Biophysical prediction of protein–peptide interactions and signaling networks using machine learning

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. Protein–peptide interactions that underpin cell signaling are accurately predicted by wedding the strengths of machine learning with the interpretability of biophysical theory, facilitating detailed mechanistic analyses at the proteome scale.