ArticleBioinformatics (Oxford, England)2019
BIPSPI: a method for the prediction of partner-specific protein-protein interfaces.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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44 citing papers in PubMed.
- Accurate protein-protein interactions modeling through physics-informed geometric invariant learning.Communications biology · 2026Article
- Graph Learning in Bioinformatics: A Survey of Graph Neural Network Architectures, Biological Graph Construction and Bioinformatics Applications.Biomolecules · 2026Review
- HVIface: sequence-based deep learning for decoding human-virus protein-protein interfaces.Frontiers in bioinformatics · 2026Article
- A structurally informed human protein-protein interactome reveals proteome-wide perturbations caused by disease mutations.Nature biotechnology · 2025Article
- GTE-PPIS: a protein-protein interaction site predictor based on graph transformer and equivariant graph neural network.Briefings in bioinformatics · 2025Article
- ParaSurf: a surface-based deep learning approach for paratope-antigen interaction prediction.Bioinformatics (Oxford, England) · 2025Article
- Pair-EGRET: enhancing the prediction of protein-protein interaction sites through graph attention networks and protein language models.Bioinformatics (Oxford, England) · 2024Article
- Deciphering the enigmatic PilY1 ofBiochemistry and biophysics reports · 2024Article
- A Computational Pipeline for Accurate Prioritization of Protein-Protein Binding Candidates in High-Throughput Protein Libraries.Angewandte Chemie (International ed. in English) · 2024Article
- A variational expectation-maximization framework for balanced multi-scale learning of protein and drug interactions.Nature communications · 2024Article
- ZEPPI: Proteome-scale sequence-based evaluation of protein-protein interaction models.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Computational Approaches to Predict Protein-Protein Interactions in Crowded Cellular Environments.Chemical reviews · 2024Review
- A comprehensive review of protein-centric predictors for biomolecular interactions: from proteins to nucleic acids and beyond.Briefings in bioinformatics · 2024Review
- Structurally-informed human interactome reveals proteome-wide perturbations by disease mutations.bioRxiv : the preprint server for biology · 2024Article
- Sifting Through the Noise: A Computational Pipeline for Accurate Prioritization of Protein-Protein Binding Candidates in High-Throughput Protein Libraries.bioRxiv : the preprint server for biology · 2024Article
- Machine Learning Methods in Protein-Protein Docking.Methods in molecular biology (Clifton, N.J.) · 2024Article
- DIPS-Plus: The enhanced database of interacting protein structures for interface prediction.Scientific data · 2023Article
- E(3) equivariant graph neural networks for robust and accurate protein-protein interaction site prediction.PLoS computational biology · 2023Article
- DeepBindPPI: Protein-Protein Binding Site Prediction Using Attention Based Graph Convolutional Network.The protein journal · 2023Article
- Protein-protein interfaces in molecular glue-induced ternary complexes: classification, characterization, and prediction.RSC chemical biology · 2023Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Motivation: Protein-Protein Interactions (PPI) are essentials for most cellular processes and thus, unveiling how proteins interact is a crucial question that can be better understood by identifying which residues are responsible for the interaction. Computational approaches are orders of magnitude cheaper and faster than experimental ones, leading to proliferation of multiple methods aimed to predict which residues belong to the interface of an interaction. Results: We present BIPSPI, a new machine learning-based method for the prediction of partner-specific PPI sites. Contrary to most binding site prediction methods, the proposed approach takes into account a pair of interacting proteins rather than a single one in order to predict partner-specific binding sites. BIPSPI has been trained employing sequence-based and structural features from both protein partners of each complex compiled in the Protein-Protein Docking Benchmark version 5.0 and in an additional set independently compiled. Also, a version trained only on sequences has been developed. The performance of our approach has been assessed by a leave-one-out cross-validation over different benchmarks, outperforming state-of-the-art methods. Availability and implementation: BIPSPI web server is freely available at http://bipspi.cnb.csic.es. BIPSPI code is available at https://github.com/bioinsilico/BIPSPI. Docker image is available at https://hub.docker.com/r/bioinsilico/bipspi/. Supplementary information: Supplementary data are available at Bioinformatics online.
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