QSAR, docking, and Molecular dynamic studies on the polyphenolic as inhibitors of β-amyloid aggregation

Mahmoodabadi, Najmeh; Ajloo, Davood

doi:10.1007/s00044-016-1620-0

Cited by 8 publications

(4 citation statements)

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“…Furthermore, α-helices provide the surfaces suitable for the binding of πdelocalized ligands, as well [77]. Next, the quantitative structure-activity analysis of the polyphenolic inhibitors of Aβ aggregation showed negative correlation between the fibrillization extent and the molecular descriptors such as P , .. Mol wt , CV and CA , indicating that the highest size factor leads to the greatest inhibitory effect [78]. In turn, the P values derived for the dyes under investigation were lower than those reported previously, and a negative correlation between P , L , .. Mol wt and the inhibitor potency 0 max / FF was observed (the correlation coefficients were estimated to be RY = -0.6, -0.75 and -0.73, respectively), suggesting that either different mechanisms are involved in the prevention of insulin aggregation [77], or that the high ability of the dyes to self-associate in buffer solution and/or in the presence of native proteins prevents them from exerting specific inhibitory effects [79].…”

Section: Accepted Manuscriptmentioning

confidence: 98%

“…At the same time, the polyphenolic inhibitors (luteolin, transilitin, maritimetin) of Aβ aggregation showed the π-π stacking interactions with the peptide [78]. Interestingly, the bovine insulin is significantly more prone to fibrillization than the human and porcine insulins due to the A8 residue (A-chain) on the protein surface, enhancing the hydrophobic intermolecular interactions [54].…”

Section: Accepted Manuscriptmentioning

confidence: 99%

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Cyanine dyes derived inhibition of insulin fibrillization

Vus

Girych

Trusova

et al. 2019

Journal of Molecular Liquids

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The potential of novel cyanine dyes to inhibit the insulin amyloid formation was evaluated using thioflavin T fluorescence assay, quantum-chemical calculations, molecular docking and molecular dynamics simulations. According to the ability to suppress the insulin fibrillization under physiological conditions the examined compounds were found to follow the order: trimethines > pentamethines > monomethines > heptamethines. Of these, the trimethines 3-3 and 3-5, and pentamethines 5-3 and 5-9 almost completely prevented the protein aggregation by retarding both nucleation (except 3-3) and elongation processes. The quantum-chemical calculations revealed a complex relationship between the dye structure and its inhibitory effects.The molecular docking studies showed that most cyanines bind specifically to the L17 ladder of the B chain, located at the dry steric zipper of the insulin fibril protofilament, and form the stable complexes with the helices of the insulin monomer. The molecular dynamics simulations provided evidence for the increase of insulin helicity in the presence of cyanines. Collectively, the presented findings highlight two possible mechanisms by which cyanines can inhibit the insulin fibrillization: i) stabilization of the native protein structure followed by the retardation of the protein nucleation (all dyes); and ii) blocking the lateral extension of β-sheets via the dye-protein stacking interactions (3-3, 3-5, 5-3, 5-9). Overall, the obtained results may prove of importance for the design of small molecules capable of preventing amyloid fibril formation by insulin and other proteins.

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Section: Accepted Manuscriptmentioning

confidence: 98%

Section: Accepted Manuscriptmentioning

confidence: 99%

Cyanine dyes derived inhibition of insulin fibrillization

Vus

Girych

Trusova

et al. 2019

Journal of Molecular Liquids

View full text Add to dashboard Cite

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“…Khatkar et al [ 38 ] performed a QSAR model employing p -coumaric acid derivatives as AChE inhibitors. Mahmoodabadi et al [ 39 ] developed a QSAR model with molecular dynamic studies using polyphenolic compounds as inhibitors of β-amyloid aggregation. Dhiman et al [ 40 ] developed QSAR studies utilizing a large series of flavonoid derivatives (apigenin, kaempferol, luteolin and quercetin) as monoamine oxidase inhibitors.…”

Section: Introductionmentioning

confidence: 99%

Deciphering the Interactions of Bioactive Compounds in Selected Traditional Medicinal Plants against Alzheimer’s Diseases via Pharmacophore Modeling, Auto-QSAR, and Molecular Docking Approaches

Ojo

Okolie

et al. 2021

Molecules

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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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“…Finally, in 2016 Mahmoodabadi and Ajloo developed a QSAR study using docking and molecular dynamics to study polyphenols as inhibitors of β-amyloid aggregation [30]. The inhibitory effect on amyloid-β aggregation was investigated on 25 polyphenolic compounds.…”

Section: Alzheimer - 10 Years Of Qsarmentioning

confidence: 99%