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多尺度响应动力学建模:量化不同电化学条件下的生物分子反应通量。

Multiscale Responsive Kinetic Modeling: Quantifying Biomolecular Reaction Flux under Varying Electrochemical Conditions.

作者信息

Weckel-Dahman Hannah, Carlsen Ryan, Swanson Jessica M J

机构信息

Department of Chemistry, University of Utah, Salt Lake City, UT, 84112 - United States of America.

出版信息

bioRxiv. 2024 Aug 2:2024.08.01.606205. doi: 10.1101/2024.08.01.606205.

DOI:10.1101/2024.08.01.606205
PMID:39131358
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11312519/
Abstract

Attaining a complete thermodynamic and kinetic characterization for processes involving multiple interconnected rare-event transitions remains a central challenge in molecular biophysics. This challenge is amplified when the process must be understood under a range of reaction conditions. Herein, we present a condition-responsive kinetic modeling framework that can combine the strengths of bottom-up rate quantification from multiscale simulations with top-down solution refinement using experimental data. Although this framework can be applied to any process, we demonstrate its use for electrochemically driven transport through channels and transporters. Using the Cl /H antiporter ClC-ec1 as a model system, we show how robust and predictive kinetic solutions can be obtained when the solution space is grounded by thermodynamic constraints, seeded through multiscale rate quantification, and further refined with experimental data, such as electrophysiology assays. Turning to the Shaker K channel, we demonstrate that robust solutions and biophysical insights can also be obtained with sufficient experimental data. This multi-pathway method proves capable of identifying single-pathway dominant mechanisms but also highlights that competing and off-pathway flux is still essential to replicate experimental findings and to describe concentration-dependent channel rectification.

摘要

对于涉及多个相互关联的罕见事件转变的过程,获得完整的热力学和动力学表征仍然是分子生物物理学中的一个核心挑战。当必须在一系列反应条件下理解该过程时,这一挑战会被放大。在此,我们提出了一个条件响应动力学建模框架,该框架可以将多尺度模拟中自下而上的速率量化优势与使用实验数据进行自上而下的解决方案优化相结合。尽管该框架可应用于任何过程,但我们展示了其在通过通道和转运体的电化学驱动运输中的应用。以Cl⁻/H⁺反向转运体ClC-ec1作为模型系统,我们展示了在通过热力学约束确定解空间、通过多尺度速率量化进行种子设定并使用诸如电生理学测定等实验数据进一步优化时,如何获得稳健且具有预测性的动力学解决方案。对于Shaker钾通道,我们证明了在有足够实验数据的情况下也能获得稳健的解决方案和生物物理见解。这种多途径方法被证明能够识别单途径主导机制,但也突出表明,竞争通量和非途径通量对于复制实验结果和描述浓度依赖性通道整流仍然至关重要。

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