ArticleInternational journal of molecular sciences2016
A Machine Learning Approach for Hot-Spot Detection at Protein-Protein Interfaces.
Article in International journal of molecular sciences, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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24 citing papers in PubMed.
- Molecular mechanism of complement interference by Streptococcus pyogenes nuclease A.Communications biology · 2026Article
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- DensePPI-2: a bio-inspired update for sequence-based PPI prediction leveraging mutation rates.Briefings in bioinformatics · 2025Article
- Unraveling the molecular basis of snake venom nerve growth factor: human TrkA recognition through molecular dynamics simulation and comparison with human nerve growth factor.Frontiers in bioinformatics · 2025Article
- PPI-hotspoteLife · 2024Article
- Distinct or Overlapping Areas of Mitochondrial Thioredoxin 2 May Be Used for Its Covalent and Strong Non-Covalent Interactions with Protein Ligands.Antioxidants (Basel, Switzerland) · 2023Article
- Graph Neural Network for Protein-Protein Interaction Prediction: A Comparative Study.Molecules (Basel, Switzerland) · 2022Article
- Prediction of protein-protein interaction using graph neural networks.Scientific reports · 2022Article
- Native or Non-Native Protein-Protein Docking Models? Molecular Dynamics to the Rescue.Journal of chemical theory and computation · 2021Article
- MENSAdb: a thorough structural analysis of membrane protein dimers.Database : the journal of biological databases and curation · 2021Article
- The UBA domain of conjugating enzyme Ubc1/Ube2K facilitates assembly of K48/K63-branched ubiquitin chains.The EMBO journal · 2021Article
- Structural Biology in the Clouds: The WeNMR-EOSC Ecosystem.Frontiers in molecular biosciences · 2021Article
- Applying MAPPs Assays to Assess Drug Immunogenicity.Frontiers in immunology · 2020Review
- Machine Learning as an Effective Method for Identifying True Single Nucleotide Polymorphisms in Polyploid Plants.The plant genome · 2019Article
- SKEMPI 2.0: an updated benchmark of changes in protein-protein binding energy, kinetics and thermodynamics upon mutation.Bioinformatics (Oxford, England) · 2019Article
- Discovery of novel helix binding sites at protein-protein interfaces.Computational and structural biotechnology journal · 2019Article
- dbMPIKT: a database of kinetic and thermodynamic mutant protein interactions.BMC bioinformatics · 2018Article
- Machine Learning Approaches for Protein⁻Protein Interaction Hot Spot Prediction: Progress and Comparative Assessment.Molecules (Basel, Switzerland) · 2018Review
- Exploring the potential of 3D Zernike descriptors and SVM for protein-protein interface prediction.BMC bioinformatics · 2018Article
- 3DCONS-DB: A Database of Position-Specific Scoring Matrices in Protein Structures.Molecules (Basel, Switzerland) · 2017Article
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9 authors.
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Abstract
Understanding protein-protein interactions is a key challenge in biochemistry. In this work, we describe a more accurate methodology to predict Hot-Spots (HS) in protein-protein interfaces from their native complex structure compared to previous published Machine Learning (ML) techniques. Our model is trained on a large number of complexes and on a significantly larger number of different structural- and evolutionary sequence-based features. In particular, we added interface size, type of interaction between residues at the interface of the complex, number of different types of residues at the interface and the Position-Specific Scoring Matrix (PSSM), for a total of 79 features. We used twenty-seven algorithms from a simple linear-based function to support-vector machine models with different cost functions. The best model was achieved by the use of the conditional inference random forest (c-forest) algorithm with a dataset pre-processed by the normalization of features and with up-sampling of the minor class. The method has an overall accuracy of 0.80, an F1-score of 0.73, a sensitivity of 0.76 and a specificity of 0.82 for the independent test set.
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