2024-08-09 カロリンスカ研究所(KI)
<関連情報>
- https://news.ki.se/ai-accelerates-the-pace-of-development-of-new-drugs-for-brain-diseases
- https://www.science.org/doi/10.1126/sciadv.adn1524
AlphaFoldが微量アミン関連受容体1を標的とする向精神薬作動薬の発見を加速 AlphaFold accelerated discovery of psychotropic agonists targeting the trace amine–associated receptor 1
Alejandro Díaz-Holguín, Marcus Saarinen, Duc Duy Vo, Andrea Sturchio, […], and Per Svenningsson
Science Advances Published:7 Aug 2024
DOI:https://doi.org/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.
