ArticleBMC systems biology2018
Hot spot prediction in protein-protein interactions by an ensemble system.
Article in BMC systems biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Untangling the Complexity of Binding Across Biomolecular Interfaces with Interaction Network Models.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Article
- DNA sequence analysis landscape: a comprehensive review of DNA sequence analysis task types, databases, datasets, word embedding methods, and language models.Frontiers in medicine · 2025Review
- Polymers for Disrupting Protein-Protein Interactions: Where Are We and Where Should We Be?Biomacromolecules · 2024Review
- PPI-hotspoteLife · 2024Article
- Prediction of hot spots towards drug discovery by protein sequence embedding with 1D convolutional neural network.PloS one · 2023Article
- Oral_voting_transfer: classification of oral microorganisms' function proteins with voting transfer model.Frontiers in microbiology · 2023Article
- Densest subgraph-based methods for protein-protein interaction hot spot prediction.BMC bioinformatics · 2022Article
- iFeatureOmega: an integrative platform for engineering, visualization and analysis of features from molecular sequences, structural and ligand data sets.Nucleic acids research · 2022Article
- Functional inhibition of c-Myc using novel inhibitors identified through "hot spot" targeting.The Journal of biological chemistry · 2022Article
- Overview of methods for characterization and visualization of a protein-protein interaction network in a multi-omics integration context.Frontiers in molecular biosciences · 2022Review
- Glycoprotein attachment with host cell surface receptor ephrin B2 and B3 in mediating entry of nipah and hendra virus: a computational investigation.Journal of chemical sciences (Bangalore, India) · 2022Article
- RF_phage virion: Classification of phage virion proteins with a random forest model.Frontiers in genetics · 2022Article
- A novel lncRNA-protein interaction prediction method based on deep forest with cascade forest structure.Scientific reports · 2021Article
- iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization.Nucleic acids research · 2021Article
- Article
- SPOTONE: Hot Spots on Protein Complexes with Extremely Randomized Trees via Sequence-Only Features.International journal of molecular sciences · 2020Article
- Developing Computational Model to Predict Protein-Protein Interaction Sites Based on the XGBoost Algorithm.International journal of molecular sciences · 2020Article
- A Coarse-Grained Methodology Identifies Intrinsic Mechanisms That Dissociate Interacting Protein Pairs.Frontiers in molecular biosciences · 2020Article
- Semi-supervised prediction of protein interaction sites from unlabeled sample information.BMC bioinformatics · 2019Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHot spot residues are functional sites in protein interaction interfaces. The identification of hot spot residues is time-consuming and laborious using experimental methods. In order to address the issue, many computational methods have been developed to predict hot spot residues. Moreover, most prediction methods are based on structural features, sequence characteristics, and/or other protein features.
resultsThis paper proposed an ensemble learning method to predict hot spot residues that only uses sequence features and the relative accessible surface area of amino acid sequences. In this work, a novel feature selection technique was developed, an auto-correlation function combined with a sliding window technique was applied to obtain the characteristics of amino acid residues in protein sequence, and an ensemble classifier with SVM and KNN base classifiers was built to achieve the best classification performance.
conclusionThe experimental results showed that our model yields the highest F1 score of 0.92 and an MCC value of 0.87 on ASEdb dataset. Compared with other machine learning methods, our model achieves a big improvement in hot spot prediction. AVAILABILITY: http://deeplearner.ahu.edu.cn/web/HotspotEL.htm .
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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 Socratic graph.