ArticleOncotarget2016
Predicting hot spots in protein interfaces based on protrusion index, pseudo hydrophobicity and electron-ion interaction pseudopotential features.
Article in Oncotarget, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Article
- Advances in protein-protein interaction prediction: a deep learning perspective.Frontiers in bioinformatics · 2025Review
- Identification of Small-Molecule Inhibitors of Fibroblast Growth Factor 23 Signaling via In Silico Hot Spot Prediction and Molecular Docking to α-Klotho.Journal of chemical information and modeling · 2022Article
- Nabe: an energetic database of amino acid mutations in protein-nucleic acid binding interfaces.Database : the journal of biological databases and curation · 2021Article
- An improved DNA-binding hot spot residues prediction method by exploring interfacial neighbor properties.BMC bioinformatics · 2021Article
- Identification of hot regions in hub protein-protein interactions by clustering and PPRA optimization.BMC medical informatics and decision making · 2021Article
- Predicting Hot Spot Residues at Protein-DNA Binding Interfaces Based on Sequence Information.Interdisciplinary sciences, computational life sciences · 2021Article
- Prediction of hot spots in protein-DNA binding interfaces based on supervised isometric feature mapping and extreme gradient boosting.BMC bioinformatics · 2020Article
- Hot spot prediction in protein-protein interactions by an ensemble system.BMC systems biology · 2018Article
- Machine Learning Approaches for Protein⁻Protein Interaction Hot Spot Prediction: Progress and Comparative Assessment.Molecules (Basel, Switzerland) · 2018Review
- Enhanced Prediction of Hot Spots at Protein-Protein Interfaces Using Extreme Gradient Boosting.Scientific reports · 2018Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The identification of hot spots, a small subset of protein interfaces that accounts for the majority of binding free energy, is becoming more important for the research of drug design and cancer development. Based on our previous methods (APIS and KFC2), here we proposed a novel hot spot prediction method. For each hot spot residue, we firstly constructed a wide variety of 108 sequence, structural, and neighborhood features to characterize potential hot spot residues, including conventional ones and new one (pseudo hydrophobicity) exploited in this study. We then selected 3 top-ranking features that contribute the most in the classification by a two-step feature selection process consisting of minimal-redundancy-maximal-relevance algorithm and an exhaustive search method. We used support vector machines to build our final prediction model. When testing our model on an independent test set, our method showed the highest F1-score of 0.70 and MCC of 0.46 comparing with the existing state-of-the-art hot spot prediction methods. Our results indicate that these features are more effective than the conventional features considered previously, and that the combination of our and traditional features may support the creation of a discriminative feature set for efficient prediction of hot spots in protein interfaces.
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