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

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- κ B 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 IC 50 of 0.010 ± 0.06 μM, which…

• OS
  Ohio State University

  Award: P30CA016058
• NI
  National Institutes of Health

  Awards: GM122459, CA234124, P30CA016058
• NC
  National Cancer Institute

  Awards: P30CA016058, CA234124