ReviewMolecules (Basel, Switzerland)2018
Machine Learning Approaches for Protein⁻Protein Interaction Hot Spot Prediction: Progress and Comparative Assessment.
Review in Molecules (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Accurate protein-protein interactions modeling through physics-informed geometric invariant learning.Communications biology · 2026Article
- TPepPro: a deep learning model for predicting peptide-protein interactions.Bioinformatics (Oxford, England) · 2024Article
- Polymers for Disrupting Protein-Protein Interactions: Where Are We and Where Should We Be?Biomacromolecules · 2024Review
- Open-Source Machine Learning in Computational Chemistry.Journal of chemical information and modeling · 2023Review
- Toward real-world automated antibody design with combinatorial Bayesian optimization.Cell reports methods · 2023Article
- Phenotype analysis of cultivation processes via unsupervised machine learning: Demonstration forEngineering in life sciences · 2022Article
- A stepwise mutagenesis approach using histidine and acidic amino acid to engineer highly pH-dependent protein switches.3 Biotech · 2022Article
- Modelling eNvironment for Isoforms (MoNvIso): A general platform to predict structural determinants of protein isoforms in genetic diseases.Frontiers in chemistry · 2022Article
- Editorial: Artificial Intelligence in Bioinformatics and Drug Repurposing: Methods and Applications.Frontiers in genetics · 2022Article
- Machine learning prediction of antiviral-HPV protein interactions for anti-HPV pharmacotherapy.Scientific reports · 2021Article
- Nabe: an energetic database of amino acid mutations in protein-nucleic acid binding interfaces.Database : the journal of biological databases and curation · 2021Article
- Biomolecular Modeling and Simulation: A Prospering Multidisciplinary Field.Annual review of biophysics · 2021Article
- Structural Characterization of Receptor-Receptor Interactions in the Allosteric Modulation of G Protein-Coupled Receptor (GPCR) Dimers.International journal of molecular sciences · 2021Review
- Bringing New Methods to the Seed Proteomics Platform: Challenges and Perspectives.International journal of molecular sciences · 2020Review
- Rapid prediction of crucial hotspot interactions for icosahedral viral capsid self-assembly by energy landscape atlasing validated by mutagenesis.PLoS computational biology · 2020Article
- Protein-Protein Interactions Efficiently Modeled by Residue Cluster Classes.International journal of molecular sciences · 2020Article
- Article
- A Coarse-Grained Methodology Identifies Intrinsic Mechanisms That Dissociate Interacting Protein Pairs.Frontiers in molecular biosciences · 2020Article
- The Molecular Basis of JAZ-MYC Coupling, a Protein-Protein Interface Essential for Plant Response to Stressors.Frontiers in plant science · 2020Article
- Prediction of Anticancer Peptides Using a Low-Dimensional Feature Model.Frontiers in bioengineering and biotechnology · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Hot spots are the subset of interface residues that account for most of the binding free energy, and they play essential roles in the stability of protein binding. Effectively identifying which specific interface residues of protein⁻protein complexes form the hot spots is critical for understanding the principles of protein interactions, and it has broad application prospects in protein design and drug development. Experimental methods like alanine scanning mutagenesis are labor-intensive and time-consuming. At present, the experimentally measured hot spots are very limited. Hence, the use of computational approaches to predicting hot spots is becoming increasingly important. Here, we describe the basic concepts and recent advances of machine learning applications in inferring the protein⁻protein interaction hot spots, and assess the performance of widely used features, machine learning algorithms, and existing state-of-the-art approaches. We also discuss the challenges and future directions in the prediction of hot spots.
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Registered trials
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 OpenQuestion graph.