Centre de Biologie Structurale, CNRS UMR 5048, INSERM U1054, Univ Montpellier, 60 rue de Navacelles, 34090, Montpellier, France.
PicoQuant GmbH, Rudower Chaussee 29, 12489, Berlin, Germany.
Nat Commun. 2022 Sep 14;13(1):5402. doi: 10.1038/s41467-022-33023-3.
Single-molecule FRET (smFRET) is a versatile technique to study the dynamics and function of biomolecules since it makes nanoscale movements detectable as fluorescence signals. The powerful ability to infer quantitative kinetic information from smFRET data is, however, complicated by experimental limitations. Diverse analysis tools have been developed to overcome these hurdles but a systematic comparison is lacking. Here, we report the results of a blind benchmark study assessing eleven analysis tools used to infer kinetic rate constants from smFRET trajectories. We test them against simulated and experimental data containing the most prominent difficulties encountered in analyzing smFRET experiments: different noise levels, varied model complexity, non-equilibrium dynamics, and kinetic heterogeneity. Our results highlight the current strengths and limitations in inferring kinetic information from smFRET trajectories. In addition, we formulate concrete recommendations and identify key targets for future developments, aimed to advance our understanding of biomolecular dynamics through quantitative experiment-derived models.
单分子荧光共振能量转移(smFRET)是一种研究生物分子动力学和功能的多功能技术,因为它可以将纳米级运动转化为荧光信号进行检测。然而,从 smFRET 数据中推断定量动力学信息的强大能力受到实验限制的影响。已经开发出各种分析工具来克服这些障碍,但缺乏系统的比较。在这里,我们报告了一项盲基准研究的结果,该研究评估了十一种用于从 smFRET 轨迹推断动力学速率常数的分析工具。我们针对包含在分析 smFRET 实验中最突出困难的模拟和实验数据对它们进行了测试:不同的噪声水平、不同的模型复杂性、非平衡动力学和动力学异质性。我们的结果突出了从 smFRET 轨迹推断动力学信息的当前优势和局限性。此外,我们提出了具体的建议,并确定了未来发展的关键目标,旨在通过定量实验衍生模型来推进我们对生物分子动力学的理解。