Login Register Cart 0
Generic placeholder image

Current Drug Discovery Technologies

Editor-in-Chief

ISSN (Print): 1570-1638
ISSN (Online): 1875-6220

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Short Communication

Exploring the Molecular Structural Requirements of Flavonoids as Beta- Secretase-1 Inhibitors Using Molecular Modeling Studies

Author(s): Uttam A. More*, Malleshappa N. Noolvi, Devendra Kumar and Avanish Tripathi

Volume 20, Issue 3, 2023

Published on: 05 April, 2023

Article ID: e290323215095 Pages: 14

DOI: 10.2174/1570163820666230329090424

Price: $65

conference banner
Abstract

Background: BACE1 (beta-site amyloid precursor protein (APP) cleaving enzyme) is a key target for Alzheimer's disease research because it catalyses the rate-limiting step in the formation of amyloid protein (Aβ). Natural dietary flavonoids have gained a lot of interest as potential Alzheimer's therapy candidates because of their anti-amyloidogenic, antioxidative, and anti-inflammatory properties. More research is needed, however, to learn more about the specific routes through which flavonoids may have neuroprotective benefits in Alzheimer's disease.

Objective: Here, we report an in silico molecular modeling study for natural compounds, particularly flavonoids, as BACE-1 inhibitors.

Methods: The interactions of flavonoids with the BACE-1 catalytic core were disclosed by demonstrating the predicted docking pose of flavonoids with BACE-1. The stability of flavonoids BACE-1 complex was analyzed by molecular dynamic simulation (standard dynamic cascade).

Results: Our findings imply that these flavonoids, which have methoxy group instead of hydroxy may be promising BACE1 inhibitors that could reduce Aβ formation in Alzheimer's disease. The molecular docking study revealed that flavonoids e bind with the BACE1’s wide active site along with the catalytic residues Asp32 and Asp228. Further molecular dynamic investigation revealed that the average RMSD for all complexes ranged from 2.05 to 2.32 Å, indicating that the molecules were relatively stable during MD simulation. The RMSD analyses demonstrate that the flavonoids were structurally stable during the MD simulation. The RMSF was utilised to study the time-dependent fluctuation of the complexes. The N-terminal (~2.5 Å) fluctuates less than the C-terminal (~6.5 Å). Rutin and Hesperidin were highly stable in the catalytic region as compared to other flavonoids like Rhoifolin, Hesperidin, Methylchalcone, Phlorizin and Naringin.

Conclusion: We were able to justify the flavonoids' selectivity for BACE-1 and crossing BBB for the treatment of Alzheimer's disease by using a combination of molecular modelling tools.

