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Ultra-large library docking for discovering new chemotypes

Author

Listed:
  • Jiankun Lyu

    (University of California, San Francisco
    East China University of Science & Technology)

  • Sheng Wang

    (University of Chinese Academy of Sciences
    University of North Carolina at Chapel Hill School of Medicine)

  • Trent E. Balius

    (University of California, San Francisco)

  • Isha Singh

    (University of California, San Francisco)

  • Anat Levit

    (University of California, San Francisco)

  • Yurii S. Moroz

    (National Taras Shevchenko University of Kiev
    Chemspace)

  • Matthew J. O’Meara

    (University of California, San Francisco)

  • Tao Che

    (University of North Carolina at Chapel Hill School of Medicine)

  • Enkhjargal Algaa

    (University of California, San Francisco)

  • Kateryna Tolmachova

    (Enamine)

  • Andrey A. Tolmachev

    (Enamine)

  • Brian K. Shoichet

    (University of California, San Francisco)

  • Bryan L. Roth

    (University of North Carolina at Chapel Hill School of Medicine
    University of North Carolina at Chapel Hill
    University of North Carolina at Chapel Hill School of Medicine)

  • John J. Irwin

    (University of California, San Francisco)

Abstract

Despite intense interest in expanding chemical space, libraries containing hundreds-of-millions to billions of diverse molecules have remained inaccessible. Here we investigate structure-based docking of 170 million make-on-demand compounds from 130 well-characterized reactions. The resulting library is diverse, representing over 10.7 million scaffolds that are otherwise unavailable. For each compound in the library, docking against AmpC β-lactamase (AmpC) and the D4 dopamine receptor were simulated. From the top-ranking molecules, 44 and 549 compounds were synthesized and tested for interactions with AmpC and the D4 dopamine receptor, respectively. We found a phenolate inhibitor of AmpC, which revealed a group of inhibitors without known precedent. This molecule was optimized to 77 nM, which places it among the most potent non-covalent AmpC inhibitors known. Crystal structures of this and other AmpC inhibitors confirmed the docking predictions. Against the D4 dopamine receptor, hit rates fell almost monotonically with docking score, and a hit-rate versus score curve predicted that the library contained 453,000 ligands for the D4 dopamine receptor. Of 81 new chemotypes discovered, 30 showed submicromolar activity, including a 180-pM subtype-selective agonist of the D4 dopamine receptor.

Suggested Citation

  • Jiankun Lyu & Sheng Wang & Trent E. Balius & Isha Singh & Anat Levit & Yurii S. Moroz & Matthew J. O’Meara & Tao Che & Enkhjargal Algaa & Kateryna Tolmachova & Andrey A. Tolmachev & Brian K. Shoichet , 2019. "Ultra-large library docking for discovering new chemotypes," Nature, Nature, vol. 566(7743), pages 224-229, February.
  • Handle: RePEc:nat:nature:v:566:y:2019:i:7743:d:10.1038_s41586-019-0917-9
    DOI: 10.1038/s41586-019-0917-9
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    Citations

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    Cited by:

    1. Stefan Gahbauer & Chelsea DeLeon & Joao M. Braz & Veronica Craik & Hye Jin Kang & Xiaobo Wan & Xi-Ping Huang & Christian B. Billesbølle & Yongfeng Liu & Tao Che & Ishan Deshpande & Madison Jewell & El, 2023. "Docking for EP4R antagonists active against inflammatory pain," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    2. Paul Beroza & James J. Crawford & Oleg Ganichkin & Leo Gendelev & Seth F. Harris & Raphael Klein & Anh Miu & Stefan Steinbacher & Franca-Maria Klingler & Christian Lemmen, 2022. "Chemical space docking enables large-scale structure-based virtual screening to discover ROCK1 kinase inhibitors," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    3. Jing Gu & Rui-Kun Peng & Chun-Ling Guo & Meng Zhang & Jie Yang & Xiao Yan & Qian Zhou & Hongwei Li & Na Wang & Jinwei Zhu & Qin Ouyang, 2022. "Construction of a synthetic methodology-based library and its application in identifying a GIT/PIX protein–protein interaction inhibitor," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    4. Lifan Chen & Zisheng Fan & Jie Chang & Ruirui Yang & Hui Hou & Hao Guo & Yinghui Zhang & Tianbiao Yang & Chenmao Zhou & Qibang Sui & Zhengyang Chen & Chen Zheng & Xinyue Hao & Keke Zhang & Rongrong Cu, 2023. "Sequence-based drug design as a concept in computational drug design," Nature Communications, Nature, vol. 14(1), pages 1-21, December.
    5. Ryan Theisen & Tianduanyi Wang & Balaguru Ravikumar & Rayees Rahman & Anna Cichońska, 2024. "Leveraging multiple data types for improved compound-kinase bioactivity prediction," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    6. Yi An & Jiwoong Lim & Marta Glavatskikh & Xiaowen Wang & Jacqueline Norris-Drouin & P. Brian Hardy & Tina M. Leisner & Kenneth H. Pearce & Dmitri Kireev, 2024. "In silico fragment-based discovery of CIB1-directed anti-tumor agents by FRASE-bot," Nature Communications, Nature, vol. 15(1), pages 1-10, December.

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