Dataset S4-6: Leveraging co-evolutionary insights and AI-based structural modeling to unravel receptor-peptide ligand-binding mechanisms

Significance statement: This study presents proof-of-concept for a rapid and inexpensive alternative to classical structure-based approaches for resolving ligand-receptor binding mechanisms. It relies on a multilayered bioinformatic approach that leverages genomic data across diverse species in combination with AI-based structural modeling to identify true ligand and receptor homologues, and subsequently predict their binding mechanisms. In silico findings were validated by multiple experimental approaches, which investigated the effect of amino acid changes in the proposed binding pockets on ligand-binding, complex formation with a co-receptor essential for downstream signaling, and activation of downstream signaling. Our analysis combining evolutionary insights, in silico modeling and functional validation provides a framework for structure-function analysis of other peptide-receptor pairs, which could be easily implemented by most laboratories. Zip file contains: Dataset S4: Plasmid maps of constructs used in this study. Dataset S5: AFM and AF3 predicted structures (.pdb) and AFM confidence metrics (.pae) Dataset S6: Unedited files (.tiff) of co-IP and western blotting.