ArticleeLife2015
Contacts-based prediction of binding affinity in protein-protein complexes.
Article in eLife, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 335 papers.
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Who cites it
335 citing papers in PubMed.
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- Comparative structural analysis of protein complexes with SPICE.Nucleic acids research · 2026Article
- Subtractive Proteomics and Immunoinformatics-Guided Design of a Multi-Epitope Vaccine Against Aspergillus fumigatus.Probiotics and antimicrobial proteins · 2026Article
- Hybrid Approach to Protein-Protein Complex Affinity Prediction Based on Language Models and Molecular Dynamics.International journal of molecular sciences · 2026Article
- Sustainable management of whitefly (Bemisia tabaci) in vegetables using entomopathogenic fungi, Isaria farinosa: Characterization, efficacy, insecticide compatibility with molecular docking insights.World journal of microbiology & biotechnology · 2026Article
- Identifying potential binding sites for complex formation between Tyrosyl-DNA phosphodiesterase 1 and poly [ADP-ribose] polymerase 1.Physical chemistry chemical physics : PCCP · 2026Article
- Thermodynamic variations at the HPV E1-E2 interface correlate with clinical risk groups: an in-silico analysis.Virology journal · 2026Article
- Catalytic Efficiency for the Chemoenzymatic Synthesis of Core Human Milk Oligosaccharides and Analogs via Sugar Oxazolines.Journal of agricultural and food chemistry · 2026Article
- Immunoinformatics-based design of artificial chimeric proteins as universal vaccine candidates against foot-and-mouth disease virus serotypes A, O, and SAT2.Scientific reports · 2026Article
275 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Almost all critical functions in cells rely on specific protein-protein interactions. Understanding these is therefore crucial in the investigation of biological systems. Despite all past efforts, we still lack a thorough understanding of the energetics of association of proteins. Here, we introduce a new and simple approach to predict binding affinity based on functional and structural features of the biological system, namely the network of interfacial contacts. We assess its performance against a protein-protein binding affinity benchmark and show that both experimental methods used for affinity measurements and conformational changes have a strong impact on prediction accuracy. Using a subset of complexes with reliable experimental binding affinities and combining our contacts and contact-types-based model with recent observations on the role of the non-interacting surface in protein-protein interactions, we reach a high prediction accuracy for such a diverse dataset outperforming all other tested methods.
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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 OpenQuestion graph.