Probing the origins of human acetylcholinesterase inhibition via QSAR modeling and molecular docking
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 i...
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| Format: | Article |
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PeerJ Inc.
2016-08-01
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| Online Access: | https://peerj.com/articles/2322.pdf |
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| author | Saw Simeon Nuttapat Anuwongcharoen Watshara Shoombuatong Aijaz Ahmad Malik Virapong Prachayasittikul Jarl E.S. Wikberg Chanin Nantasenamat |
| author_facet | Saw Simeon Nuttapat Anuwongcharoen Watshara Shoombuatong Aijaz Ahmad Malik Virapong Prachayasittikul Jarl E.S. Wikberg Chanin Nantasenamat |
| author_sort | Saw Simeon |
| collection | DOAJ |
| description | 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, ${Q}_{\mathrm{CV }}^{2}$ Q CV 2 and ${Q}_{\mathrm{Ext}}^{2}$ Q Ext 2 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, ${Q}_{\mathrm{CV }}^{2}$ Q CV 2 and ${Q}_{\mathrm{Ext}}^{2}$ Q Ext 2 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. |
| first_indexed | 2024-03-09T08:18:05Z |
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| language | English |
| last_indexed | 2024-03-09T08:18:05Z |
| publishDate | 2016-08-01 |
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| spelling | doaj.art-e05fc79b75b54bce9a0f26ba671337ed2023-12-02T22:00:51ZengPeerJ Inc.PeerJ2167-83592016-08-014e232210.7717/peerj.2322Probing the origins of human acetylcholinesterase inhibition via QSAR modeling and molecular dockingSaw Simeon0Nuttapat Anuwongcharoen1Watshara Shoombuatong2Aijaz Ahmad Malik3Virapong Prachayasittikul4Jarl E.S. Wikberg5Chanin Nantasenamat6Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, ThailandCenter of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, ThailandCenter of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, ThailandCenter of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, ThailandDepartment of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok, ThailandDepartment of Pharmaceutical Biosciences, Uppsala University, Uppsala, SwedenCenter of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, ThailandAlzheimer’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, ${Q}_{\mathrm{CV }}^{2}$ Q CV 2 and ${Q}_{\mathrm{Ext}}^{2}$ Q Ext 2 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, ${Q}_{\mathrm{CV }}^{2}$ Q CV 2 and ${Q}_{\mathrm{Ext}}^{2}$ Q Ext 2 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.https://peerj.com/articles/2322.pdfAcetylcholinesteraseAcetylcholinesterase inhibitorAlzheimer’s diseaseDementiaNeurodegenerative diseaseQuantitative structure-activity relationship |
| spellingShingle | Saw Simeon Nuttapat Anuwongcharoen Watshara Shoombuatong Aijaz Ahmad Malik Virapong Prachayasittikul Jarl E.S. Wikberg Chanin Nantasenamat Probing the origins of human acetylcholinesterase inhibition via QSAR modeling and molecular docking PeerJ Acetylcholinesterase Acetylcholinesterase inhibitor Alzheimer’s disease Dementia Neurodegenerative disease Quantitative structure-activity relationship |
| title | Probing the origins of human acetylcholinesterase inhibition via QSAR modeling and molecular docking |
| title_full | Probing the origins of human acetylcholinesterase inhibition via QSAR modeling and molecular docking |
| title_fullStr | Probing the origins of human acetylcholinesterase inhibition via QSAR modeling and molecular docking |
| title_full_unstemmed | Probing the origins of human acetylcholinesterase inhibition via QSAR modeling and molecular docking |
| title_short | Probing the origins of human acetylcholinesterase inhibition via QSAR modeling and molecular docking |
| title_sort | probing the origins of human acetylcholinesterase inhibition via qsar modeling and molecular docking |
| topic | Acetylcholinesterase Acetylcholinesterase inhibitor Alzheimer’s disease Dementia Neurodegenerative disease Quantitative structure-activity relationship |
| url | https://peerj.com/articles/2322.pdf |
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