Adenekan Oyinkansola, Kasson Peter M
Department of Biomedical Engineering, University of Virginia, Charlottesville VA 22903.
Departments of Chemistry & Biochemistry and Biomedical Engineering, Georgia Institute of Technology, Atlanta GA 30332.
bioRxiv. 2025 May 23:2025.05.18.654751. doi: 10.1101/2025.05.18.654751.
Single-event completion times, such as are estimated in viral entry, offer both promise and challenge to kinetic interpretation. The promise is that they are able to constrain underlying kinetic models much more efficiently than bulk kinetics, but the challenge is that completion times alone can incompletely determine complex reaction topologies. Gamma distributions or mechanistic models have often been used to estimate kinetic parameters for such data, but the gamma distribution relies on homogenous processes contributing to the rate-limiting behavior of the system. Here, we introduce hypoexponential analysis to estimate heterogeneous kinetic processes. We demonstrate that hypoexponential fitting can indeed estimate rate constants separated by 2-3 orders of magnitude. We then apply this approach to measurements of SARS-CoV-2 entry, showing that ACE2 reduces the number of rate-limiting steps but does not change the rates of these kinetic processes. We propose a kinetic model whereby SARS-CoV-2 entry is driven by a mixture of ACE2-accelerated and ACE2-independent spike protein activation events. Inferring such models requires the capability to detect heterogeneous kinetic processes, provided by robust estimation of hypoexponential distributions.
单事件完成时间，如在病毒进入过程中所估计的那样，给动力学解释带来了机遇与挑战。机遇在于，与整体动力学相比，它们能够更有效地约束潜在的动力学模型，但挑战在于，仅完成时间本身无法完全确定复杂的反应拓扑结构。伽马分布或机理模型常常被用于估计此类数据的动力学参数，但伽马分布依赖于对系统限速行为有贡献的均匀过程。在此，我们引入次指数分析来估计非均匀动力学过程。我们证明，次指数拟合确实能够估计相差2至3个数量级的速率常数。然后，我们将此方法应用于对严重急性呼吸综合征冠状病毒2（SARS-CoV-2）进入的测量，结果表明血管紧张素转换酶2（ACE2）减少了限速步骤的数量，但并未改变这些动力学过程的速率。我们提出了一个动力学模型，据此SARS-CoV-2的进入是由ACE2加速的和不依赖ACE2的刺突蛋白激活事件的混合驱动的。推断此类模型需要具备检测非均匀动力学过程的能力，而这由次指数分布的稳健估计提供。
