Probing the origins of human acetylcholinesterase inhibition via QSAR modeling and molecular docking

Abstract

Alzheimer’s disease (AD) is a chronic neurodegenerative disease which leads to the gradual loss of neuronal cells. Several hypotheses for AD exists (e.g., cholinergic, amyloid, tau hypotheses, etc.). As per the cholinergic hypothesis, the deficiency of choline is responsible for AD; therefore, the inhibition of AChE is a lucrative therapeutic strategy for the treatment of AD. Acetylcholinesterase (AChE) is an enzyme that catalyzes the breakdown of the neurotransmitter acetylcholine that is essential for cognition and memory. A large non-redundant data set of 2,570 compounds with reported IC50 values against AChE was obtained from ChEMBL and employed in quantitative structure-activity relationship (QSAR) study so as to gain insights on their origin of bioactivity. AChE inhibitors were described by a set of 12 fingerprint descriptors and predictive models were constructed from 100 different data splits using random forest. Generated models afforded R2, and values in ranges of 0.66–0.93, 0.55–0.79 and 0.56–0.81 for the training set, 10-fold cross-validated set and external set, respectively. The best model built using the substructure count was selected according to the OECD guidelines and it afforded R2, and values of 0.92 ± 0.01, 0.78 ± 0.06 and 0.78 ± 0.05, respectively. Furthermore, Y-scrambling was applied to evaluate the possibility of chance correlation of the predictive model. Subsequently, a thorough analysis of the substructure fingerprint count was conducted to provide informative insights on the inhibitory activity of AChE inhibitors. Moreover, Kennard–Stone sampling of the actives were applied to select 30 diverse compounds for further molecular docking studies in order to gain structural insights on the origin of AChE inhibition. Site-moiety mapping of compounds from the diversity set revealed three binding anchors encompassing both hydrogen bonding and van der Waals interaction. Molecular docking revealed that compounds 13, 5 and 28 exhibited the lowest binding energies of −12.2, −12.0 and −12.0 kcal/mol, respectively, against human AChE, which is modulated by hydrogen bonding, π–π stacking and hydrophobic interaction inside the binding pocket. These information may be used as guidelines for the design of novel and robust AChE inhibitors.

Synthesis, enzyme inhibitory kinetics mechanism and computational study ofN-(4-methoxyphenethyl)-N-(substituted)-4-methylbenzenesulfonamides as novel therapeutic agents for Alzheimer’s disease

“…Moreover, single π – π interaction was observed between benzene of 5c and Trp286 residue having distance 5.38 Å. Literature study also justified that these interacted residues are significant in downstream signaling pathways and justify the significance of our docking results ( Fang et al, 2014 ; Simeon et al, 2016 ). The graphical depiction of 5c docking complex is mentioned in Figs.…”

Drug-1,3,4-Thiadiazole Conjugates as Novel Mixed-Type Inhibitors of Acetylcholinesterase: Synthesis, Molecular Docking, Pharmacokinetics, and ADMET Evaluation

“…Likewise, two hydrophobic interactions were observed between Tyr341 and Trp286 with distances of 4.50 Å and 3.80 Å. A previous study reported that these cooperated residues are important in downstream signalling pathways [28,29]. A graphical depiction of the complex with 3f docked is shown in Figure 5.…”

In Silico Prediction of O6-Methylguanine-DNA Methyltransferase Inhibitory Potency of Base Analogs with QSAR and Machine Learning Methods

“…Ext fingerprint is well fit for predicting and gaining insight into drug activity. For example, Ext fingerprint has been proven to perform well for classifying predicting androgen or estrogen receptors binders or non-binders and acetylcholinesterase inhibitors or noninhibitors, respectively [ 36 , 37 ]. Based on the results, the top ten models were Ext-RF, Ext-LR, Ext-ANN, Ext-SVM, Graph-RF, PubChem-LR, Ext-Tree, PubChem-RF, Graph-LR and PubChem-Tree, respectively.…”