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. 2024;22(14):2353-2367.
doi: 10.2174/1570159X22666240515090434.

In Silico Prediction of Quercetin Analogs for Targeting Death-Associated Protein Kinase 1 (DAPK1) Against Alzheimer's Disease

Yilu Sun  1   2 , Jia Zhao  1   2   3 , Yizhu Lu  2 , Fung Yin Ngo  2 , Bo Shuai  4 , Zhang-Jin Zhang  1   2 , Yibin Feng  2 , Jianhui Rong  2
Affiliations

In Silico Prediction of Quercetin Analogs for Targeting Death-Associated Protein Kinase 1 (DAPK1) Against Alzheimer's Disease

Yilu Sun et al. Curr Neuropharmacol. 2024.

Abstract

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that greatly affects the health and life quality of the elderly population. Existing drugs mainly alleviate symptoms but fail to halt disease progression, underscoring the urgent need for the development of novel drugs. Based on the neuroprotective effects of flavonoid quercetin in AD, this study was designed to identify potential AD-related targets for quercetin and perform in silico prediction of promising analogs for the treatment of AD. Database mining suggested death-associated protein kinase 1 (DAPK1) as the most promising AD-related target for quercetin among seven protein candidates. To achieve better biological effects for the treatment of AD, we devised a series of quercetin analogs as ligands for DAPK1, and molecular docking analyses, absorption, distribution, metabolism, and excretion (ADME) predictions, as well as molecular dynamics (MD) simulations, were performed. The energy for drug-protein interaction was predicted and ranked. As a result, quercetin-A1a and quercetin-A1a1 out of 19 quercetin analogs exhibited the lowest interaction energy for binding to DAPK1 than quercetin, and they had similar dynamics performance with quercetin. In addition, quercetin-A1a and quercetin-A1a1 were predicted to have better water solubility. Thus, quercetin-A1a and quercetin-A1a1 could be promising agents for the treatment of AD. Our findings paved the way for further experimental studies and the development of novel drugs.

Keywords: Alzheimer’s disease (AD); In silico prediction; death-associated protein kinase 1 (DAPK1); neurodegenerative disease.; quercetin; quercetin analogs.

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Conflict of interest statement

The authors declare no conflict of interest, financial or otherwise.

Figures

Fig. (1)
Fig. (1)
(A) Chemical structure of quercetin. (B) Protein-protein interaction network of DAPK1 and targets in “inhibition of apoptosis.” ADORA1, Adenosine A1 Receptor; AKT1, Protein kinase B alpha; CDK1, Cyclin-dependent kinase 1; DAPK1, Death-associated protein kinase 1; EGFR, Epidermal Growth Factor Receptor; GLO1, Glyoxalase 1; GSK3B, Glycogen synthase kinase-3 beta; IGF1R, Insulin-Like Growth Factor 1 Receptor; KDR, Vascular endothelial growth factor receptor 2; MMP9, Matrix Metallopeptidase 9; MPO, Myeloperoxidase; PIK3R1, Phosphatidyl inositol 3 kinase; PLK1, Polo-like kinase 1. (C) Biological and functional characterization of the protein targets. (D) The role of DAPK1 in Alzheimer’s Disease. DAPK1, Death-associated protein kinase 1; PKD, protein kinase D; NDRG2, NDRG Family Member 2; APP, Amyloid Beta Precursor Protein; PIK3R1, Phosphatidyl inositol 3 kinase; JNK, c-Jun N-terminal kinase; CypD, Cyclophilin D; Aβ, Amyloid beta.
Fig. (2)
Fig. (2)
(A) 2D structure of quercetin; (B) 2D structure of quercetin-A1a; (C) 2D structure of quercetin-A1a1; (D) 3D structure of quercetin; (E) 3D structure of quercetin-A1a; (F) 3D structure of quercetin-A1a1. Modified sites were marked with red and green squares.
Fig. (3)
Fig. (3)
Molecular docking analyses of DAPK1 with the top three compounds. (A) 2D predicted interaction between quercetin and DAPK1; (B) 2D predicted interaction between quercetin-A1a and DAPK1; (C) 2D predicted interaction between quercetin-A1a1 and DAPK1; (D) 3D predicted interaction between quercetin and DAPK1; (E) 3D predicted interaction between quercetin-A1a and DAPK1; (F) 3D predicted interaction between quercetin-A1a1 and DAPK1.
Fig. (4)
Fig. (4)
(A) BOILED egg Model prediction; (B) Water solubility prediction (Solubility class: Log S scale Insoluble < -10 < Poorly < -6 < Moderately < -4 < Soluble < -2 Very < 0 < Highly).
Fig. (5)
Fig. (5)
MD simulation. (A) The evaluation of potential system pressure of quercetin, quercetin1A1a and quercetin-A1a1 with DAPK1 as a function of time. (B) The evaluation of potential system energy of quercetin, quercetin1A1a and quercetin-A1a1 with DAPK1 as a function of time.
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References

