Title:Exploration of Fingerprints and Data Mining-based Prediction of Some Bioactive Compounds from Allium sativum as Histone Deacetylase 9
(HDAC9) Inhibitors
Volume: 20
Author(s): Totan Das, Arijit Bhattacharya, Tarun Jha and Shovanlal Gayen*
Affiliation:
- Laboratory of Drug Design and Discovery, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India
Keywords:
Histone deacetylase 9 (HDAC9), bayesian classification, recursive partitioning tree, Allium sativum, molecular docking, molecular dynamics simulation.
Abstract: Background: Histone deacetylase 9 (HDAC9) is an important member of the class
IIa family of histone deacetylases. It is well established that over-expression of HDAC9 causes
various types of cancers including gastric cancer, breast cancer, ovarian cancer, liver cancer,
lung cancer, lymphoblastic leukaemia, etc. The important role of HDAC9 is also recognized in
the development of bone, cardiac muscles, and innate immunity. Thus, it will be beneficial to
find out the important structural attributes of HDAC9 inhibitors for developing selective
HDAC9 inhibitors with higher potency.
Methods: The classification QSAR-based methods namely Bayesian classification and recursive
partitioning method were applied to a dataset consisting of HADC9 inhibitors. The structural
features strongly suggested that sulphur-containing compounds can be a good choice for
HDAC9 inhibition. For this reason, these models were applied further to screen some natural
compounds from Allium sativum. The screened compounds were further accessed for the ADME
properties and docked in the homology-modelled structure of HDAC9 in order to find important
amino acids for the interaction. The best-docked compound was considered for molecular
dynamics (MD) simulation study.
Results: The classification models have identified good and bad fingerprints for HDAC9 inhibition.
The screened compounds like ajoene, 1,2 vinyl dithiine, diallyl disulphide and diallyl trisulphide
had been identified as compounds having potent HDAC9 inhibitory activity. The results
from ADME and molecular docking study of these compounds show the binding interaction
inside the active site of the HDAC9. The best-docked compound ajoene shows satisfactory
results in terms of different validation parameters of MD simulation study.
Conclusion: This in-silico modelling study has identified the natural potential lead (s) from Allium
sativum. Specifically, the ajoene with the best in-silico features can be considered for further
in-vitro and in-vivo investigation to establish as potential HDAC9 inhibitors.
