Identification of structural fingerprints among natural inhibitors of HDAC1 to accelerate nature-inspired drug discovery in cancer epigenetics

Abstract

Histone deacetylase 1 (HDAC1), a class I HDAC enzyme, is crucial for histone modification. Currently, it is emerged as one of the important biological targets for designing small molecule drugs through cancer epigenetics. Along with synthetic inhibitors different natural inhibitors are showing potential HDAC1 inhibitions. In order to gain insights into the relationship between the molecular structures of the natural inhibitors and HDAC1, different molecular modelling techniques (Bayesian classification, recursive partitioning, molecular docking and molecular dynamics simulations) have been applied on a dataset of 155 HDAC1 nature-inspired inhibitors with diverse scaffolds. The Bayesian study showed acceptable ROC values for both the training set and test sets. The Recursive partitioning study produced decision tree 1 with 6 leaves. Further, molecular docking study was processed for generating the protein ligand complex which identified some potential amino acid residues such as F205, H28, L271, P29, F150, Y204 for the binding interactions in case of natural inhibitors. Stability of these HDAC1-natutal inhibitors complexes has been also evaluated by molecular dynamics simulation study. The current modelling study is an attempt to get a deep insight into the different important structural fingerprints among different natural compounds modulating HDAC1 inhibition.

Communicated by Ramaswamy H. Sarma

Keywords:

• Cancer
• HDAC1 inhibitor
• natural product
• fingerprint
• molecular docking
• MD simulation

Acknowledgments

Authors sincerely acknowledge the Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India and Department of Pharmaceutical Technology, JIS University, Kolkata, India for providing the research facilities. The authors acknowledge Centre for Modelling Simulation & Design (CMSD), University of Hyderabad, Hyderabad for computational resources for MD simulation studies. Shovanlal Gayen thanks SERB, Govt. of India for financial assistance under the MATRICS scheme (MTR/2022/000286).

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.