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dc.contributor.authorSaw Simeonen_US
dc.contributor.authorNuttapat Anuwongcharoenen_US
dc.contributor.authorWatshara Shoombuatongen_US
dc.contributor.authorAijaz Ahmad Maliken_US
dc.contributor.authorVirapong Prachayasittikulen_US
dc.contributor.authorJarl E.S. Wikbergen_US
dc.contributor.authorChanin Nantasenamaten_US
dc.contributor.otherMahidol Universityen_US
dc.contributor.otherUppsala Universiteten_US
dc.identifier.citationPeerJ. Vol.2016, No.9 (2016)en_US
dc.description.abstract© 2016 Simeon et al. 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, QCV2 and QExt2 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, QCV2 and QExt2 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.en_US
dc.rightsMahidol Universityen_US
dc.subjectAgricultural and Biological Sciencesen_US
dc.subjectBiochemistry, Genetics and Molecular Biologyen_US
dc.titleProbing the origins of human acetylcholinesterase inhibition via QSAR modeling and molecular dockingen_US
Appears in Collections:Scopus 2016-2017

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