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Updated: Jun 3, 2026
Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
Published on: July 25, 2013
AI-Driven De novo Design of HDAC6/EZH2 Dual-target Peptide Inhibitors for Epigenetic Cancer Therapy
Yu Zhang1, Si Chen2, ShiJie Gai3
1School of Computer Engineering and Science, Shanghai University, Shanghai, China.
Introduction:
Histone deacetylase 6 (HDAC6) and enhancer of zeste homolog 2 (EZH2) are crucial epigenetic regulators in cancer, with their synergistic roles playing a significant part in tumorigenesis and progression. However, dual-target peptide inhibitors that target both HDAC6 and EZH2 have not yet been developed. In this work, we present an AI-driven de novo design strategy to develop novel peptide inhibitors that target HDAC6 and EZH2.
Methods:
Our approach used RFDiffusion to generate diverse peptide conformations, followed by ProteinMPNN to predict the most probable amino acid sequences. The HDAC6 and EZH2- binding peptides were then fused using linker sequences, and AlphaFold3 was employed to predict the resulting structures, thereby validating the feasibility of the fusion design and identifying high-confidence peptide candidates.
Results:
Through 100 ns molecular dynamics simulations, three peptide inhibitors were identified, and DTP-1 showed superior stability and binding free energies of －88.96 kcal/mol (HDAC6) and －62.53 kcal/mol (EZH2).
Discussion:
These findings substantiate the feasibility of an AI-guided workflow for designing dual-target peptide inhibitors that establish a foundation for enhancing the efficacy of epigenetic combination therapies and accelerating the development of dual-target inhibitors.
Conclusion:
This study introduces novel peptide inhibitors targeting HDAC6 and EZH2, along with a generalizable strategy for designing dual-target peptide inhibitors. DTP-1 emerges as a promising lead inhibitor for further preclinical development.
