Deep Local Analysis deconstructs protein–protein interfaces and accurately estimates binding affinity changes upon mutation
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Abstract
Motivation: The spectacular recent advances in protein and protein complex structure prediction hold promise for reconstructing interactomes at large-scale and residue resolution. Beyond determining the 3D arrangement of interacting partners, modeling approaches should be able to unravel the impact of sequence variations on the strength of the association. Results: In this work, we report on Deep Local Analysis, a novel and efficient deep learning framework that relies on a strikingly simple deconstruction of protein interfaces into small locally oriented residue-centered cubes and on 3D convolutions recognizing patterns within cubes. Merely based on the two cubes associated with the wild-type and the mutant residues, DLA accurately estimates the binding affinity change for the associated complexes. It achieves a Pearson correlation coefficient of 0.735 on about 400 mutations on unseen complexes. Its generalization capability on blind datasets of complexes is higher than the state-of-the-art methods. We show that taking into account the evolutionary constraints on residues contributes to predictions. We also discuss the influence of conformational variability on performance. Beyond the predictive power on the effects of mutations, DLA is a general framework for transferring the knowledge gained from the available non-redundant set of complex protein structures to various tasks. For instance, given a single partially masked cube, it recovers the identity and physicochemical class of the central residue. Given an ensemble of cubes representing an interface, it predicts the function of the complex. Availability and implementation: Source code and models are available at http://gitlab.lcqb.upmc.fr/DLA/DLA.git.
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Cites
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Flagged first, then by citations
- Authors
- Mohseni Behbahani, Y., Laine, E., & Carbone, A.
- Affiliations
- Laboratory of Computational and Quantitative Biology (LCQB), UMR 7238, Sorbonne Université, CNRS, IBPS , Paris 75005, France
- Centre National de la Recherche Scientifique (FR)
- Corresponding author
- Carbone, Alessandra
- ORCID
- Carbone, Alessandra
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2023-06-01
- Volume
- 39
- Issue
- Supplement_1
- Pages
- i544-i552
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC10311296
- OpenAlex
- W4382632548
- Cited by
- 23
- References
- 17
- Citations by year
- 2026: 4 · 2025: 6 · 2024: 6 · 2023: 4 · 2022: 1 · 2012: 2
- FWCI
- 1.78
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- HAL (submittedVersion)
- Funders
- Centre National de la Recherche Scientifique
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- protein-protein interfaces, binding affinity prediction, 3D convolution, protein complex structure, evolutionary constraints, conformational heterogeneity, protein function prediction, protein-protein interaction prediction
- MeSH terms
- Biological Evolution, Mutation, Software
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