Prioritizing multiple therapeutic targets in parallel using automated DNA-encoded library screening.
Carl A MachuttaChristopher S KollmannKenneth E LindXiaopeng BaiPan F ChanJianzhong HuangLluis BallellSvetlana BelyanskayaGurdyal S BesraDavid Barros-AguirreRobert H BatesPaolo A CentrellaSandy S ChangJing ChaiAnthony E ChoudhryAaron CoffinChristopher P DavieHongfeng DengJianghe DengYun DingJason W DodsonDavid T FosbennerEnoch N GaoTaylor L GrahamTodd L GraybillKaren IngrahamWalter P JohnsonBryan W KingChristopher R KwiatkowskiJoël LelièvreYue LiXiaorong LiuQuinn LuRuth LehrAlfonso Mendoza-LosanaJohn MartinLynn McCloskeyPatti McCormickHeather P O'KeefeThomas O'KeeffeChristina PaoChristopher B PhelpsHongwei QiKeith RaffertyGenaro S ScavelloMatt S SteigingaFlora S SundersinghSharon M SweitzerLawrence M SzewczukAmy TaylorMay Fern TohJuan WangMinghui WangDevan J WilkinsBing XiaGang YaoJean ZhangJingye ZhouChristine P DonahueJeffrey A MesserDavid HolmesChristopher C Arico-MuendelAndrew J PopeJeffrey W GrossGhotas EvindarPublished in: Nature communications (2017)
The identification and prioritization of chemically tractable therapeutic targets is a significant challenge in the discovery of new medicines. We have developed a novel method that rapidly screens multiple proteins in parallel using DNA-encoded library technology (ELT). Initial efforts were focused on the efficient discovery of antibacterial leads against 119 targets from Acinetobacter baumannii and Staphylococcus aureus. The success of this effort led to the hypothesis that the relative number of ELT binders alone could be used to assess the ligandability of large sets of proteins. This concept was further explored by screening 42 targets from Mycobacterium tuberculosis. Active chemical series for six targets from our initial effort as well as three chemotypes for DHFR from M. tuberculosis are reported. The findings demonstrate that parallel ELT selections can be used to assess ligandability and highlight opportunities for successful lead and tool discovery.
Keyphrases
- high throughput
- mycobacterium tuberculosis
- acinetobacter baumannii
- small molecule
- staphylococcus aureus
- circulating tumor
- multidrug resistant
- drug resistant
- pseudomonas aeruginosa
- cell free
- single molecule
- pulmonary tuberculosis
- machine learning
- deep learning
- nucleic acid
- gene expression
- cystic fibrosis
- wound healing
- anti inflammatory
- adverse drug
- antiretroviral therapy