DockNet: high-throughput protein-protein interface contact prediction

Motivation: Over 300 000 protein-protein interaction (PPI) pairs have been identified in the human proteome and targeting these is fast becoming the next frontier in drug design. Predicting PPI sites, however, is a challenging task that traditionally requires computationally expensive and time-consuming docking simulations. A major weakness of modern protein docking algorithms is the inability to account for protein flexibility, which ultimately leads to relatively poor results.Results: Here, we propose DockNet, an efficient Siamese graph-based neural network method which predicts con-tact residues between two interacting proteins. Unlike other methods that only utilize a protein's surface or treat the protein structure as a rigid body, DockNet incorporates the entire protein structure and places no limits on protein flexibility during an interaction. Predictions are modeled at the residue level, based on a diverse set of input node features including residue type, surface accessibility, residue depth, secondary structure, pharmacophore and torsional angles. DockNet is comparable to current state-of-the-art methods, achieving an area under the curve (AUC) value of up to 0.84 on an independent test set (DB5), can be applied to a variety of different protein structures and can be utilized in situations where accurate unbound protein structures cannot be obtained.Availability and implementation: DockNet is available at https://github.com/npwilliams09/docknet and an easy-to-use webserver at https://biosig.lab.uq.edu.au/docknet. All other data underlying this article are available in the article and in its online supplementary material.

Automated summary

In this study, a efficient Siamese graph-based neural network method called DockNet is proposed for predicting contact residues between two interacting proteins. Unlike other methods, DockNet incorporates the entire protein structure and does not limit protein flexibility during the interaction. Using diverse input node features such as residue type, surface accessibility, residue depth, secondary structure, pharmacophore, and torsional angles, predictions are made at the residue level. DockNet achieves comparable performance to current state-of-the-art methods, with an area under the curve (AUC) value of up to 0.84 on an independent test set (DB5), and can be applied to various protein structures even when accurate unbound protein structures cannot be obtained.