Predicting the Effect of Mutations on Protein-Protein Binding Interactions through Structure-Based Interface Profiles
Open accessAbstract
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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The conservation measure used in those ΔΔG predictors is structure-based and relies on the availability of structural homologs.
“However, since the conservation measure they used is structure‐based, relying on the availability of structural homologs,13, 19”
From iSEE: Interface structure, evolution, and energy-based machine learning predictor of binding affinity changes upon mutations (2019) · cites this paper as [13]
The application and prediction of those structure-based conservation ΔΔG predictors are largely limited by the availability and number of structural homologs.
“the application and prediction of these ΔΔG predictors are largely limited by the availability and the number of such homologs.”
From iSEE: Interface structure, evolution, and energy-based machine learning predictor of binding affinity changes upon mutations (2019) · cites this paper as [13]
Interface conservation has been used in several of the best performing ΔΔG predictors.
“Interface conversation has been used in several of the best performing ΔΔG predictors.13, 19”
From iSEE: Interface structure, evolution, and energy-based machine learning predictor of binding affinity changes upon mutations (2019) · cites this paper as [13]
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- Authors
- Brender, J. R., & Zhang, Y.
- Affiliations
- University of Michigan (US)
- Corresponding author
- Zhang, Yang
- Venue
- PLOS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2015-10-27
- Volume
- 11
- Issue
- 10
- Pages
- e1004494
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC4624718
- OpenAlex
- W1823111029
- Cited by
- 140
- References
- 67
- Citations by year
- 2026: 7 · 2025: 6 · 2024: 7 · 2023: 12 · 2022: 12 · 2021: 12 · 2020: 17 · 2019: 19 · 2018: 15 · 2017: 18 · 2016: 15
- FWCI
- 6.94
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institute of General Medical Sciences (GM-083107, R01GM116960, R01GM083107)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- protein-protein binding affinity, protein-protein complex, evolutionary profiles, random forest, coarse-grained potentials, pathogenic variants
- MeSH terms
- Amino Acid Sequence, Binding Sites, Models, Chemical, Models, Genetic, Models, Molecular, Molecular Sequence Data, Mutation, Protein Binding, Protein Conformation, Proteins, Structure-Activity Relationship, Sequence Analysis, Protein, Protein Interaction Mapping
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