Information-theoretical limit on the estimates of dissipation by molecular machines using single-molecule fluorescence resonance energy transfer experiments

被引:1
作者
Song, Kevin [1 ]
Makarov, Dmitrii E. [2 ,3 ]
Vouga, Etienne [1 ]
机构
[1] Univ Texas Austin, Dept Comp Sci, Austin, TX 78712 USA
[2] Univ Texas Austin, Dept Chem, Austin, TX 78712 USA
[3] Univ Texas Austin, Oden Inst Computat Engn & Sci, Austin, TX 78712 USA
基金
美国国家科学基金会;
关键词
TRANSITION PATH TIMES; DYNAMICS; FRET; ENTROPY; PHOTON; SPECTROSCOPY; DIRECTIONALITY; STATISTICS;
D O I
10.1063/5.0218040
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
Single-molecule fluorescence resonance energy transfer (FRET) experiments are commonly used to study the dynamics of molecular machines. While in vivo molecular processes often break time-reversal symmetry, the temporal directionality of cyclically operating molecular machines is often not evident from single-molecule FRET trajectories, especially in the most common two-color FRET studies. Solving a more quantitative problem of estimating the energy dissipation/entropy production by a molecular machine from single-molecule data is even more challenging. Here, we present a critical assessment of several practical methods of doing so, including Markov-model-based methods and a model-free approach based on an information-theoretical measure of entropy production that quantifies how (statistically) dissimilar observed photon sequences are from their time reverses. The Markov model approach is computationally feasible and may outperform model free approaches, but its performance strongly depends on how well the assumed model approximates the true microscopic dynamics. Markov models are also not guaranteed to give a lower bound on dissipation. Meanwhile, model-free, information-theoretical methods systematically underestimate entropy production at low photoemission rates, and long memory effects in the photon sequences make these methods demanding computationally. There is no clear winner among the approaches studied here, and all methods deserve to belong to a comprehensive data analysis toolkit.
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页数:14
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