Error-based extraction of states and energy landscapes from experimental single-molecule time-series.
Error-based extraction of states and energy landscapes from experimental single-molecule time-series.
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DOI：
10.1038/srep09174
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发表时间：
2015-03-17
影响因子：
4.6
通讯作者：
Komatsuzaki T
中科院分区：
文献类型：
--
作者：
Taylor JN;Li CB;Cooper DR;Landes CF;Komatsuzaki T
由于测量中固有的噪声的存在，状态的表征是单分子(SM)实验中最有趣的主题之一，它是基本能量景观的基本组成部分。这里我们提出了一种从实验SM时间序列中提取潜在状态序列的方法。考虑到经验误差和时间序列的有限抽样，该方法提取了一个提供基本有效自由能图景的近似的稳态网络。该方法的核心是应用信息论中的率失真理论，允许将单个数据点同时分配到多个状态。我们展示了该方法在模拟轨迹以及实验SM荧光共振能量转移(FRET)轨迹中的熟练应用，该轨迹是从AMPA受体的分离激动剂结合域获得的，AMPA受体是一种普遍存在于中枢神经系统的离子型谷氨酸受体。
Characterization of states, the essential components of the underlying energy landscapes, is one of the most intriguing subjects in single-molecule (SM) experiments due to the existence of noise inherent to the measurements. Here we present a method to extract the underlying state sequences from experimental SM time-series. Taking into account empirical error and the finite sampling of the time-series, the method extracts a steady-state network which provides an approximation of the underlying effective free energy landscape. The core of the method is the application of rate-distortion theory from information theory, allowing the individual data points to be assigned to multiple states simultaneously. We demonstrate the method's proficiency in its application to simulated trajectories as well as to experimental SM fluorescence resonance energy transfer (FRET) trajectories obtained from isolated agonist binding domains of the AMPA receptor, an ionotropic glutamate receptor that is prevalent in the central nervous system.
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影响因子：
2.9
作者：
Andrec, M;Levy, RM;Talaga, DS
通讯作者：
Talaga, DS
影响因子：
4.4
作者：
Becker, OM;Karplus, M
通讯作者：
Karplus, M
影响因子：
2.5
作者：
ARIMOTO, S
通讯作者：
ARIMOTO, S
DOI：
10.1073/pnas.1318405111
发表时间：
2014-02-11
影响因子：
11.1
作者：
Kisley, Lydia;Chen, Jixin;Landes, Christy F.
通讯作者：
Landes, Christy F.
影响因子：
8.6
作者：
Harris, Nolan C.;Song, Yang;Kiang, Ching-Hwa
通讯作者：
Kiang, Ching-Hwa