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包含二聚化和配体结合的冠状病毒主要蛋白酶的酶动力学模型。

Enzyme kinetics model for the coronavirus main protease including dimerization and ligand binding.

作者信息

Thuy La Van Ngoc, Kang Lulu, Minh David D L

机构信息

Department of Biology, Illinois Institute of Technology, Chicago, IL 60616, USA.

Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA, 01003, USA.

出版信息

bioRxiv. 2025 Jan 2:2025.01.01.631001. doi: 10.1101/2025.01.01.631001.

DOI:10.1101/2025.01.01.631001
PMID:39803422
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11722214/
Abstract

The coronavirus main protease (MPro) plays a pivotal role in viral replication and is the target of several antivirals against SARS-CoV-2. In some species, CRCs of MPro enzymatic activity can exhibit biphasic behavior in which low ligand concentrations activate the enzyme whereas higher ones inhibit it. While this behavior has been attributed to ligand-induced dimerization, quantitative enzyme kinetics models have not been fit to it. Here, we develop a kinetic model integrating dimerization and ligand binding. We perform a Bayesian regression to globally fit the model to multiple types of biochemical and biophysical data. The reversible covalent inhibitor GC376 strongly induces dimerization and binds to the dimer with no cooperativity. In contrast, the fluorescent peptide substrate has a minor effect on dimerization but binds to the dimer with positive cooperativity. The biphasic concentration response curve occurs because compared to substrate, the inhibitor accelerates turnover in the opposite catalytic site.

摘要

冠状病毒主要蛋白酶(MPro)在病毒复制中起关键作用,是多种抗SARS-CoV-2抗病毒药物的靶点。在某些物种中,MPro酶活性的浓度响应曲线(CRCs)可能表现出双相行为,即低浓度配体激活该酶,而高浓度配体则抑制它。虽然这种行为被归因于配体诱导的二聚化,但尚未有定量酶动力学模型与之拟合。在此,我们开发了一个整合二聚化和配体结合的动力学模型。我们进行贝叶斯回归,将该模型整体拟合到多种类型的生化和生物物理数据。可逆共价抑制剂GC376强烈诱导二聚化,且无协同性地结合到二聚体上。相比之下,荧光肽底物对二聚化影响较小,但以正协同性结合到二聚体上。出现双相浓度响应曲线的原因是,与底物相比,抑制剂在相反的催化位点加速了周转。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6386/11722214/4f196fc573bf/nihpp-2025.01.01.631001v1-f0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6386/11722214/816052d37669/nihpp-2025.01.01.631001v1-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6386/11722214/089638f729b8/nihpp-2025.01.01.631001v1-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6386/11722214/0e0cb5ad11b2/nihpp-2025.01.01.631001v1-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6386/11722214/ed6307ce1498/nihpp-2025.01.01.631001v1-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6386/11722214/e6161d0bfea6/nihpp-2025.01.01.631001v1-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6386/11722214/4f196fc573bf/nihpp-2025.01.01.631001v1-f0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6386/11722214/816052d37669/nihpp-2025.01.01.631001v1-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6386/11722214/089638f729b8/nihpp-2025.01.01.631001v1-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6386/11722214/0e0cb5ad11b2/nihpp-2025.01.01.631001v1-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6386/11722214/ed6307ce1498/nihpp-2025.01.01.631001v1-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6386/11722214/e6161d0bfea6/nihpp-2025.01.01.631001v1-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6386/11722214/4f196fc573bf/nihpp-2025.01.01.631001v1-f0006.jpg

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Bayesian Regression Quantifies Uncertainty of Binding Parameters from Isothermal Titration Calorimetry More Accurately Than Error Propagation.贝叶斯回归比误差传播更准确地量化了等温滴定量热法中结合参数的不确定性。
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