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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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“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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Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
DeLong, E. R., DeLong, D. M., & Clarke-Pearson, D. L. (1988) · Biometrics · doi:10.2307/2531595 · Cited by 22,649
PhosphoSitePlus, 2014: mutations, PTMs and recalibrations
Hornbeck, P. V., Zhang, B., Murray, B., Kornhauser, J. M., Latham, V., & Skrzypek, E. (2015) · Nucleic Acids Research · doi:10.1093/nar/gku1267 · Cited by 3,411
Towards a proteome-scale map of the human protein–protein interaction network
Rual, J.-F. M., Venkatesan, K., Hao, T., Hirozane-Kishikawa, T., Dricot, A., Li, N., Berriz, G. F., Gibbons, F. D., Dreze, M., Ayivi-Guedehoussou, N., et al. (2005) · Nature · doi:10.1038/nature04209 · Cited by 2,904
The MIntAct project—IntAct as a common curation platform for 11 molecular interaction databases
Orchard, S. E., Ammari, M. G., Aranda, B., Breuza, L., Briganti, L., Broackes-Carter, F. C., Campbell, N. H., Chavali, G. B., Chen, C. C. L., del‐Toro, N., et al. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt1115 · Cited by 1,845
Architecture of the human interactome defines protein communities and disease networks
Huttlin, E. L., Bruckner, R. J., Paulo, J. A., Cannon, J. R., Ting, L., Baltier, K., Colby, G., Gebreab, F., Gygi, M. P., Parzen, H., et al. (2017) · Nature · doi:10.1038/nature22366 · Cited by 1,437
The BioPlex network: a systematic exploration of the human interactome
Huttlin, E. L., Ting, L., Bruckner, R. J., Gebreab, F., Gygi, M. P., Szpyt, J., Tam, S., Zarraga, G., Colby, G., Baltier, K., et al. (2015) · Cell · doi:10.1016/j.cell.2015.06.043 · Cited by 1,425
A Proteome-Scale Map of the Human Interactome Network
Rolland, T., Taşan, M., Charloteaux, B., Pevzner, S. J., Zhong, Q., Sahni, N., Yi, S. S., Lemmens, I. H., Fontanillo, C., Mosca, R., et al. (2014) · Cell · doi:10.1016/j.cell.2014.10.050 · Cited by 1,381
A Human Interactome in Three Quantitative Dimensions Organized by Stoichiometries and Abundances
Hein, M. Y., Hubner, N. C., Poser, I., Cox, J., Nagaraj, N., Toyoda, Y., Gak, I. A., Weisswange, I., Mansfeld, J., Buchholz, F., et al. (2015) · Cell · doi:10.1016/j.cell.2015.09.053 · Cited by 1,270
Deep Learning of Representations for Unsupervised and Transfer Learning.
Bengio, Y. (2011) · Cited by 1,080
Research works printed but not linked: 10
- Cesareni, G., Gimona, M., Sudol, M. & Yaffe, M. Modular Protein Domains (John Wiley & Sons, 2006).
- Goldstein, H., Poole Jr., C. P. & Safko, J. L. Classical Mechanics (Addison-Wesley, 2001).
- Kundu, K. et al. Semi-supervised prediction of SH2-peptide interactions from imbalanced high-throughput data. PLoS One 8, e62732 (2013). · DOI
- Kundu, K. & Backofen, R. Cluster based prediction of PDZ-peptide interactions. BMC Genomics 15, S5 (2014). · DOI
- Horn, H. et al. KinomeXplorer: an integrated platform for kinome biology studies. Nat. Methods 11, 603–604 (2014). · DOI
- Goodfellow, I, Bengio, Y. & Courville, A. Deep Learning. (MIT Press, 2016).
- Snell, J., Swersky, K. & Zemel, R. S. in Advances in Neural Information Processing Systems Vol. 30 (eds Guyon, I. et al.) 4077–4087 (Curran Associates, Inc., 2017).
- Shah, N. H., Lobel, M., Weiss, A. & Kuriyan, J. Fine-tuning of substrate preferences of the Src-family kinase Lck revealed through a high-throughput specificity screen. eLife 7, e35190 (2018). · DOI
- Kundu, K., Costa, F. & Backofen, R. A graph kernel approach for alignment-free domain-peptide interaction prediction with an application to human SH3 domains. Bioinformatics 29, i335–i343 (2013). · DOI
- Hu, F. Efficient and high quality force-directed graph. Mathemtica J. 10, 37–71 (2005).
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“To predict these PBDs across multiple protein families bespoke ML tool was developed, known as hierarchical statistical mechanical modeling (HSMM) [152].”
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Lei, Y., Li, S., Liu, Z., Wan, F., Tian, T., Li, S., Zhao, D. Y., & Zeng, J. (2021) · Nature Communications · doi:10.1038/s41467-021-25772-4 · Cited by 231
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Lukinović, V., & Biggar, K. K. (2021) · Comparative Biochemistry and Physiology Part B Biochemistry and Molecular Biology · doi:10.1016/j.cbpb.2021.110616 · Cited by 1
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Zhan, X., Zhai, S., Liu, T., Lin, S., Bi, T., Zhu, B., & Siu, S. W. I. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.12.699173 · Cited by 1
Cheng, F., Zhao, J., Wang, Y., Lü, W., Liu, Z., Zhou, Y., Martin, W., Wang, R., Huang, J. Y., Hao, T., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.09.18.302588
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Dionne, U., Bourgault, É., Dubé, A. K., Bradley, D. T. C., Chartier, F. J.-M., Dandage, R., Dibyachintan, S., Després, P. C., Gish, G. D., Lambert, J., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.18.103002
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Kim, S., Jana, B., Go, E. M., Lee, J. E., Jin, S., Sim, Y., Son, S., Kim, D., Kim, C., Kwak, S. K., et al. (2021) · Research Square · doi:10.21203/rs.3.rs-86561/v1
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Moarefian, M., Capossela, A. M., Eom, R., & Aran, K. (2022) · GEN Biotechnology · doi:10.1089/genbio.2022.0014
Mondal, A., Chang, L., & Pérez, A. (2022) · QRB Discovery · doi:10.1017/qrd.2022.14.pr4
Mondal, A., Chang, L., & Pérez, A. (2022) · QRB Discovery · doi:10.1017/qrd.2022.14.pr5
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Mondal, A., Chang, L., & Pérez, A. (2022) · QRB Discovery · doi:10.1017/qrd.2022.14.pr2
Mondal, A., Chang, L., & Pérez, A. (2022) · QRB Discovery · doi:10.1017/qrd.2022.14.pr3
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Rechciński, T. (2023) · Advances in biochemistry in health and disease · doi:10.1007/978-3-031-14952-8_7
Mezghrani, A., Simon, J., Reys, V., & Labesse, G. (2024) · Methods in molecular biology · doi:10.1007/978-1-0716-4007-4_14
Zhang, H., Zhang, X., Si, Y., Li, H., Han, J., Yang, C., & Yang, H. (2024) · Sensing and Bio-Sensing Research · doi:10.1016/j.sbsr.2024.100697
Callahan, T. J., Shi, J., Cheng, K. J., Sauer, M. A., Pogorelov, T. V., & Capponi, S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.18.670971
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Li, J., & Han, Y. (2025) · doi:10.1002/9783527842346.ch07
Metadata
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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