期刊
JOURNAL OF PHYSICAL CHEMISTRY B
卷 110, 期 33, 页码 16366-16376出版社
AMER CHEMICAL SOC
DOI: 10.1021/jp063367k
关键词
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资金
- NIGMS NIH HHS [R01 GM071684-02, F32GM072328, R01GM071684, R01 GM071684-01, R01 GM071684-04, R01 GM071684-03, F32 GM072328, R01 GM071684] Funding Source: Medline
The interpretation of single-molecule measurements is greatly complicated by the presence of multiple fluorescent labels. However, many molecular systems of interest consist of multiple interacting components. We investigate this issue using multiply labeled dextran polymers that we intentionally photobleach to the background on a single-molecule basis. Hidden Markov models allow for unsupervised analysis of the data to determine the number of fluorescent subunits involved in the fluorescence intermittency of the 6-carboxy-tetramethylrhodamine labels by counting the discrete steps in fluorescence intensity. The Bayes information criterion allows us to distinguish between hidden Markov models that differ by the number of states, that is, the number of fluorescent molecules. We determine information-theoretical limits and show via Monte Carlo simulations that the hidden Markov model analysis approaches these theoretical limits. This technique has resolving power of one fluorescing unit up to as many as 30 fluorescent dyes with the appropriate choice of dye and adequate detection capability. We discuss the general utility of this method for determining aggregation-state distributions as could appear in many biologically important systems and its adaptability to general photometric experiments.
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