Structure-based screening and molecular dynamics simulation studies for the identification of potential acetylcholinesterase inhibitors

ABSTRACT

Alzheimer’s disease (AD) is an irreversible, progressive neurodegenerative disease characterised by the appearance of amyloid β plaques and neurofibrillary tangles in the brain. The loss of cholinergic neurons is considered to be one of the contributing factors for cognitive and memory deficits in the disease. Donepezil, the most successful and prescribed drug to treat the symptoms of AD, is acetylcholinesterase (AChE) inhibitor that improves the memory and brain’s cognitive functions. In this study, the pharmacophoric features of donepezil were mapped from its cocrystal with AChE and were used to screen the Zinc15 database. The obtained hits were subjected to molecular properties and PAINS filter. The homology modelling and molecular dynamics (MD) were performed to prepare selected AChE protein model (PDB id 4EY7) for advanced studies. The virtual screening and precision docking led to the identification of five compounds. The ADMET property prediction and free energy calculations were carried out to obtain three final compounds. The Alanine scanning and MD study of the compounds viz. ZINC000013719534, ZINC000035551243 and ZINC000035596918 produced stable complexes of the ligands. The identified virtual leads have the potential for better AChE inhibition.

Highlights

• Pharmacophore mapping of donepezil was developed from PDB id 4EY7.

• Pharmacophore-based screening yielded 601 compounds with RMSD < 0.5 Å.

• PAINS and Drug likeliness filtration resulted in 199 compounds for further virtual screening and precision docking studies.

• Free binding energy and ADMET studies provided three active compounds.

• The molecular dynamics study indicated that the compounds ZINC000013719534, ZINC000035551243 and ZINC000035596918 formed stable interactions with the protein.

KEYWORDS:

• Acetylcholinesterase
• donepezil
• Alzheimer’s disease
• Amber
• docking

Acknowledgements

The authors would like to acknowledge the financial research support from Ministry of Human Resource and Development (MHRD), New Delhi, India, in the form of teaching assistantship to AG, RS, DK, GG and PG.

The authors would also like to extend their gratitude toward Professor David A. Case, Department of Chemistry & Chemical Biology, Rutgers University, New Jersey, USA, for granting a license for Amber 18 and Dr. Stefano Forli, Department of Integrative Structural and Computational Biology, Scripps Research, California Campus for providing python script, vstools_v0.16. Molecular graphics and analyses performed with UCSF Chimera, developed by the Resource for Biocomputing, Visualization, and Informatics at the University of California, San Francisco, with support from NIH P41-GM103311 is also thankfully acknowledged.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Ankit Ganeshpurkarhttp://orcid.org/0000-0002-2029-3424

Ravi Singh http://orcid.org/0000-0002-2737-2205

Gopichand Guttihttp://orcid.org/0000-0002-5284-2379

Sushil Kumar Singh http://orcid.org/0000-0003-0473-5824