IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-024-49892-9.html
   My bibliography  Save this article

In silico fragment-based discovery of CIB1-directed anti-tumor agents by FRASE-bot

Author

Listed:
  • Yi An

    (University of North Carolina)

  • Jiwoong Lim

    (University of North Carolina)

  • Marta Glavatskikh

    (University of North Carolina)

  • Xiaowen Wang

    (University of North Carolina
    University of Missouri, Columbia)

  • Jacqueline Norris-Drouin

    (University of North Carolina)

  • P. Brian Hardy

    (University of North Carolina)

  • Tina M. Leisner

    (University of North Carolina)

  • Kenneth H. Pearce

    (University of North Carolina)

  • Dmitri Kireev

    (University of North Carolina
    University of Missouri, Columbia)

Abstract

Chemical probes are an indispensable tool for translating biological discoveries into new therapies, though are increasingly difficult to identify since novel therapeutic targets are often hard-to-drug proteins. We introduce FRASE-based hit-finding robot (FRASE-bot), to expedite drug discovery for unconventional therapeutic targets. FRASE-bot mines available 3D structures of ligand-protein complexes to create a database of FRAgments in Structural Environments (FRASE). The FRASE database can be screened to identify structural environments similar to those in the target protein and seed the target structure with relevant ligand fragments. A neural network model is used to retain fragments with the highest likelihood of being native binders. The seeded fragments then inform ultra-large-scale virtual screening of commercially available compounds. We apply FRASE-bot to identify ligands for Calcium and Integrin Binding protein 1 (CIB1), a promising drug target implicated in triple negative breast cancer. FRASE-based virtual screening identifies a small-molecule CIB1 ligand (with binding confirmed in a TR-FRET assay) showing specific cell-killing activity in CIB1-dependent cancer cells, but not in CIB1-depletion-insensitive cells.

Suggested Citation

  • 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.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-49892-9
    DOI: 10.1038/s41467-024-49892-9
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-49892-9
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-024-49892-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Christoph Gorgulla & Andras Boeszoermenyi & Zi-Fu Wang & Patrick D. Fischer & Paul W. Coote & Krishna M. Padmanabha Das & Yehor S. Malets & Dmytro S. Radchenko & Yurii S. Moroz & David A. Scott & Kons, 2020. "An open-source drug discovery platform enables ultra-large virtual screens," Nature, Nature, vol. 580(7805), pages 663-668, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    3. Ayan Chatterjee & Robin Walters & Zohair Shafi & Omair Shafi Ahmed & Michael Sebek & Deisy Gysi & Rose Yu & Tina Eliassi-Rad & Albert-László Barabási & Giulia Menichetti, 2023. "Improving the generalizability of protein-ligand binding predictions with AI-Bind," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    4. Kun Wang & Chia-Wei Lee & Xuewu Sui & Siyoung Kim & Shuhui Wang & Aidan B. Higgs & Aaron J. Baublis & Gregory A. Voth & Maofu Liao & Tobias C. Walther & Robert V. Farese, 2023. "The structure of phosphatidylinositol remodeling MBOAT7 reveals its catalytic mechanism and enables inhibitor identification," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    5. 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.
    6. Wai Cheung Chan & Xiaoxi Liu & Robert S. Magin & Nicholas M. Girardi & Scott B. Ficarro & Wanyi Hu & Maria I. Tarazona Guzman & Cara A. Starnbach & Alejandra Felix & Guillaume Adelmant & Anthony C. Va, 2023. "Accelerating inhibitor discovery for deubiquitinating enzymes," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    7. 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.
    8. 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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-49892-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.