Discovery and Validation of Novel Peptide Agonists for G-protein-coupled Receptors

Iris Hecht,Dvir Dahary,Yossi Cohen,Dani Eshel,Eyal Gofer,Amir Toporik,Galit Rotman,Zurit Levine,Avi Rosenberg,Michal Ayalon Soffer,Ronen Shemesh,Yossef Kliger,Assaf Wool

doi:10.1074/jbc.m805181200

Abstract

G-protein-coupled receptors (GPCRs) represent an important group of targets for pharmaceutical therapeutics. The completion of the human genome revealed a large number of putative GPCRs. However, the identification of their natural ligands, and especially peptides, suffers from low discovery rates, thus impeding development of therapeutics based on these potential drug targets. We describe the discovery of novel GPCR ligands encrypted in the human proteome. Hundreds of potential peptide ligands were predicted by machine learning algorithms. In vitro screening of selected 33 peptides on a set of 152 GPCRs, including a group of designated orphan receptors, was conducted by intracellular calcium measurements and cAMP assays. The screening revealed eight novel peptides as potential agonists that specifically activated six different receptors in a dose-dependent manner. Most of the peptides showed distinct stimulatory patterns targeted at designated and orphan GPCRs. Further analysis demonstrated a significant in vivo effect for one of the peptides in a mouse inflammation model.

Highlights

G-protein-coupled receptors (GPCRs)2 represent a large group of receptors that are directly involved in cellular signaling networks and are considered to be an important family of targets for pharmacological intervention
We describe the discovery of novel GPCR ligands encrypted in the human proteome
Bioinformatics can serve as a powerful tool that provides reliable predictive measures to select for the high potential candidates and provide a spotlight pointed at potential new candidates for experimental discovery, thereby enabling higher success rates in identification of novel GPCR ligands [11]

Summary

EXPERIMENTAL PROCEDURES

Data Set Preparation for the Proteolytic Site Predictor—All mammalian proteins [28,780] were downloaded from the Swiss Protein Database (release 43) [12]. Based on these numeric parameters the classifying program was trained to distinguish between the learning set of known ligands (true set) and the negative (false) set This program was used to give each predicted peptide a score signifying the likelihood of being a GPCR-activating ligand. The selection of GPCR-activating peptides was based on the following criteria: expression profile and tissue specificity of the precursor, with relation to the receptor; comparison of the cleavage and GPCR classifier scores of the candidate peptides and their mouse orthologs; conservation of the sequence and cleavage sites of the peptide in the precursor proteins of all orthologs; position of the peptide within the precursor with respect to known domains and features (as a negative rule); and the number of cysteine residues and disulfide bridge annotations.

RESULTS

Protein name of precursor

GPCRs activation and specificity by screened peptides

Other GPCRsa activated by same positive control

DISCUSSION

Eshel and Yossi Cohen