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

Chemistry

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

S. Chen, T. Lin, et al.

This innovative study by Sijie Chen, Tong Lin, Ruchira Basu, Jeremy Ritchey, Shen Wang, Yichuan Luo, Xingcan Li, Dehua Pei, Levent Burak Kara, and Xiaolin Cheng showcases a groundbreaking computational method combining GRU-VAE with Rosetta FlexPepDock, leading to significant enhancements in peptide inhibitors targeting β-catenin and NEMO. With some inhibitors demonstrating up to a 15-fold improvement in binding affinity, this research elegantly merges deep learning with molecular modeling.

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Playback language: English
Abstract
This study presents a computational approach for designing target-specific peptide inhibitors by integrating a Gated Recurrent Unit-based Variational Autoencoder (GRU-VAE) with Rosetta FlexPepDock for peptide sequence generation and binding affinity assessment, followed by molecular dynamics (MD) simulations for refinement. Applying this to β-catenin and NF-κB essential modulator (NEMO), six out of twelve β-catenin inhibitors showed improved binding affinity (best with a 15-fold improvement), and two out of four NEMO inhibitors showed enhanced binding. This highlights the successful integration of deep learning and structure-based modeling for peptide design.
Publisher
Nature Communications
Published On
Feb 21, 2024
Authors
Sijie Chen, Tong Lin, Ruchira Basu, Jeremy Ritchey, Shen Wang, Yichuan Luo, Xingcan Li, Dehua Pei, Levent Burak Kara, Xiaolin Cheng
Tags
peptide inhibitors
binding affinity
GRU-VAE
molecular dynamics
deep learning
structure-based modeling
β-catenin

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