Graphical Abstract

[1]
Giannakopoulos P, Hop PR, Kövari E, Vallet PG, Herrmann FR, Bouras C. Distinct patterns of neuronal loss and Alzheimer’s disease lesion distribution in elderly individuals older than 90 years. J Neuropathol Exp Neurol 1996; 55(12): 1210-20.
[http://dx.doi.org/10.1097/00005072-199612000-00004] [PMID: 8957444]
[2]
Vassar R, Bennett BD, Babu-Khan S, et al. Beta-secretase cleavage of Alzheimer’s amyloid precursor protein by the transmembrane aspartic protease BACE. Science 1999; 286(5440): 735-41.
[http://dx.doi.org/10.1126/science.286.5440.735] [PMID: 10531052]
[3]
Yan R, Bienkowski MJ, Shuck ME, et al. Membrane-anchored aspartyl protease with Alzheimer’s disease β-secretase activity. Nature 1999; 402(6761): 533-7.
[http://dx.doi.org/10.1038/990107] [PMID: 10591213]
[4]
Barten DM, Meredith JE Jr, Zaczek R, Houston JG, Albright CF. γ-secretase inhibitors for Alzheimer’s disease: Balancing efficacy and toxicity Drugs R D 2006; 7(2): 87-97.
[http://dx.doi.org/10.2165/00126839-200607020-00003] [PMID: 16542055]
[5]
Beecher GR. Overview of dietary flavonoids: Nomenclature, occurrence and intake. J Nutr 2003; 133(10): S3248-54.
[http://dx.doi.org/10.1093/jn/133.10.3248S] [PMID: 14519822]
[6]
Commenges D, Scotet V, Renaud S, Jacqmin-Gadda H, Barberger-Gateau P, Dartigues JF. Intake of flavonoids and risk of dementia. Eur J Epidemiol 2000; 16(4): 357-63.
[http://dx.doi.org/10.1023/A:1007614613771] [PMID: 10959944]
[7]
Ko PC, Kilduff PT, Higgins JA, Milberg W, McGlinchey R. Evidence for intact selective attention in Alzheimer’s disease patients using a location priming task. Neuropsychology 2005; 19(3): 381-9.
[http://dx.doi.org/10.1037/0894-4105.19.3.381] [PMID: 15910124]
[8]
More UA, Patel S, Rahevar V, Noolvi MN, Aminabhavi TM, Joshi SD. In silico ADME and QSAR studies on a set of coumarin derivatives as acetylcholinesterase inhibitors against Alzheimer’s disease: CoMFA, CoMSIA, Topomer CoMFA, and HQSAR. Lett Drug Des Discov 2020; 17(6): 684-712.
[http://dx.doi.org/10.2174/1570180816666190712095907]
[9]
Ambure P, Bhat J, Puzyn T, Roy K. Identifying natural compounds as multi-target-directed ligands against Alzheimer’s disease: An in silico approach. J Biomol Struct Dyn 2019; 37(5): 1282-306.
[http://dx.doi.org/10.1080/07391102.2018.1456975] [PMID: 29578387]
[10]
Biovia DS. Discovery studio. Dassault Systèmes BIOVIA 2016.
[11]
Brooks BR, Brooks CL III, Mackerell AD Jr, et al. CHARMM: The biomolecular simulation program. J Comput Chem 2009; 30(10): 1545-614.
[http://dx.doi.org/10.1002/jcc.21287] [PMID: 19444816]
[12]
LigPrep Version 24. New York, NY, USA: Schrodinger. LLC 2010.
[13]
Hong L, Koelsch G, Lin X, et al. Structure of the protease domain of memapsin 2 (β-secretase) complexed with inhibitor. Science 2000; 290(5489): 150-3.
[http://dx.doi.org/10.1126/science.290.5489.150] [PMID: 11021803]
[14]
Glide, Version 56. New York, NY, USA: Schrodinger. LLC 2010.
[15]
Friesner RA, Banks JL, Murphy RB, et al. Glide: A new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 2004; 47(7): 1739-49.
[http://dx.doi.org/10.1021/jm0306430] [PMID: 15027865]
[16]
Ryckaert JP, Ciccotti G, Berendsen HJC. Numerical integration of the cartesian equations of motion of a system with constraints: Molecular dynamics of n-alkanes. J Comput Phys 1977; 23(3): 327-41.
[http://dx.doi.org/10.1016/0021-9991(77)90098-5]
[17]
Xia X, Maliski EG, Gallant P, Rogers D. Classification of kinase inhibitors using a Bayesian model. J Med Chem 2004; 47(18): 4463-70.
[http://dx.doi.org/10.1021/jm0303195] [PMID: 15317458]
[18]
Venkatapathy R, Moudgal CJ, Bruce RM. Assessment of the oral rat chronic lowest observed adverse effect level model in TOPKAT, a QSAR software package for toxicity prediction. J Chem Inf Comput Sci 2004; 44(5): 1623-9.
[http://dx.doi.org/10.1002/9780470744307.gat079] [PMID: 15446819]
[19]
Gonella DR, Manganelli S, Esposito A, Roncaglioni A, Manganaro A, Benfenati E. Comparison of in silico tools for evaluating rat oral acute toxicity. SAR QSAR Environ Res 2015; 26(1): 1-27.
[http://dx.doi.org/10.1080/1062936X.2014.977819] [PMID: 25567032]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy