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Ojo OA, Ojo AB, Okolie C, Nwakama MAC, Iyobhebhe M, Evbuomwan IO, Nwonuma CO, Maimako RF, Adegboyega AE, Taiwo OA, Alsharif KF, Batiha GES. Deciphering the Interactions of Bioactive Compounds in Selected Traditional Medicinal Plants against Alzheimer's Diseases via Pharmacophore Modeling, Auto-QSAR, and Molecular Docking Approaches. Molecules 2021; 26:molecules26071996. [PMID: 33915968 PMCID: PMC8037217 DOI: 10.3390/molecules26071996] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 03/22/2021] [Accepted: 03/29/2021] [Indexed: 02/06/2023] Open
Abstract
Neurodegenerative diseases, for example Alzheimer’s, are perceived as driven by hereditary, cellular, and multifaceted biochemical actions. Numerous plant products, for example flavonoids, are documented in studies for having the ability to pass the blood-brain barrier and moderate the development of such illnesses. Computer-aided drug design (CADD) has achieved importance in the drug discovery world; innovative developments in the aspects of structure identification and characterization, bio-computational science, and molecular biology have added to the preparation of new medications towards these ailments. In this study we evaluated nine flavonoid compounds identified from three medicinal plants, namely T. diversifolia, B. sapida, and I. gabonensis for their inhibitory role on acetylcholinesterase (AChE), butyrylcholinesterase (BChE) and monoamine oxidase (MAO) activity, using pharmacophore modeling, auto-QSAR prediction, and molecular studies, in comparison with standard drugs. The results indicated that the pharmacophore models produced from structures of AChE, BChE and MAO could identify the active compounds, with a recuperation rate of the actives found near 100% in the complete ranked decoy database. Moreso, the robustness of the virtual screening method was accessed by well-established methods including enrichment factor (EF), receiver operating characteristic curve (ROC), Boltzmann-enhanced discrimination of receiver operating characteristic (BEDROC), and area under accumulation curve (AUAC). Most notably, the compounds’ pIC50 values were predicted by a machine learning-based model generated by the AutoQSAR algorithm. The generated model was validated to affirm its predictive model. The best models achieved for AChE, BChE and MAO were models kpls_radial_17 (R2 = 0.86 and Q2 = 0.73), pls_38 (R2 = 0.77 and Q2 = 0.72), kpls_desc_44 (R2 = 0.81 and Q2 = 0.81) and these externally validated models were utilized to predict the bioactivities of the lead compounds. The binding affinity results of the ligands against the three selected targets revealed that luteolin displayed the highest affinity score of −9.60 kcal/mol, closely followed by apigenin and ellagic acid with docking scores of −9.60 and −9.53 kcal/mol, respectively. The least binding affinity was attained by gallic acid (−6.30 kcal/mol). The docking scores of our standards were −10.40 and −7.93 kcal/mol for donepezil and galanthamine, respectively. The toxicity prediction revealed that none of the flavonoids presented toxicity and they all had good absorption parameters for the analyzed targets. Hence, these compounds can be considered as likely leads for drug improvement against the same.
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Affiliation(s)
- Oluwafemi Adeleke Ojo
- Medicinal Biochemistry and Biochemical Toxicology Group, Department of Biochemistry, Landmark University, Omu-Aran PMB 1001, Nigeria; (M.-A.C.N.); (M.I.); (C.O.N.); (R.F.M.)
- Correspondence: ; Tel.: +234-703-782-4647
| | - Adebola Busola Ojo
- Department of Biochemistry, Faculty of Sciences, Ekiti State University, Ado-Ekiti PMB 5363, Nigeria;
| | - Charles Okolie
- Department of Microbiology, Landmark University, Omu-Aran PMB 1001, Nigeria; (C.O.); (I.O.E.)
| | - Mary-Ann Chinyere Nwakama
- Medicinal Biochemistry and Biochemical Toxicology Group, Department of Biochemistry, Landmark University, Omu-Aran PMB 1001, Nigeria; (M.-A.C.N.); (M.I.); (C.O.N.); (R.F.M.)
| | - Matthew Iyobhebhe
- Medicinal Biochemistry and Biochemical Toxicology Group, Department of Biochemistry, Landmark University, Omu-Aran PMB 1001, Nigeria; (M.-A.C.N.); (M.I.); (C.O.N.); (R.F.M.)
| | | | - Charles Obiora Nwonuma
- Medicinal Biochemistry and Biochemical Toxicology Group, Department of Biochemistry, Landmark University, Omu-Aran PMB 1001, Nigeria; (M.-A.C.N.); (M.I.); (C.O.N.); (R.F.M.)
| | - Rotdelmwa Filibus Maimako
- Medicinal Biochemistry and Biochemical Toxicology Group, Department of Biochemistry, Landmark University, Omu-Aran PMB 1001, Nigeria; (M.-A.C.N.); (M.I.); (C.O.N.); (R.F.M.)
| | | | | | - Khalaf F. Alsharif
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia;
| | - Gaber El-Saber Batiha
- Department of Pharmacology and Therapeutics, Faculty of Veterinary Medicine, Damanhour University, Damanhour, AlBeheira 22511, Egypt;
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Zhang H, Gu X, Meng C, Zhou D, Chen G, Wang J, Liu Y, Li N. Computational investigation of 4,5-diphenyl-1H-pyrrole-3-carboxylic acid derivatives as B-cell lymphoma-extra large (Bcl-xL) inhibitors by using 3D-QSAR, molecular docking, and molecular dynamics simulations. Struct Chem 2020. [DOI: 10.1007/s11224-020-01631-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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