ArticleBioinformatics (Oxford, England)2018
IntPred: a structure-based predictor of protein-protein interaction sites.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- MED-PPIS: Multi-order Moments External Graph Attention Network with Dual-Axis Attention for Protein-Protein Interaction Site Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Soft Computing Approaches toward Density-Based Analysis of Many-Electron Systems.ACS physical chemistry Au · 2026Review
- Accurate Identification of Protein Binding Sites for All Drug Modalities Using ALLSites.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Other Techniques.Advances in biochemical engineering/biotechnology · 2026Review
- Relational subgraphs fused with complete subgraphs based on the knowledge graph for mining protein complexes.Scientific reports · 2025Article
- MESM: integrating multi-source data for high-accuracy protein-protein interactions prediction through multimodal language models.BMC biology · 2025Article
- ASCE-PPIS: a protein-protein interaction sites predictor based on equivariant graph neural network with fusion of structure-aware pooling and graph collapse.Bioinformatics (Oxford, England) · 2025Article
- Gated-GPS: enhancing protein-protein interaction site prediction with scalable learning and imbalance-aware optimization.Briefings in bioinformatics · 2025Article
- MGMA-PPIS: Predicting the protein-protein interaction site with multiview graph embedding and multiscale attention fusion.GigaScience · 2025Article
- Pair-EGRET: enhancing the prediction of protein-protein interaction sites through graph attention networks and protein language models.Bioinformatics (Oxford, England) · 2024Article
- Assessing the functional impact of protein binding site definition.Protein science : a publication of the Protein Society · 2024Article
- Machine Learning Empowering Drug Discovery: Applications, Opportunities and Challenges.Molecules (Basel, Switzerland) · 2024Review
- MEG-PPIS: a fast protein-protein interaction site prediction method based on multi-scale graph information and equivariant graph neural network.Bioinformatics (Oxford, England) · 2024Article
- Machine Learning Methods in Protein-Protein Docking.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Protein-protein interaction site prediction by model ensembling with hybrid feature and self-attention.BMC bioinformatics · 2023Article
- E(3) equivariant graph neural networks for robust and accurate protein-protein interaction site prediction.PLoS computational biology · 2023Article
- SENSDeep: An Ensemble Deep Learning Method for Protein-Protein Interaction Sites Prediction.Interdisciplinary sciences, computational life sciences · 2023Article
- A Transformer-Based Ensemble Framework for the Prediction of Protein-Protein Interaction Sites.Research (Washington, D.C.) · 2023Article
- ADH-PPI: An attention-based deep hybrid model for protein-protein interaction prediction.iScience · 2022Article
- Struct2Graph: a graph attention network for structure based predictions of protein-protein interactions.BMC bioinformatics · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationProtein-protein interactions are vital for protein function with the average protein having between three and ten interacting partners. Knowledge of precise protein-protein interfaces comes from crystal structures deposited in the Protein Data Bank (PDB), but only 50% of structures in the PDB are complexes. There is therefore a need to predict protein-protein interfaces in silico and various methods for this purpose. Here we explore the use of a predictor based on structural features and which exploits random forest machine learning, comparing its performance with a number of popular established methods.
resultsOn an independent test set of obligate and transient complexes, our IntPred predictor performs well (MCC = 0.370, ACC = 0.811, SPEC = 0.916, SENS = 0.411) and compares favourably with other methods. Overall, IntPred ranks second of six methods tested with SPPIDER having slightly better overall performance (MCC = 0.410, ACC = 0.759, SPEC = 0.783, SENS = 0.676), but considerably worse specificity than IntPred. As with SPPIDER, using an independent test set of obligate complexes enhanced performance (MCC = 0.381) while performance is somewhat reduced on a dataset of transient complexes (MCC = 0.303). The trade-off between sensitivity and specificity compared with SPPIDER suggests that the choice of the appropriate tool is application-dependent. AVAILABILITY AND IMPLEMENTATION: IntPred is implemented in Perl and may be downloaded for local use or run via a web server at www.bioinf.org.uk/intpred/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.