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A cryptic pocket in Ebola VP35 allosterically controls RNA binding

Author

Listed:
  • Matthew A. Cruz

    (Washington University School of Medicine)

  • Thomas E. Frederick

    (Washington University School of Medicine)

  • Upasana L. Mallimadugula

    (Washington University School of Medicine)

  • Sukrit Singh

    (Washington University School of Medicine)

  • Neha Vithani

    (Washington University School of Medicine)

  • Maxwell I. Zimmerman

    (Washington University School of Medicine)

  • Justin R. Porter

    (Washington University School of Medicine)

  • Katelyn E. Moeder

    (Washington University School of Medicine)

  • Gaya K. Amarasinghe

    (Washington University School of Medicine)

  • Gregory R. Bowman

    (Washington University School of Medicine
    Washington University in St. Louis)

Abstract

Protein-protein and protein-nucleic acid interactions are often considered difficult drug targets because the surfaces involved lack obvious druggable pockets. Cryptic pockets could present opportunities for targeting these interactions, but identifying and exploiting these pockets remains challenging. Here, we apply a general pipeline for identifying cryptic pockets to the interferon inhibitory domain (IID) of Ebola virus viral protein 35 (VP35). VP35 plays multiple essential roles in Ebola’s replication cycle but lacks pockets that present obvious utility for drug design. Using adaptive sampling simulations and machine learning algorithms, we predict VP35 harbors a cryptic pocket that is allosterically coupled to a key dsRNA-binding interface. Thiol labeling experiments corroborate the predicted pocket and mutating the predicted allosteric network supports our model of allostery. Finally, covalent modifications that mimic drug binding allosterically disrupt dsRNA binding that is essential for immune evasion. Based on these results, we expect this pipeline will be applicable to other proteins.

Suggested Citation

  • Matthew A. Cruz & Thomas E. Frederick & Upasana L. Mallimadugula & Sukrit Singh & Neha Vithani & Maxwell I. Zimmerman & Justin R. Porter & Katelyn E. Moeder & Gaya K. Amarasinghe & Gregory R. Bowman, 2022. "A cryptic pocket in Ebola VP35 allosterically controls RNA binding," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-29927-9
    DOI: 10.1038/s41467-022-29927-9
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    References listed on IDEAS

    as
    1. Michael D. Ward & Maxwell I. Zimmerman & Artur Meller & Moses Chung & S. J. Swamidass & Gregory R. Bowman, 2021. "Deep learning the structural determinants of protein biochemical properties by comparing structural ensembles with DiffNets," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    2. Shaoyong Lu & Xinheng He & Zhao Yang & Zongtao Chai & Shuhua Zhou & Junyan Wang & Ashfaq Ur Rehman & Duan Ni & Jun Pu & Jinpeng Sun & Jian Zhang, 2021. "Activation pathway of a G protein-coupled receptor uncovers conformational intermediates as targets for allosteric drug design," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
    3. Kathryn M. Hart & Chris M. W. Ho & Supratik Dutta & Michael L. Gross & Gregory R. Bowman, 2016. "Modelling proteins’ hidden conformations to predict antibiotic resistance," Nature Communications, Nature, vol. 7(1), pages 1-10, December.
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    Cited by:

    1. Artur Meller & Michael Ward & Jonathan Borowsky & Meghana Kshirsagar & Jeffrey M. Lotthammer & Felipe Oviedo & Juan Lavista Ferres & Gregory R. Bowman, 2023. "Predicting locations of cryptic pockets from single protein structures using the PocketMiner graph neural network," Nature Communications, Nature, vol. 14(1), pages 1-15, December.

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