自然(考古学)
计算机科学
化学
地质学
古生物学
作者
Ping Liu,Refeya Jannatul,Juan Chen,Lihua Hou,Mingjuan Gao,Pengjie Wang,Lulu Wang,Dekui Jin,Hao Chen,Rong Liu,Ran Wang,Yin-Hua Zhu,Bing Fang,Lirong Jia,Yanan Sun,Yixin Zhang,Fazheng Ren,Weilin Lin
标识
DOI:10.1002/ange.202409746
摘要
Abstract Non‐natural building blocks (BBs) present a vast reservoir of chemical diversity for molecular recognition and drug discovery. However, leveraging evolutionary principles to efficiently generate bioactive molecules with a larger number of diverse BBs poses challenges within current laboratory evolution systems. Here, we introduce programmable chemical evolution (PCEvo) by integrating chemoinformatic classification and high‐throughput array synthesis/screening. PCEvo initiates evolution by constructing a diversely combinatorial library to create ancestral molecules, streamlines the molecular evolution process and identifies high‐affinity binders within 2–4 cycles. By employing PCEvo with 108 BBs and exploring >10 17 chemical space, we identify bicyclic peptidomimetic binders against targets SAR‐CoV‐2 RBD and Claudin18.2, achieving nanomolar affinity. Remarkably, Claudin18.2 binders selectively stain gastric adenocarcinoma cell lines and patient samples. PCEvo achieves expedited evolution in a few rounds, marking a significant advance in utilizing non‐natural building blocks for rapid chemical evolution applicable to targets with or without prior structural information and ligand preference.
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