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 ligand 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. Here, we establish and apply an integrated pipeline that prioritizes candidate molecules through structure-guided computation and zebrafish-based functional profiling. We introduce Rosetta Engine for Anchoring Ligands with a Motif (“REAL-M”), a 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, 28 of 30 predicted antagonists significantly blocked binding of the cognate peptide agonist in a cell-based reporter assay, including six chemically diverse molecules with efficacy comparable to preexisting antagonists. Three of these six significantly mitigated hypocretin-induced larval zebrafish hyperactivity. Secondary testing with a zebrafish hcrtr2 null mutant ensured that behavioral phenotypes were on target, in contrast to off-target phenotypes observed with a clinically approved antagonist. This pipeline is readily adaptable to the thousands of zebrafish proteins with highly conserved binding pockets.