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. 2023 Jan 1;39(1):btac797.
doi: 10.1093/bioinformatics/btac797.

DockNet: high-throughput protein-protein interface contact prediction

Nathan P Williams  1 , Carlos H M Rodrigues  2   3 , Jia Truong  1 , David B Ascher  2   3 , Jessica K Holien  1
Affiliations

DockNet: high-throughput protein-protein interface contact prediction

Nathan P Williams et al. Bioinformatics. .

Abstract

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 contact 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.

Supplementary information: Supplementary data are available at Bioinformatics online.

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Figures

Fig. 1.
Fig. 1.
Performance of DockNet on rigid-body (A and B) and difficult (C and D) PPI from the DB5 database. (A and C) The predicted pairwise residue contact probability matrix for the interactions between HIV-1 capsid and cyclophilin A protein (A), Staphostatin B and Staphopain B (C). Structure of the complexes are shown in (B) (PDB: 1ak4) and (D) (PDB: 1pxv). Interface residues are highlighted as sticks on the protein structure and marked as squares on the heatmap
See this image and copyright information in PMC

References

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