ArticleAlgorithms for molecular biology : AMB2015
Algorithmic approaches to protein-protein interaction site prediction.
Article in Algorithms for molecular biology : AMB, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Artificial intelligence driven protein design and sustainable nanomedicine for advanced theranostics.Bioactive materials · 2026Review
- Assessing the functional impact of protein binding site definition.Protein science : a publication of the Protein Society · 2024Article
- A multi-source molecular network representation model for protein-protein interactions prediction.Scientific reports · 2024Article
- 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
- Computational approaches for the design of modulators targeting protein-protein interactions.Expert opinion on drug discovery · 2023Review
- Review
- Complementarity of the residue-level protein function and structure predictions in human proteins.Computational and structural biotechnology journal · 2022Article
- From complete cross-docking to partners identification and binding sites predictions.PLoS computational biology · 2022Article
- CATH functional families predict functional sites in proteins.Bioinformatics (Oxford, England) · 2021Article
- Classification and prediction of protein-protein interaction interface using machine learning algorithm.Scientific reports · 2021Article
- A Deep Learning and XGBoost-Based Method for Predicting Protein-Protein Interaction Sites.Frontiers in genetics · 2021Article
- Evolution of Sequence-based Bioinformatics Tools for Protein-protein Interaction Prediction.Current genomics · 2020Review
- Prediction of Protein-Protein Interaction Sites Using Convolutional Neural Network and Improved Data Sets.International journal of molecular sciences · 2020Article
- Article
- Protein-protein interaction sites prediction by ensemble random forests with synthetic minority oversampling technique.Bioinformatics (Oxford, England) · 2019Article
- Interactome of Glyceraldehyde-3-Phosphate Dehydrogenase Points to the Existence of Metabolons inFrontiers in microbiology · 2019Article
- Learning protein binding affinity using privileged information.BMC bioinformatics · 2018Article
- Pattern to Knowledge: Deep Knowledge-Directed Machine Learning for Residue-Residue Interaction Prediction.Scientific reports · 2018Article
- Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery.Molecules (Basel, Switzerland) · 2018Review
- Protein-protein interaction specificity is captured by contact preferences and interface composition.Bioinformatics (Oxford, England) · 2018Article
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Interaction sites on protein surfaces mediate virtually all biological activities, and their identification holds promise for disease treatment and drug design. Novel algorithmic approaches for the prediction of these sites have been produced at a rapid rate, and the field has seen significant advancement over the past decade. However, the most current methods have not yet been reviewed in a systematic and comprehensive fashion. Herein, we describe the intricacies of the biological theory, datasets, and features required for modern protein-protein interaction site (PPIS) prediction, and present an integrative analysis of the state-of-the-art algorithms and their performance. First, the major sources of data used by predictors are reviewed, including training sets, evaluation sets, and methods for their procurement. Then, the features employed and their importance in the biological characterization of PPISs are explored. This is followed by a discussion of the methodologies adopted in contemporary prediction programs, as well as their relative performance on the datasets most recently used for evaluation. In addition, the potential utility that PPIS identification holds for rational drug design, hotspot prediction, and computational molecular docking is described. Finally, an analysis of the most promising areas for future development of the field is presented.
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