Biophysical prediction of protein–peptide interactions and signaling networks using machine learning
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In mammalian cells, much of signal transduction is mediated by weak protein-protein interactions between globular peptide-binding domains (PBDs) and unstructured peptidic motifs in partner proteins. The number and diversity of these PBDs (over 1,800 are known), their low binding affinities and the sensitivity of binding properties to minor sequence variation represent a substantial challenge to experimental and computational analysis of PBD specificity and the networks PBDs create. Here, we introduce a bespoke machine-learning approach, hierarchical statistical mechanical modeling (HSM), capable of accurately predicting the affinities of PBD-peptide interactions across multiple protein families. By synthesizing biophysical priors within a modern machine-learning framework, HSM outperforms existing computational methods and high-throughput experimental assays. HSM models are interpretable in familiar biophysical terms at three spatial scales: the energetics of protein-peptide binding, the multidentate organization of protein-protein interactions and the global architecture of signaling networks.
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AI modeling paradigms have been used successfully in the biomolecular realm, for example, to predict 3D molecular structures from sequences and interactions between different molecular components.
“Such modeling paradigms have been used successfully in the biomolecular realm, for example, to predict three-dimensional (3D) molecular structures from sequences32–34 and interactions between different molecular components.35–38”
From How to build the virtual cell with artificial intelligence: Priorities and opportunities (2024) · cites this paper as [36]
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Protein Interaction Prediction with Artificial Intelligence
Li, J., & Han, Y. (2025) · doi:10.1002/9783527842346.ch07
- Authors
- Cunningham, J. M., Rogers, J. R., Koytiger, G., Sorger, P. K., & AlQuraishi, M.
- Affiliations
- Harvard University (US)
- Corresponding author
- Sorger, Peter K.
- AlQuraishi, Mohammed
- Venue
- Nature Methods
- Published
- 2020-01-06
- Volume
- 17
- Issue
- 2
- Pages
- 175-183
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC7004877
- OpenAlex
- W2997482957
- Cited by
- 108
- References
- 55
- Citations by year
- 2026: 12 · 2025: 17 · 2024: 14 · 2023: 14 · 2022: 21 · 2021: 20 · 2020: 10
- FWCI
- 5.27
- Type
- journal article
- Language
- en
- Update notices
- correction (2026-05-21)
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Nature Methods (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (acceptedVersion)
- Funders
- United States Department of Defense | Defense Advanced Research Projects Agency (W911NF-14-1-0397)
- U.S. Department of Health & Human Services | NIH | National Cancer Institute (U54-CA225088)
- U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (P50-GM107618)
- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- protein-peptide interactions, peptide-binding domain, protein-protein interaction, signal transduction, signaling networks, binding affinity prediction, model interpretability
- MeSH terms
- Machine Learning, Humans, Peptides, Protein Binding, Proteins, Reproducibility of Results, Signal Transduction, src Homology Domains, Biophysical Phenomena
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