    1. Nichols E., Szoeke C.E.I., Vollset S.E., Abbasi N., Abd-Allah F., Abdela J., Aichour M.T.E., Akinyemi R.O., Alahdab F., Asgedom S.W., Awasthi A., Barker-Collo S.L., Baune B.T., Béjot Y., Belachew A.B., Bennett D.A., Biadgo B., Bijani A., Bin Sayeed M.S., Brayne C., Carpenter D.O., Carvalho F., Catalá-López F., Cerin E., Choi J-Y.J., Dang A.K., Degefa M.G., Djalalinia S., Dubey M., Duken E.E., Edvardsson D., Endres M., Eskandarieh S., Faro A., Farzadfar F., Fereshtehnejad S-M., Fernandes E., Filip I., Fischer F., Gebre A.K., Geremew D., Ghasemi-Kasman M., Gnedovskaya E.V., Gupta R., Hachinski V., Hagos T.B., Hamidi S., Hankey G.J., Haro J.M., Hay S.I., Irvani S.S.N., Jha R.P., Jonas J.B., Kalani R., Karch A., Kasaeian A., Khader Y.S., Khalil I.A., Khan E.A., Khanna T., Khoja T.A.M., Khubchandani J., Kisa A., Kissimova-Skarbek K., Kivimäki M., Koyanagi A., Krohn K.J., Logroscino G., Lorkowski S., Majdan M., Malekzadeh R., März W., Massano J., Mengistu G., Meretoja A., Mohammadi M., Mohammadi-Khanaposhtani M., Mokdad A.H., Mondello S., Moradi G., Nagel G., Naghavi M., Naik G., Nguyen L.H., Nguyen T.H., Nirayo Y.L., Nixon M.R., Ofori-Asenso R., Ogbo F.A., Olagunju A.T., Owolabi M.O., Panda-Jonas S., Passos V.M.A., Pereira D.M., Pinilla-Monsalve G.D., Piradov M.A., Pond C.D., Poustchi H., Qorbani M., Radfar A., Reiner R.C., Jr, Robinson S.R., Roshandel G., Rostami A., Russ T.C., Sachdev P.S., Safari H., Safiri S., Sahathevan R., Salimi Y., Satpathy M., Sawhney M., Saylan M., Sepanlou S.G., Shafieesabet A., Shaikh M.A., Sahraian M.A., Shigematsu M., Shiri R., Shiue I., Silva J.P., Smith M., Sobhani S., Stein D.J., Tabarés-Seisdedos R., Tovani-Palone M.R., Tran B.X., Tran T.T., Tsegay A.T., Ullah I., Venketasubramanian N., Vlassov V., Wang Y-P., Weiss J., Westerman R., Wijeratne T., Wyper G.M.A., Yano Y., Yimer E.M., Yonemoto N., Yousefifard M., Zaidi Z., Zare Z., Vos T., Feigin V.L., Murray C.J.L. Global, regional, and national burden of Alzheimer’s disease and other dementias, 1990-2016: A systematic analysis for the global burden of disease study 2016. Lancet Neurol. 2019;18(1):88–106. doi: 10.1016/S1474-4422(18)30403-4. - DOI - PMC - PubMed
    1. 2020 Alzheimer’s disease facts and figures. Alzheimers Dement. 2020:2020. - PubMed
    1. Chen G., Xu T., Yan Y., Zhou Y., Jiang Y., Melcher K., Xu H.E. Amyloid beta: Structure, biology and structure-based therapeutic development. Acta Pharmacol. Sin. 2017;38(9):1205–1235. doi: 10.1038/aps.2017.28. - DOI - PMC - PubMed
    1. Binder L.I., Guillozet-Bongaarts A.L., Garcia-Sierra F., Berry R.W. Tau, tangles, and Alzheimer’s disease. Biochim. Biophys. Acta Mol. Basis Dis. 2005;1739(2-3):216–223. doi: 10.1016/j.bbadis.2004.08.014. - DOI - PubMed
    1. Lyketsos C.G., Carrillo M.C., Ryan J.M., Khachaturian A.S., Trzepacz P., Amatniek J., Cedarbaum J., Brashear R., Miller D.S. Neuropsychiatric symptoms in Alzheimer’s disease. Alzheimers Dement. 2011;7(5):532–539. doi: 10.1016/j.jalz.2011.05.2410. - DOI - PMC - PubMed

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