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Funneling modulatory peptide design with generative models: Discovery and characterization of disruptors of calcineurin protein-protein interactions

Fig 3

Generative modeling of PxIxIT binding motifs.

(a) Schematic view of the generative approach. A “smooth” probability distribution over the whole sequence space is learnt from a limited number of samples. Unseen sequences with high probability are potential novel binders, whereas regions with low probability are likely non-functional proteins. (b) Graphical depiction of the cRBM model, the parametric form chosen. The visible layer corresponds to the aligned sequence; each visible unit contributes a site-specific term g_i(s_i) to the log-likelihood. The hidden (representation) layer corresponds to unobserved hidden units, each of which contributes an additional term to the log-likelihood function, defined as a linear projection through a sparse tensor followed by a trainable, strictly convex non-linearity. (c,d) cRBM-predicted mutational landscapes for the NFATc2 and AKAP79 peptides. Red, white and blue entries correspond respectively to beneficial, neutral and deleterious mutations. (e) Comparison between cRBM-predicted mutational landscapes and deep mutational scans of change in binding affinity measured by Nguyen et al. Four DMS were performed taking as wild type the PVIVIT, PKIVIT, NFATc2 and AKAP79 peptides. Spearman correlation coefficients are annotated. (f,g,h) Selected examples of sequence motifs learnt by the cRBM (f), together with their activity distribution (g) and top-activating sequences. Motif 1 is gene-specific, whereas motifs 2 and 3 are shared by multiple genes.

doi: https://doi.org/10.1371/journal.pcbi.1010874.g003