VoroIF-GNN: Voronoi tessellation-derived protein-protein interface assessment using a graph neural network
Open accessPreprint — published as 10.1002/prot.26554 · Paper page
Abstract
We present VoroIF-GNN, a novel single-model method for assessing inter-subunit interfaces in protein-protein complexes. Given a multimeric protein structural model, we derive interface contacts from the Voronoi tessellation of atomic balls, construct a graph of those contacts, and predict accuracy of every contact using an attention-based graph neural network. The contact-level predictions are then summarized to produce whole interface-level scores. VoroIF-GNN was blindly tested for its ability to estimate accuracy of protein complexes during CASP15 and showed strong performance in selecting the best multimeric model out of many. The method implementation is freely available at https://klimentolechnovic.github.io/voronota/expansion_js/ .
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Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works
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Physical-aware model accuracy estimation for protein complex using deep learning method
Wang, H., Sun, M., Xie, L., Liu, D., & Zhang, G. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.01.017 · Cited by 5 · see citing works
“Inspired by the concepts of atom-atom and atom-solvent contact area [12,16 – 18], we use residue-level contact areas and contact surface-based orientation features to represent the strength of physical interactions between protein surface regions and their surrounding solvents, which may provide a…”
Influential
- Authors
- Olechnovič, K., & Venclovas, Č.
- Affiliations
- Vilnius University (LT)
- Corresponding author
- Olechnovič, Kliment
- Venclovas, Česlovas
- Venue
- Proteins Structure Function and Bioinformatics
- Publisher
- openRxiv
- Published
- 2023-04-21
- Volume
- 91
- Issue
- 12
- Pages
- 1879-1888
- ISSN
- 0887-3585 (ISSN-L)
- OpenAlex
- W4385075189
- Cited by
- 38
- References
- 1
- Citations by year
- 2026: 11 · 2025: 16 · 2024: 7 · 2023: 4
- FWCI
- 3.38
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proteins Structure Function and Bioinformatics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- Funders
- Lietuvos Mokslo Taryba (S-MIP-21-35)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Keywords
- protein-protein interfaces, Voronoi tessellation, graph neural networks, multimeric proteins, model selection
- MeSH terms
- Models, Molecular, Proteins, Neural Networks, Computer
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