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

  • Sci Adv. 2024 Aug 9;10(32):eadn1524. doi: 10.1126/sciadv.adn1524.
Alejandro Díaz-Holguín  1 ,  Marcus Saarinen  2 ,  Duc Duy Vo  1 ,  Andrea Sturchio  2  3 ,  Niclas Branzell  2 ,  Israel Cabeza de Vaca  1 ,  Huabin Hu  1 ,  Núria Mitjavila-Domènech  1 ,  Annika Lindqvist  4 ,  Pawel Baranczewski  4 ,  Mark J Millan  5 ,  Yunting Yang  2 ,  Jens Carlsson  1 ,  Per Svenningsson  2
Affiliations
  • 1. Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Box 596, SE-751 24 Uppsala, Sweden.
  • 2. Neuro Svenningsson, Department of Clinical Neuroscience, Karolinska Institute, SE-171 76 Stockholm, Sweden.
  • 3. Department of Neurology, James J. and Joan A. Gardner Family Center for Parkinson's Disease and Movement Disorders, University of Cincinnati, Cincinnati, OH, USA.
  • 4. Department of Pharmacy, SciLifeLab Drug Discovery and Development Platform, Uppsala University, Box 580, SE-751 23 Uppsala, Sweden.
  • 5. Neuroinflammation Therapeutic Area, Institut de Recherches Servier, Centre de Recherches de Croissy, Paris, France and Institute of Neuroscience and Psychology, College of Medicine, Vet and Life Sciences, Glasgow University, Scotland, Glasgow, UK.
PMID: 39110804 DOI: 10.1126/sciadv.adn1524
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 (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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