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Artificial intelligence is revolutionizing protein structure prediction, providing unprecedented opportunities for drug design. To assess the potential impact on ligand discovery, we compared virtual screens using protein structures generated by the AlphaFold machine learning method and traditional homology modeling. More than 16 million compounds were docked to models of the trace amine-associated receptor 1 (TAAR1), a G protein-coupled receptor of unknown structure and target for treating neuropsychiatric disorders. Sets of 30 and 32 highly ranked compounds from the AlphaFold and homology model screens, respectively, were experimentally evaluated. Of these, 25 were TAAR1 agonists with potencies ranging from 12 to 0.03 mu M. The AlphaFold screen yielded a more than twofold higher hit rate (60%) than the homology model and discovered the most potent agonists. A TAAR1 agonist with a promising selectivity profile and drug-like properties showed physiological and antipsychotic-like effects in wild-type but not in TAAR1 knockout mice. These results demonstrate that AlphaFold structures can accelerate drug discovery.

Receptor activity-modifying proteins (RAMPs) form complexes with G protein-coupled receptors (GPCRs) and may regulate their cellular trafficking and pharmacology. RAMP interactions have been identified for about 50 GPCRs, but only a few GPCR-RAMP complexes have been studied in detail. To elucidate a comprehensive GPCR-RAMP interactome, we created a library of 215 dual epitope-tagged (DuET) GPCRs representing all GPCR subfamilies and coexpressed each GPCR with each of the three RAMPs. Screening the GPCR-RAMP pairs with customized multiplexed suspension bead array (SBA) immunoassays, we identified 122 GPCRs that showed strong evidence for interaction with at least one RAMP. We screened for interactions in three cell lines and found 23 endogenously expressed GPCRs that formed complexes with RAMPs. Mapping the GPCR-RAMP interactome expands the current system-wide functional characterization of RAMP-interacting GPCRs to inform the design of selective therapeutics targeting GPCR-RAMP complexes., QC 20240820

BACKGROUND: Trace amine-associated receptor-1 (TAAR1) agonists have been proposed as potential antipsychotics, with ulotaront and ralmitaront having reached clinical trials. While ulotaront demonstrated efficacy in a recent Phase II trial, a corresponding study studies of ralmitaront failed to show efficacy as a monotherapy or as an adjunct to atypical antipsychotics. In addition to TAAR1 agonism, ulotaront is a partial agonist at the serotonin 1A receptor (5-HT1AR). However, little is known about ralmitaront. METHODS: We compared ulotaront and ralmitaront at TAAR1, 5-HT1AR, and dopamine D2 using luciferase complementation-based G protein recruitment, cAMP accumulation, and G protein-coupled inward rectifier potassium channel activation assays. RESULTS: Ralmitaront showed lower efficacy at TAAR1 in G protein recruitment, cAMP accumulation, and GIRK activation assays. Moreover, ralmitaront lacked detectable activity at 5-HT1AR and dopamine D2. CONCLUSIONS: Compared with ulotaront, ralmitaront shows lower efficacy and slower kinetics at TAAR1 and lacks efficacy at 5-HT1AR. These data may be relevant to understanding differences in clinical profiles of these 2 compounds.

Modulation of multiple biological targets with a single drug can lead to synergistic therapeutic effects and has been demonstrated to be essential for efficient treatment of CNS disorders. However, rational design of compounds that interact with several targets is very challenging. Here, we demonstrate that structure-based virtual screening can guide the discovery of multi-target ligands of unrelated proteins relevant for Parkinson's disease. A library with 5.4 million molecules was docked to crystal structures of the A(2A) adenosine receptor (A(2A)AR) and monoamine oxidase B (MAO-B). Twenty-four compounds that were among the highest ranked for both binding sites were evaluated experimentally, resulting in the discovery of four dual-target ligands. The most potent compound was an A(2A)AR antagonist with nanomolar affinity (K-i = 19 nM) and inhibited MAO-B with an IC50 of 100 nM. Optimization guided by the predicted binding modes led to the identification of a second potent dual-target scaffold. The two discovered scaffolds were shown to counteract 6-hydroxydopamine-induced neurotoxicity in dopaminergic neuronal-like SH-SY5Y cells. Structure-based screening can hence be used to identify ligands with specific polypharmacological profiles, providing new avenues for drug development against complex diseases.