Predicting AAK1/GAK dual-target inhibitor against SARS-CoV-2 viral entry into host cells: an in silico approach / Xavier Chee Wezen ...[et.al.]
Clathrin-mediated endocytosis (CME) is a normal biological process where cellular contents are transported into the cells.However, this process is often hijacked by different viruses to enter host cells and cause infections. Recently, two proteins that regulate CME – AAK1 and GAK – have been propose...
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my.uitm.ir.634452022-07-05T08:24:21Z https://ir.uitm.edu.my/id/eprint/63445/ Predicting AAK1/GAK dual-target inhibitor against SARS-CoV-2 viral entry into host cells: an in silico approach / Xavier Chee Wezen ...[et.al.] Wezen, Xavier Chee Clement, Sim Jun Wen Yung Ping, Lilian Siaw Yeong, Kah Ho Qing, Kong Hao Ha, Christopher San, Hwang Siaw Q Science (General) QH Natural history - Biology Biology R Medicine (General) Clathrin-mediated endocytosis (CME) is a normal biological process where cellular contents are transported into the cells.However, this process is often hijacked by different viruses to enter host cells and cause infections. Recently, two proteins that regulate CME – AAK1 and GAK – have been proposed as potential therapeutic targets for designing broad-spectrum antiviral drugs. In this work, we curated two compound datasets containing 83 AAK1 inhibitors and 196 GAK inhibitors each. Subsequently, machine learning methods,namely Random Forest, Elastic Net and Sequential Minimal Optimization, were used to construct Quantitative StructureActivity Relationship (QSAR) models to predict small molecule inhibitors of AAK1 and GAK. To ensure predictivity, these models were evaluated by using Leave-One-Out (LOO)= cross validation and with an external test set. In all cases, our QSAR models achieved a q2 LOO in range of 0.64 to 0.84 (Root Mean Squared Error; RMSE = 0.41 to 0.52) and a q2 ext in range of 0.57 to 0.92 (RMSE = 0.36 to 0.61). Besides, our QSAR models were evaluated by using additional QSAR performance metrics and y-randomization test. Finally, by using a concensus scoring approach, nine chemical compounds from the Drugbank compound library were predicted as AAK1/GAK dual-target inhibitors. The electrostatic potential maps for the nine compounds were generated and compared against two known dual-target inhibitors, sunitinib and baricitinib. Our work provides the rationale to validate these nine compounds experimentally against the protein targets AAK1 and GAK. UiTM Press 2021-09 Article PeerReviewed text en https://ir.uitm.edu.my/id/eprint/63445/1/63445.pdf Predicting AAK1/GAK dual-target inhibitor against SARS-CoV-2 viral entry into host cells: an in silico approach / Xavier Chee Wezen ...[et.al.]. (2021) Journal of Smart Science and Technology, 1 (1): 5. pp. 48-67. ISSN 2785-924x https://jsst.uitm.edu.my/index.php/jsst/issue/view/1 |
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Q Science (General) QH Natural history - Biology Biology R Medicine (General) Wezen, Xavier Chee Clement, Sim Jun Wen Yung Ping, Lilian Siaw Yeong, Kah Ho Qing, Kong Hao Ha, Christopher San, Hwang Siaw Predicting AAK1/GAK dual-target inhibitor against SARS-CoV-2 viral entry into host cells: an in silico approach / Xavier Chee Wezen ...[et.al.] |
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Clathrin-mediated endocytosis (CME) is a normal biological process where cellular contents are transported into the cells.However, this process is often hijacked by different viruses to enter host cells and cause infections. Recently, two proteins that regulate CME – AAK1 and GAK – have been proposed
as potential therapeutic targets for designing broad-spectrum antiviral drugs. In this work, we curated two compound datasets containing 83 AAK1 inhibitors and 196 GAK inhibitors each. Subsequently, machine learning methods,namely Random Forest, Elastic Net and Sequential Minimal Optimization, were used to construct Quantitative StructureActivity Relationship (QSAR) models to predict small molecule inhibitors of AAK1 and GAK. To ensure predictivity,
these models were evaluated by using Leave-One-Out (LOO)= cross validation and with an external test set. In all cases, our QSAR models achieved a q2
LOO in range of 0.64 to 0.84 (Root Mean Squared Error; RMSE = 0.41 to 0.52) and a q2 ext in range of 0.57 to 0.92 (RMSE = 0.36 to 0.61). Besides, our
QSAR models were evaluated by using additional QSAR performance metrics and y-randomization test. Finally, by using a concensus scoring approach, nine chemical compounds from the Drugbank compound library were predicted as AAK1/GAK dual-target inhibitors. The electrostatic potential maps for the nine compounds were generated and compared against two known dual-target inhibitors, sunitinib and baricitinib. Our work provides the rationale to validate these nine compounds experimentally against the protein targets AAK1 and GAK. |
format |
Article |
author |
Wezen, Xavier Chee Clement, Sim Jun Wen Yung Ping, Lilian Siaw Yeong, Kah Ho Qing, Kong Hao Ha, Christopher San, Hwang Siaw |
author_facet |
Wezen, Xavier Chee Clement, Sim Jun Wen Yung Ping, Lilian Siaw Yeong, Kah Ho Qing, Kong Hao Ha, Christopher San, Hwang Siaw |
author_sort |
Wezen, Xavier Chee |
title |
Predicting AAK1/GAK dual-target inhibitor against SARS-CoV-2 viral entry into host cells: an in silico approach / Xavier Chee Wezen ...[et.al.] |
title_short |
Predicting AAK1/GAK dual-target inhibitor against SARS-CoV-2 viral entry into host cells: an in silico approach / Xavier Chee Wezen ...[et.al.] |
title_full |
Predicting AAK1/GAK dual-target inhibitor against SARS-CoV-2 viral entry into host cells: an in silico approach / Xavier Chee Wezen ...[et.al.] |
title_fullStr |
Predicting AAK1/GAK dual-target inhibitor against SARS-CoV-2 viral entry into host cells: an in silico approach / Xavier Chee Wezen ...[et.al.] |
title_full_unstemmed |
Predicting AAK1/GAK dual-target inhibitor against SARS-CoV-2 viral entry into host cells: an in silico approach / Xavier Chee Wezen ...[et.al.] |
title_sort |
predicting aak1/gak dual-target inhibitor against sars-cov-2 viral entry into host cells: an in silico approach / xavier chee wezen ...[et.al.] |
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UiTM Press |
publishDate |
2021 |
url |
https://ir.uitm.edu.my/id/eprint/63445/1/63445.pdf https://ir.uitm.edu.my/id/eprint/63445/ https://jsst.uitm.edu.my/index.php/jsst/issue/view/1 |
_version_ |
1738514024494006272 |