically, CoGANPPIS utilizes three layers in parallel for feature
extraction: (1) Local-level representation aggregation layer, which aggregates
the neighboring residues' features as the local feature representation; (2)
Global-level representation learning layer, which employs a novel
coevolution-enhanced global attention mechanism to allocate attention weights
to all residues on the same protein sequences; (3) Coevolutionary information
learning layer, which applies CNN & pooling to coevolutionary information to
obtain the coevolutionary profile representation. Then, the three outputs are
concatenated and passed into several fully connected layers for the final
prediction. Extensive experiments on two benchmark datasets have been
conducted, demonstrating that our proposed model achieves the state-of-the-art
performance.
