ArticlePLoS computational biology2015
Predicting the Effect of Mutations on Protein-Protein Binding Interactions through Structure-Based Interface Profiles.
Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers.
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Who cites it
66 citing papers in PubMed, 145 citations in OpenAlex.
- DeepPNI: a language- and graph-based model for mutation-driven protein-nucleic acid binding energetics.Nucleic acids research · 2026Article
- How Binding Affinity and Binding Specificity Map to Sequence Space.Journal of molecular evolution · 2026Article
- A structural machine learning approach for rapid prediction of thermodynamically destabilizing tyrosine phosphorylations.Cell reports methods · 2025Article
- Decoding the effects of mutation on protein interactions using machine learning.Biophysics reviews · 2025Review
- FoldX force field revisited, an improved version.Bioinformatics (Oxford, England) · 2025Article
- MPA-MutPred: a novel strategy for accurately predicting the binding affinity change upon mutation in membrane protein complexes.Briefings in bioinformatics · 2024Article
- Diverging co-translational protein complex assembly pathways are governed by interface energy distribution.Nature communications · 2024Article
- Recognition of polymorphic Csd proteins determines sex in the honeybee.Science advances · 2023Article
- Mutations observed in somatic evolution reveal underlying gene mechanisms.Communications biology · 2023Article
- Electrostatic complementarity at the interface drives transient protein-protein interactions.Scientific reports · 2023Article
- Design of protein-binding peptides with controlled binding affinity: the case of SARS-CoV-2 receptor binding domain and angiotensin-converting enzyme 2 derived peptides.Frontiers in molecular biosciences · 2023Article
- SARS-CoV-2 Spike mutations modify the interaction between virus Spike and human ACE2 receptors.Biochemical and biophysical research communications · 2022Article
- Missense variants in the TNFA epitopes and their effects on interaction with therapeutic antibodies-in silico analysis.Journal, genetic engineering & biotechnology · 2022Article
- Biomolecular Topology: Modelling and Analysis.Acta mathematica Sinica, English series · 2022Article
- A novel computational strategy for defining the minimal protein molecular surface representation.PloS one · 2022Article
- Computational Alanine Scanning Reveals Common Features of TCR/pMHC Recognition in HLA-DQ8-Associated Celiac Disease.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Computational Assessment of Protein-protein Binding Affinity by Reversely Engineering the Energetics in Protein Complexes.Genomics, proteomics & bioinformatics · 2021Article
- The Potential Role of Human NME1 in Neuronal Differentiation of Porcine Mesenchymal Stem Cells: Application of NB-hNME1 as a Human NME1 Suppressor.International journal of molecular sciences · 2021Article
- Recent advances in user-friendly computational tools to engineer protein function.Briefings in bioinformatics · 2021Review
- Discovery of New Fusion Inhibitor Peptides against SARS-CoV-2 by Targeting the Spike S2 Subunit.Biomolecules & therapeutics · 2021Article
6 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
The formation of protein-protein complexes is essential for proteins to perform their physiological functions in the cell. Mutations that prevent the proper formation of the correct complexes can have serious consequences for the associated cellular processes. Since experimental determination of protein-protein binding affinity remains difficult when performed on a large scale, computational methods for predicting the consequences of mutations on binding affinity are highly desirable. We show that a scoring function based on interface structure profiles collected from analogous protein-protein interactions in the PDB is a powerful predictor of protein binding affinity changes upon mutation. As a standalone feature, the differences between the interface profile score of the mutant and wild-type proteins has an accuracy equivalent to the best all-atom potentials, despite being two orders of magnitude faster once the profile has been constructed. Due to its unique sensitivity in collecting the evolutionary profiles of analogous binding interactions and the high speed of calculation, the interface profile score has additional advantages as a complementary feature to combine with physics-based potentials for improving the accuracy of composite scoring approaches. By incorporating the sequence-derived and residue-level coarse-grained potentials with the interface structure profile score, a composite model was constructed through the random forest training, which generates a Pearson correlation coefficient >0.8 between the predicted and observed binding free-energy changes upon mutation. This accuracy is comparable to, or outperforms in most cases, the current best methods, but does not require high-resolution full-atomic models of the mutant structures. The binding interface profiling approach should find useful application in human-disease mutation recognition and protein interface design studies.
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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.