Safar Matthew, Saurabh Ayush, Sarkar Bidyut, Fazel Mohamadreza, Ishii Kunihiko, Tahara Tahei, Sgouralis Ioannis, Pressé Steve
Center for Biological Physics, Arizona State University, Tempe, Arizona.
Department of Mathematics and Statistical Science, Arizona State University, Tempe, Arizona.
Biophys Rep (N Y). 2022 Nov 25;2(4):100088. doi: 10.1016/j.bpr.2022.100088. eCollection 2022 Dec 14.
Förster resonance energy transfer (FRET) using pulsed illumination has been pivotal in leveraging lifetime information in FRET analysis. However, there remain major challenges in quantitative single-photon, single-molecule FRET (smFRET) data analysis under pulsed illumination including 1) simultaneously deducing kinetics and number of system states; 2) providing uncertainties over estimates, particularly uncertainty over the number of system states; and 3) taking into account detector noise sources such as cross talk and the instrument response function contributing to uncertainty; in addition to 4) other experimental noise sources such as background. Here, we implement the Bayesian nonparametric framework described in the first companion article that addresses all aforementioned issues in smFRET data analysis specialized for the case of pulsed illumination. Furthermore, we apply our method to both synthetic as well as experimental data acquired using Holliday junctions.
利用脉冲照明的Förster共振能量转移（FRET）在FRET分析中利用寿命信息方面一直起着关键作用。然而，在脉冲照明下的定量单光子、单分子FRET（smFRET）数据分析中仍存在重大挑战，包括：1）同时推导动力学和系统状态数；2）给出估计值的不确定性，特别是系统状态数的不确定性；3）考虑探测器噪声源，如串扰和仪器响应函数对不确定性的影响；此外还有4）其他实验噪声源，如背景。在此，我们实施第一篇配套文章中描述的贝叶斯非参数框架，该框架解决了专门针对脉冲照明情况的smFRET数据分析中上述所有问题。此外，我们将我们的方法应用于使用霍利迪连接体获得的合成数据和实验数据。
