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

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...

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Published inScience advances Vol. 10; no. 32; p. eadn1524
Main Authors Díaz-Holguín, Alejandro, Saarinen, Marcus, Vo, Duc Duy, Sturchio, Andrea, Branzell, Niclas, Cabeza de Vaca, Israel, Hu, Huabin, Mitjavila-Domènech, Núria, Lindqvist, Annika, Baranczewski, Pawel, Millan, Mark J, Yang, Yunting, Carlsson, Jens, Svenningsson, Per
Format Journal Article
LanguageEnglish
Published United States American Association for the Advancement of Science 09.08.2024
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Summary: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 (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.
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These authors contributed equally to this work.
ISSN:2375-2548
2375-2548
DOI:10.1126/sciadv.adn1524