2023-05-25 マサチューセッツ工科大学(MIT)
◆この研究は、AIが新しい抗生物質の発見に役立つことを示しており、今後の研究に期待が寄せられています。
<関連情報>
- https://news.mit.edu/2023/using-ai-scientists-combat-drug-resistant-infections-0525
- https://www.nature.com/articles/s41589-023-01349-8
アシネトバクター・バウマニを標的とした抗生物質のディープラーニングによる発見 Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii
Gary Liu,Denise B. Catacutan,Khushi Rathod,Kyle Swanson,Wengong Jin,Jody C. Mohammed,Anush Chiappino-Pepe,Saad A. Syed,Meghan Fragis,Kenneth Rachwalski,Jakob Magolan,Michael G. Surette,Brian K. Coombes,Tommi Jaakkola,Regina Barzilay,James J. Collins & Jonathan M. Stokes
Nature Chemical Biology Published:25 May 2023
DOI:https://doi.org/10.1038/s41589-023-01349-8
Abstract
Acinetobacter baumannii is a nosocomial Gram-negative pathogen that often displays multidrug resistance. Discovering new antibiotics against A. baumannii has proven challenging through conventional screening approaches. Fortunately, machine learning methods allow for the rapid exploration of chemical space, increasing the probability of discovering new antibacterial molecules. Here we screened ~7,500 molecules for those that inhibited the growth of A. baumannii in vitro. We trained a neural network with this growth inhibition dataset and performed in silico predictions for structurally new molecules with activity against A. baumannii. Through this approach, we discovered abaucin, an antibacterial compound with narrow-spectrum activity against A. baumannii. Further investigations revealed that abaucin perturbs lipoprotein trafficking through a mechanism involving LolE. Moreover, abaucin could control an A. baumannii infection in a mouse wound model. This work highlights the utility of machine learning in antibiotic discovery and describes a promising lead with targeted activity against a challenging Gram-negative pathogen.
