Abstract

Two-thirds of human hormones act through ~800 G protein-coupled receptors (GPCRs). The vast majority (71%) of these hormones are peptides or proteins, which also account for an increasing share of drugs. The study of peptide-receptor recognition is thus essential for understanding physiology, pathology and for drug design.

This project aims to solve the modeling problem of peptide-receptor recognition by leveraging machine learning methods and unique data from the field hub GPCRdb. I will build predictive graph neural network models representing residue interaction networks across the receptor-peptide interface. The models will utilize attention-based transformer and LSTM architectures which have shown great promise in drug-target interaction prediction and de novo-drug design. The models will be trained on a unique data representation, storing data for individual residues rather than the overall protein. This will allow peptide data to be inferred across conserved residues in different receptors – enabling use on receptors not targetable with classical methods.

The trained models will be used in three applied aims to: (1) discover peptide hormones by matching the library of predicted physiological peptide hormones to their cognate receptors with unknown physiological ligand and function; (2) identify peptide probes by matching pentameric peptide library to understudied and drug target receptors; and (3) holistically engineer probes for those receptors residue-by-residue. The in silico discovered probes will be tested in vitro by pharmacological collaborators. In all, this will let me discover novel hormones and engineer new probes, enabling functional characterization of understudied receptors that cannot be targeted with current techniques.