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

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

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.

Inhibition of amyloid oligomerization into different supramolecular architectures by small molecules: mechanistic insights and design rules

Protein misfolding and aggregation have been associated with several human disorders, including Alzheimer’s, Parkinson’s and Huntington’s diseases, as well as senile systemic amyloidosis and Type II diabetes. However, there is no current disease-modifying therapy available for the treatment of these disorders. In spite of extensive academic, pharmaceutical, medicinal and clinical research, a complete mechanistic model for this family of diseases is still lacking. In this review, we primarily discuss the different types of small molecular entities which have been used for the inhibition of the aggregation process of different amyloidogenic proteins under diseased conditions. These include small peptides, polyphenols, inositols, quinones and their derivatives, and metal chelator molecules. In recent years, these groups of molecules have been extensively studied using in vitro, in vivo and computational models to understand their mechanism of action and common structural features underlying the process of inhibition. A salient feature found to be instrumental in the process of inhibition is the balance between the aromatic unit that functions as the amyloid recognition unit and the hydrophilic amyloid breaker unit. The establishment of structure–function relationship for amyloid-modifying therapies by the various functional entities should serve as an important step toward the development of efficient therapeutics.