Funneling modulatory peptide design with generative models: Discovery and characterization of disruptors of calcineurin protein-protein interactions
Fig 4
Medium-throughput filtering by structural modeling and microarray screening.
(a) Depiction of the structural modeling protocol: after alignment to the known PxIxIT binding site, an efficient flexible backbone structure refinement algorithm is applied to estimate the docking energy. (b) Histogram of docking energy scores for the generated peptides and selected controls (lower is better; normalized to zero mean and unit variance). (c) Coefficients of the equivalent single-site model fitted by sparse linear regression, shown in weight logo representation. At each position, the height of the letter is proportional to the corresponding coefficient of the regression; residues with large negative coefficients (e.g. hydrophobic residues at the motif locations) contribute favorably to the docking score. Colors indicate physical property (black = hydrophobic, red = negatively charged, etc.). (d) Per-gene distribution of docking scores across natural fragments (lower is better). The docking protocol qualitatively discriminates between obligate and transient interactions. (e) Overview of the microarray screening. Peptides are printed on the chip (two circles per peptide). After pouring of Cn and subsequent washing, fluorescent-tagged, a Cn-targeting antibody is overlaid and an image is taken. Fluorescent spots indicate strong Cn binders. (f) Scatter plot of the sequence likelihood (normalized by length, higher is better) against fluorescence level (higher is better, see Methods for details of the data analysis).