Verma Anjali R, Ray Korak Kumar, Bodick Maya, Kinz-Thompson Colin D, Gonzalez Ruben L
Department of Chemistry, Columbia University, New York, NY 10027 USA.
Department of Chemistry, Rutgers University-Newark, Newark, NJ 07102 USA.
bioRxiv. 2024 Jan 21:2023.08.15.553409. doi: 10.1101/2023.08.15.553409.
Time-dependent single-molecule experiments contain rich kinetic information about the functional dynamics of biomolecules. A key step in extracting this information is the application of kinetic models, such as hidden Markov models (HMMs), which characterize the molecular mechanism governing the experimental system. Unfortunately, researchers rarely know the physico-chemical details of this molecular mechanism which raises questions about how to select the most appropriate kinetic model for a given single-molecule dataset and what consequences arise if the wrong model is chosen. To address these questions, we have developed and used time-series Modeling, Analysis, and Visualization ENvironment (tMAVEN), a comprehensive, open-source, and extensible software platform. tMAVEN can perform each step of the single-molecule analysis pipeline, from pre-processing to kinetic modeling to plotting, and has been designed to enable the analysis of a single-molecule dataset with multiple types of kinetic models. Using tMAVEN, we have systematically investigated mismatches between kinetic models and molecular mechanisms by analyzing simulated examples of prototypical single-molecule datasets exhibiting common experimental complications, such as molecular heterogeneity, with a series of different types of HMMs. Our results show that no single kinetic modeling strategy is mathematically appropriate for all experimental contexts. Indeed, HMMs only correctly capture the underlying molecular mechanism in the simplest of cases. As such, researchers must modify HMMs using physico-chemical principles to avoid the risk of missing the significant biological and biophysical insights into molecular heterogeneity that their experiments provide. By enabling the facile, side-by-side application of multiple types of kinetic models to individual single-molecule datasets, tMAVEN allows researchers to carefully tailor their modeling approach to match the complexity of the underlying biomolecular dynamics and increase the accuracy of their single-molecule data analyses.
时间分辨单分子实验包含有关生物分子功能动力学的丰富动力学信息。提取此信息的关键步骤是应用动力学模型，如隐马尔可夫模型（HMM），其可表征控制实验系统的分子机制。不幸的是，研究人员很少了解这种分子机制的物理化学细节，这就引发了关于如何为给定的单分子数据集选择最合适的动力学模型以及如果选择了错误的模型会产生什么后果的问题。为了解决这些问题，我们开发并使用了时间序列建模、分析和可视化环境（tMAVEN），这是一个全面、开源且可扩展的软件平台。tMAVEN可以执行单分子分析流程的每个步骤，从预处理到动力学建模再到绘图，并且设计用于使用多种类型的动力学模型分析单分子数据集。使用tMAVEN，我们通过分析一系列具有不同类型HMM的典型单分子数据集的模拟示例，系统地研究了动力学模型与分子机制之间的不匹配情况，这些示例展示了常见的实验复杂性，如分子异质性。我们的结果表明，没有一种单一的动力学建模策略在数学上适用于所有实验情况。实际上，HMM仅在最简单的情况下才能正确捕获潜在的分子机制。因此，研究人员必须使用物理化学原理修改HMM，以避免错过其实验所提供的关于分子异质性的重要生物学和生物物理见解的风险。通过允许将多种类型的动力学模型轻松地并行应用于单个单分子数据集，tMAVEN使研究人员能够精心调整其建模方法，以匹配潜在生物分子动力学的复杂性，并提高其单分子数据分析的准确性。
