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Estimation of Kinetic Parameters of ...

Illinois Institute of Technology.

Estimation of Kinetic Parameters of the Coronavirus Main Protease by Bayesian Regression and Utility for Drug Design /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Estimation of Kinetic Parameters of the Coronavirus Main Protease by Bayesian Regression and Utility for Drug Design // Van Ngoc Thuy La.
作者:	La, Van Ngoc Thuy,
面頁冊數:	1 electronic resource (101 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-06, Section: B.
提要註:	The main protease (MPro) plays a crucial role in the Coronavirus life cycle and is a target for newly developed antivirals against SARS-CoV-2. MPro is conserved across various members of the coronavirus family. The enzyme of SARS-CoV-2 and other members of the coronavirus family share similar structures and functions, commonly existing as obligate dimers. However, MPro from MERS-CoV exhibits weaker dimerization and often exists as a monomer under biochemical assay conditions, which may not accurately reflect the conditions relevant to antiviral therapy in infected cells.Interestingly, because ligand binding increases dimerization, the addition of ligands has been reported to enhance MPro activity at low concentrations before reducing it at higher concentrations. This phenomenon, known as ligand-induced dimerization, was observed not only in biochemical assays of MERS-CoV MPro but also of SARS-CoV-2 mutated MPro with weakened dimerization. Unfortunately, there are currently no published biochemical models that quantitatively fit these non-monotonic concentration-response curves. This poses a significant challenge in estimating IC50 from these curves, which is an important metric for drug potency and commonly used in drug screening. As a result, predicting compound behavior in cellular models becomes challenging.To address this challenge, we developed an enzyme kinetic model that integrates dimerization and ligand binding. We utilized Bayesian regression for the model to fit datasets published in the aforementioned study of SARS-CoV-2 MPro. Subsequently, we adjusted the model to achieve a global fit for multiple datasets of MERS-CoV MPro and estimate IC50 values. Finally, we examined the correlation between estimated IC50 and cellular EC50, demonstrating that our model is capable of predicting cellular EC50 for the biphasic curves observed in MERS-CoV MPro.
Contained By:	Dissertations Abstracts International86-06B.
標題:	Virology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31639722
ISBN:	9798346859161

Estimation of Kinetic Parameters of the Coronavirus Main Protease by Bayesian Regression and Utility for Drug Design /
La, Van Ngoc Thuy,

Estimation of Kinetic Parameters of the Coronavirus Main Protease by Bayesian Regression and Utility for Drug Design /Van Ngoc Thuy La. - 1 electronic resource (101 pages)

Source: Dissertations Abstracts International, Volume: 86-06, Section: B.

The main protease (MPro) plays a crucial role in the Coronavirus life cycle and is a target for newly developed antivirals against SARS-CoV-2. MPro is conserved across various members of the coronavirus family. The enzyme of SARS-CoV-2 and other members of the coronavirus family share similar structures and functions, commonly existing as obligate dimers. However, MPro from MERS-CoV exhibits weaker dimerization and often exists as a monomer under biochemical assay conditions, which may not accurately reflect the conditions relevant to antiviral therapy in infected cells.Interestingly, because ligand binding increases dimerization, the addition of ligands has been reported to enhance MPro activity at low concentrations before reducing it at higher concentrations. This phenomenon, known as ligand-induced dimerization, was observed not only in biochemical assays of MERS-CoV MPro but also of SARS-CoV-2 mutated MPro with weakened dimerization. Unfortunately, there are currently no published biochemical models that quantitatively fit these non-monotonic concentration-response curves. This poses a significant challenge in estimating IC50 from these curves, which is an important metric for drug potency and commonly used in drug screening. As a result, predicting compound behavior in cellular models becomes challenging.To address this challenge, we developed an enzyme kinetic model that integrates dimerization and ligand binding. We utilized Bayesian regression for the model to fit datasets published in the aforementioned study of SARS-CoV-2 MPro. Subsequently, we adjusted the model to achieve a global fit for multiple datasets of MERS-CoV MPro and estimate IC50 values. Finally, we examined the correlation between estimated IC50 and cellular EC50, demonstrating that our model is capable of predicting cellular EC50 for the biphasic curves observed in MERS-CoV MPro.

English

ISBN: 9798346859161Subjects--Topical Terms:

187922
Virology.
Subjects--Index Terms:

Bayesian regression

Estimation of Kinetic Parameters of the Coronavirus Main Protease by Bayesian Regression and Utility for Drug Design /
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520 $a The main protease (MPro) plays a crucial role in the Coronavirus life cycle and is a target for newly developed antivirals against SARS-CoV-2. MPro is conserved across various members of the coronavirus family. The enzyme of SARS-CoV-2 and other members of the coronavirus family share similar structures and functions, commonly existing as obligate dimers. However, MPro from MERS-CoV exhibits weaker dimerization and often exists as a monomer under biochemical assay conditions, which may not accurately reflect the conditions relevant to antiviral therapy in infected cells.Interestingly, because ligand binding increases dimerization, the addition of ligands has been reported to enhance MPro activity at low concentrations before reducing it at higher concentrations. This phenomenon, known as ligand-induced dimerization, was observed not only in biochemical assays of MERS-CoV MPro but also of SARS-CoV-2 mutated MPro with weakened dimerization. Unfortunately, there are currently no published biochemical models that quantitatively fit these non-monotonic concentration-response curves. This poses a significant challenge in estimating IC50 from these curves, which is an important metric for drug potency and commonly used in drug screening. As a result, predicting compound behavior in cellular models becomes challenging.To address this challenge, we developed an enzyme kinetic model that integrates dimerization and ligand binding. We utilized Bayesian regression for the model to fit datasets published in the aforementioned study of SARS-CoV-2 MPro. Subsequently, we adjusted the model to achieve a global fit for multiple datasets of MERS-CoV MPro and estimate IC50 values. Finally, we examined the correlation between estimated IC50 and cellular EC50, demonstrating that our model is capable of predicting cellular EC50 for the biphasic curves observed in MERS-CoV MPro.
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