Design of target specific peptide inhibitors using generative deep learning and molecular dynamics simulations.
Nat Commun
; 15(1): 1611, 2024 Feb 21.
Article
in En
| MEDLINE
| ID: mdl-38383543
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 ß-catenin and NF-κB essential modulator. Among the twelve ß-catenin inhibitors, six exhibit improved binding affinity compared to the parent peptide. Notably, the best C-terminal peptide binds ß-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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Molecular Dynamics Simulation
/
Deep Learning
Language:
En
Journal:
Nat Commun
Journal subject:
BIOLOGIA
/
CIENCIA
Year:
2024
Document type:
Article
Affiliation country:
United States