Blanco Mario, Walter Nils G
Department of Chemistry, University of Michigan, Ann Arbor, Michigan, USA.
Methods Enzymol. 2010;472:153-78. doi: 10.1016/S0076-6879(10)72011-5.
Single-molecule methods have given researchers the ability to investigate the structural dynamics of biomolecules at unprecedented resolution and sensitivity. One of the preferred methods of studying single biomolecules is single-molecule fluorescence resonance energy transfer (smFRET). The popularity of smFRET stems from its ability to report on dynamic, either intra- or intermolecular interactions in real-time. For example, smFRET has been successfully used to characterize the role of dynamics in functional RNAs and their protein complexes, including ribozymes, the ribosome, and more recently the spliceosome. Being able to reliably extract quantitative kinetic and conformational parameters from smFRET experiments is crucial for the interpretation of their results. The need for efficient, unbiased analysis routines becomes more evident as the systems studied become more complex. In this chapter, we focus on the practical utility of statistical algorithms, particularly hidden Markov models, to aid in the objective quantification of complex smFRET trajectories with three or more discrete states, and to extract kinetic information from the trajectories. Additionally, we present a method for systematically eliminating transitions associated with uncorrelated fluorophore behavior that may occur due to dye anisotropy and quenching effects. We also highlight the importance of data condensation through the use of various transition density plots to fully understand the underlying conformational dynamics and kinetic behavior of the biological macromolecule of interest under varying conditions. Finally, the application of these techniques to studies of pre-mRNA conformational changes during eukaryotic splicing is discussed.
单分子方法使研究人员能够以前所未有的分辨率和灵敏度研究生物分子的结构动力学。研究单个生物分子的首选方法之一是单分子荧光共振能量转移（smFRET）。smFRET的流行源于其能够实时报告分子内或分子间的动态相互作用。例如，smFRET已成功用于表征动力学在功能性RNA及其蛋白质复合物中的作用，包括核酶、核糖体，以及最近的剪接体。能够从smFRET实验中可靠地提取定量动力学和构象参数对于解释其结果至关重要。随着所研究的系统变得更加复杂，对高效、无偏分析程序的需求变得更加明显。在本章中，我们重点关注统计算法，特别是隐马尔可夫模型的实际应用，以帮助客观量化具有三个或更多离散状态的复杂smFRET轨迹，并从轨迹中提取动力学信息。此外，我们提出了一种系统地消除与可能由于染料各向异性和猝灭效应而出现的不相关荧光团行为相关的跃迁的方法。我们还强调了通过使用各种跃迁密度图进行数据浓缩的重要性，以全面了解感兴趣的生物大分子在不同条件下的潜在构象动力学和动力学行为。最后，讨论了这些技术在真核生物剪接过程中前体mRNA构象变化研究中的应用。
