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Sequence-based drug design as a concept in computational drug design

Author

Listed:
  • Lifan Chen

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Zisheng Fan

    (Chinese Academy of Sciences
    Nanjing University of Chinese Medicine
    Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University)

  • Jie Chang

    (Chinese Academy of Sciences
    Nanjing University of Chinese Medicine)

  • Ruirui Yang

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences
    Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University)

  • Hui Hou

    (Chinese Academy of Sciences)

  • Hao Guo

    (Chinese Academy of Sciences)

  • Yinghui Zhang

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Tianbiao Yang

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Chenmao Zhou

    (Chinese Academy of Sciences
    Nanjing University of Chinese Medicine)

  • Qibang Sui

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Zhengyang Chen

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Chen Zheng

    (Chinese Academy of Sciences)

  • Xinyue Hao

    (Chinese Academy of Sciences
    Nanjing University of Chinese Medicine)

  • Keke Zhang

    (Chinese Academy of Sciences
    Nanjing University of Chinese Medicine)

  • Rongrong Cui

    (Chinese Academy of Sciences)

  • Zehong Zhang

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Hudson Ma

    (Chinese Academy of Sciences)

  • Yiluan Ding

    (Chinese Academy of Sciences)

  • Naixia Zhang

    (Chinese Academy of Sciences)

  • Xiaojie Lu

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Xiaomin Luo

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Hualiang Jiang

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences
    Nanjing University of Chinese Medicine
    Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University)

  • Sulin Zhang

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Mingyue Zheng

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences
    Nanjing University of Chinese Medicine
    Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University)

Abstract

Drug development based on target proteins has been a successful approach in recent decades. However, the conventional structure-based drug design (SBDD) pipeline is a complex, human-engineered process with multiple independently optimized steps. Here, we propose a sequence-to-drug concept for computational drug design based on protein sequence information by end-to-end differentiable learning. We validate this concept in three stages. First, we design TransformerCPI2.0 as a core tool for the concept, which demonstrates generalization ability across proteins and compounds. Second, we interpret the binding knowledge that TransformerCPI2.0 learned. Finally, we use TransformerCPI2.0 to discover new hits for challenging drug targets, and identify new target for an existing drug based on an inverse application of the concept. Overall, this proof-of-concept study shows that the sequence-to-drug concept adds a perspective on drug design. It can serve as an alternative method to SBDD, particularly for proteins that do not yet have high-quality 3D structures available.

Suggested Citation

  • 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.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-39856-w
    DOI: 10.1038/s41467-023-39856-w
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    References listed on IDEAS

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