The relaxin family peptide receptor-3 (RXFP3) is a G protein-coupled receptor (GPCR) which, together with its neuropeptide ligand relaxin-3, is primarily expressed in the brain, and forms a system that regulates several physiological processes: food intake, stress responses, arousal, exploratory behaviors, spatial learning and memory, as well as cognition. The therapeutic potential for targeting this system remains, however, largely unexplored due to a lack of RXFP3-selective ligands able to penetrate the blood-brain-barrier (BBB).
Recent advances in neuronal networks has culminated with the release of the very first computational method- AlphaFold, an artificial intelligence algorithm able to predict protein structures with atomic accuracy even in the complete absence of similar structures. Utilising this breakthrough combined with the existing in silico tools such as LigandScout, that allows creating three-dimensional (3D) pharmacophore models from structural data of macromolecule–ligand complexes, we have searched the vast chemical space to identify novel RXFP3 binders. This state-of-the-art approach allowed us to obtain virtual hits with high similarity of pharmacophore features. Compounds were commercially acquired from chemical vendors and subsequently pharmacologically profiled for binding affinity to RXFP3- and the closely related GPCR, RXFP4. Their potential to penetrate the BBB was evaluated using a novel HPLC based method. Compounds which showed binding to RXFP3 were assessed for their agonist or antagonist activity in cell signalling assays.
This integrated computational to experimental workflow has delivered a number of chemically distinct compounds which are BBB permeant and RXFP3 specific and exemplifies a general pipeline for small molecule development against challenging peptide GPCRs. Current efforts are focused on hit-to-lead optimisation and preclinical evaluation of the compounds in animal models of stress-related and cognitive disorders.