Prioritizing Neuroactive Ligands Using Motif-Guided Virtual Discovery and Zebrafish Profiling

Abstract

Virtual screening of ultra-large chemical libraries is a highly effective strategy for early-stage drug discovery. However, these pipelines often yield thousands of molecules that pass computational filters, and in silico-derived interaction energies do not consistently predict experimental efficacy. Furthermore, many high-affinity hits do not necessarily function effectively in an organism with tissues, barriers, and extensive off-target possibilities. A major hurdle in drug discovery is the prioritization of top candidates for rodent testing. Here, we introduce Rosetta Engine for Anchoring Ligands with a Motif ("REAL-M"), a novel computational screening algorithm that uses structural interaction data from the Protein Data Bank (PDB) to guide ligand placement and selection. Using the hypocretin receptor as a test case for this computational pipeline, 28 of 30 predicted antagonists significantly blocked binding of the cognate peptide agonist in a PRESTO-Tango cell-based reporter assay, including six chemically diverse molecules with comparable efficacy to preexisting antagonists. Three of the six molecules significantly mitigated hypocretin-induced larval zebrafish hyperactivity. Secondary testing with a zebrafish hcrtr2 null mutant ensured that behavioral phenotypes were not due to off-target interactions, which we did observe with preexisting antagonists. This pipeline is readily adaptable to the thousands of zebrafish proteins with highly conserved binding pockets.