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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The domain–peptide and PPI predictions are made available through a custom website (https://ProteinPeptide.io). The protein–peptide interaction data are also made available in figshare with the identifiers https://doi.org/10.6084/m9.figshare.10084745. Data used in training the model are available as Supplementary Dataset 2.
All code and data used for training and testing HSM are available in a public repository at https://github.com/aqlaboratory/hsm.
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This work was funded by NIH grants (nos. U54-CA225088 and P50-GM107618) and by DARPA/DOD (grant no. W911NF-14-1-0397) to P.K.S.
P.K.S. is a member of the SAB or Board of Directors of Merrimack Pharmaceutical, Glencoe Software, Applied Biomath and RareCyte Inc. and has equity in these companies. P.K.S. declares that none of these relationships are directly or indirectly related to the content of this manuscript.
Peer review information Rita Strack was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Figs. 1–7, Tables 1–5 and Notes 1 and 2.
Domain sequences and multiple sequence alignments used in both HSM/D and HSM/P.
Raw domain-peptide training data used for training HSM/D.
Potential peptidic sites used in predictions generated with HSM/P.
Assessment of HSM/D relative to other domain models. Contains source data for Fig. 2a and Supplementary Fig. 3.
PyMOL structural data associated with analysing HSM/D inferred energy profiles. Includes source data for Figs. 4 and 5 and Supplementary Figs. 5 and 6.
Assessment of HSM/P. Contains source data for Figs. 2b, 3 and 6 and Supplementary Fig. 7.
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Cunningham, J.M., Koytiger, G., Sorger, P.K. et al. Biophysical prediction of protein–peptide interactions and signaling networks using machine learning. Nat Methods 17, 175–183 (2020). https://doi.org/10.1038/s41592-019-0687-1
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