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Design of target specific peptide inhibitors using generative deep learning and molecular dynamics simulations

Author

Listed:
  • Sijie Chen

    (The Ohio State University)

  • Tong Lin

    (Carnegie Mellon University
    Carnegie Mellon University)

  • Ruchira Basu

    (The Ohio State University)

  • Jeremy Ritchey

    (The Ohio State University)

  • Shen Wang

    (The Ohio State University)

  • Yichuan Luo

    (Carnegie Mellon University)

  • Xingcan Li

    (Affiliated Hospital and Medical School of Nantong University)

  • Dehua Pei

    (The Ohio State University)

  • Levent Burak Kara

    (Carnegie Mellon University)

  • Xiaolin Cheng

    (The Ohio State University
    The Ohio State University)

Abstract

We introduce a computational approach for the design of target-specific peptides. Our method integrates a Gated Recurrent Unit-based Variational Autoencoder with Rosetta FlexPepDock for peptide sequence generation and binding affinity assessment. Subsequently, molecular dynamics simulations are employed to narrow down the selection of peptides for experimental assays. We apply this computational strategy to design peptide inhibitors that specifically target β-catenin and NF-κB essential modulator. Among the twelve β-catenin inhibitors, six exhibit improved binding affinity compared to the parent peptide. Notably, the best C-terminal peptide binds β-catenin with an IC50 of 0.010 ± 0.06 μM, which is 15-fold better than the parent peptide. For NF-κB essential modulator, two of the four tested peptides display substantially enhanced binding compared to the parent peptide. Collectively, this study underscores the successful integration of deep learning and structure-based modeling and simulation for target specific peptide design.

Suggested Citation

  • Sijie Chen & Tong Lin & Ruchira Basu & Jeremy Ritchey & Shen Wang & Yichuan Luo & Xingcan Li & Dehua Pei & Levent Burak Kara & Xiaolin Cheng, 2024. "Design of target specific peptide inhibitors using generative deep learning and molecular dynamics simulations," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-45766-2
    DOI: 10.1038/s41467-024-45766-2
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    References listed on IDEAS

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    2. Noelia Ferruz & Steffen Schmidt & Birte Höcker, 2022. "ProtGPT2 is a deep unsupervised language model for protein design," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    3. Jeffrey A. Schneider & Timothy W. Craven & Amanda C. Kasper & Chi Yun & Michael Haugbro & Erica M. Briggs & Vladimir Svetlov & Evgeny Nudler & Holger Knaut & Richard Bonneau & Michael J. Garabedian & , 2018. "Design of Peptoid-peptide Macrocycles to Inhibit the β-catenin TCF Interaction in Prostate Cancer," Nature Communications, Nature, vol. 9(1), pages 1-10, December.
    4. John Jumper & Richard Evans & Alexander Pritzel & Tim Green & Michael Figurnov & Olaf Ronneberger & Kathryn Tunyasuvunakool & Russ Bates & Augustin Žídek & Anna Potapenko & Alex Bridgland & Clemens Me, 2021. "Highly accurate protein structure prediction with AlphaFold," Nature, Nature, vol. 596(7873), pages 583-589, August.
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