Programmable Chemical Evolution with Natural/Non-Natural Building Blocks

Research output: Contribution to journalResearch articleContributedpeer-review

Contributors

  • Ping Liu - , China Agricultural University (Author)
  • Refeya Jannatul - , TUD Dresden University of Technology (Author)
  • Juan Chen - , China Agricultural University (Author)
  • Lihua Hou - , General Hospital of People's Liberation Army (Author)
  • Mingjuan Gao - , General Hospital of People's Liberation Army (Author)
  • Pengjie Wang - , China Agricultural University (Author)
  • Lulu Wang - , Tianjin Medical University (Author)
  • Dekui Jin - , General Hospital of People's Liberation Army (Author)
  • Hao Chen - , Suzhou Institute of Systems Medicine (Author)
  • Rong Liu - , China Agricultural University (Author)
  • Ran Wang - , China Agricultural University (Author)
  • Yinhua Zhu - , China Agricultural University (Author)
  • Bing Fang - , China Agricultural University (Author)
  • Lirong Jia - , China Agricultural University (Author)
  • Yanan Sun - , China Agricultural University (Author)
  • Yixin Zhang - , Clusters of Excellence PoL: Physics of Life, Chair of Biomolecular Interactions (Author)
  • Fazheng Ren - , China Agricultural University (Author)
  • Weilin Lin - , Suzhou Institute of Systems Medicine (Author)

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 >1017 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.

Details

Original languageEnglish
Article numbere202409746
JournalAngewandte Chemie - International Edition
Volume63
Issue number46
Publication statusPublished - 11 Nov 2024
Peer-reviewedYes

External IDs

ORCID /0000-0002-6669-4995/work/171553078

Keywords

ASJC Scopus subject areas

Keywords

  • ancestral molecule, mutation, nanomolar affinity, natural/non-natural building blocks, Programmable chemical evolution, selection