Integration of ligand and structure-based pharmacophore screening for the identification of novel natural leads against Euchromatic histone lysine methyltransferase 2 (EHMT2/G9a)

Abstract

Herein, we report a blended ligand and structure-based pharmacophore screening approach to identify new natural leads against the Protein Lysine Methyltransferase 2 (EHMT2/G9a). The EHMT2/G9a has been associated with Cancer, Alzheimer’s, and aging and is considered an emerging drug target having no clinically passed inhibitor. Purposefully, we developed the ligand-based pharmacophore (Pharmacophore-L) based on the common features of known inhibitors and the structure-based pharmacophore (Pharmacophore-S) based on the interaction profile of available crystal structures. The Pharmacophore-L and Pharmacophore-S were subjected to multiple tiers of validations and utilized in combination for the screening of total 741543 compounds coming from multiple databases. Additional layers of stringency were applied in the screening process to test drug-likeness (using Lipinski’s rule, Veber’s rule, SMARTS and ADMET filtration), to rule out any toxicity (TOPKAT analysis). The interaction profiles, stabilities, and comparative analysis against the reference were carried out by flexible docking, MD simulation, and MM-GBSA analysis, which finally led to three leads as potential inhibitors of G9a.

Communicated by Ramaswamy H. Sarma

Keywords:

• Epigenetics
• histone
• lysine methyltransferase
• dual pharmacophore
• QSAR
• EHMT2
• G9a

Data availability statement

All data generated or analysed during this study are included in this published article [and its supplementary information files].

Authors contribution

AJ carried out the experiments and helped in preparing the manuscript. RN and SS contributed in MD simulation study. CG-F contributed in knowledge sharing and language checking. DRB mentored the research, and wrote the manuscript.

Acknowledgements

DRB wants to express his gratitude to the National Institute of Technology Durgapur (NITDGP) for the platform and for the purchase of BIOVIA Discovery Studio academic research suite. DRB also want to thank CRG (CRG/2022/002672) and SIRE (SIR/2022/000031) by the Science and Engineering Research Board (SERB) of India for funding. DRB thanks DST-FIST program for the purchase of server node at Chemistry, NIT Durgapur, and Dr Subhas Ghosal for helping with DFT analysis and overall help. AJ wants to thank the National Institute of Technology Durgapur (NITDGP) for the institute fellowship.

Disclosure statement

The authors have no potential conflicts of interest (financial or non-financial) to disclose. No study-specific approval or consent by the appropriate ethics committee was required for this study.

Additional information

Funding

The study was funded by CRG (CRG/2022/002672) and SIRE (SIR/2022/000031) by the Science and Engineering Research Board (SERB). DST-SERB;