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

Sijie Chen, Tong Lin, Ruchira Basu, Jeremy Ritchey, Shen Wang, Yichuan Luo, Xingcan Li, Dehua Pei (), Levent Burak Kara () and Xiaolin Cheng ()
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Sijie Chen: The Ohio State University
Tong Lin: 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

Nature Communications, 2024, vol. 15, issue 1, 1-20

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-45766-2

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