ArticlePloS one2011
Predictions of hot spot residues at protein-protein interfaces using support vector machines.
Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
28 citing papers in PubMed.
- PPI-hotspoteLife · 2024Article
- Deep learning for the PSIPRED Protein Analysis Workbench.Nucleic acids research · 2024Article
- Computational Simulations Highlight the IL2Rα Binding Potential of Polyphenol Stilbenes from Fenugreek.Molecules (Basel, Switzerland) · 2022Article
- An Overview of Antiviral Peptides and Rational Biodesign Considerations.Biodesign research · 2022Review
- In Silico Investigation of Potential Applications of Gamma Carbonic Anhydrases as Catalysts of COInternational journal of molecular sciences · 2021Article
- Review
- Recognition of Potential COVID-19 Drug Treatments through the Study of Existing Protein-Drug and Protein-Protein Structures: An Analysis of Kinetically Active Residues.Biomolecules · 2020Article
- The PSIPRED Protein Analysis Workbench: 20 years on.Nucleic acids research · 2019Article
- Machine Learning Approaches for Protein⁻Protein Interaction Hot Spot Prediction: Progress and Comparative Assessment.Molecules (Basel, Switzerland) · 2018Review
- Heterodimer Binding Scaffolds Recognition via the Analysis of Kinetically Hot Residues.Pharmaceuticals (Basel, Switzerland) · 2018Article
- Article
- Systematic Identification of Machine-Learning Models Aimed to Classify Critical Residues for Protein Function from Protein Structure.Molecules (Basel, Switzerland) · 2017Article
- Breaking the color barrier - a multi-selective antibody reporter offers innovative strategies of fluorescence detection.Journal of cell science · 2017Article
- Prediction of Protein Hotspots from Whole Protein Sequences by a Random Projection Ensemble System.International journal of molecular sciences · 2017Article
- Peptiderive server: derive peptide inhibitors from protein-protein interactions.Nucleic acids research · 2016Article
- Surfing the Protein-Protein Interaction Surface Using Docking Methods: Application to the Design of PPI Inhibitors.Molecules (Basel, Switzerland) · 2015Review
- Structure-based inhibition of protein-protein interactions.European journal of medicinal chemistry · 2015Review
- Reliable estimation of prediction errors for QSAR models under model uncertainty using double cross-validation.Journal of cheminformatics · 2014Article
- Thermodynamic signatures of the antigen binding site of mAb 447-52D targeting the third variable region of HIV-1 gp120.Biochemistry · 2013Article
- Scalable web services for the PSIPRED Protein Analysis Workbench.Nucleic acids research · 2013Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Protein-protein interactions are critically dependent on just a few 'hot spot' residues at the interface. Hot spots make a dominant contribution to the free energy of binding and they can disrupt the interaction if mutated to alanine. Here, we present HSPred, a support vector machine(SVM)-based method to predict hot spot residues, given the structure of a complex. HSPred represents an improvement over a previously described approach (Lise et al, BMC Bioinformatics 2009, 10:365). It achieves higher accuracy by treating separately predictions involving either an arginine or a glutamic acid residue. These are the amino acid types on which the original model did not perform well. We have therefore developed two additional SVM classifiers, specifically optimised for these cases. HSPred reaches an overall precision and recall respectively of 61% and 69%, which roughly corresponds to a 10% improvement. An implementation of the described method is available as a web server at http://bioinf.cs.ucl.ac.uk/hspred. It is free to non-commercial users.
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Registered trials
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