AlphaFold accelerated discovery of psychotropic agonists targeting the trace amine-associated receptor 1.

ABSTRACT: 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 μM. The AlphaFold screen yielded a more than twofold higher hit rate (

SUBMITTER: Diaz-Holguin A 

PROVIDER: S-EPMC11305387 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature