• Spectroscopy and Spectral Analysis
  • Vol. 37, Issue 1, 13 (2017)
WU Xi, PEI Xiao-jing, LIN Ruo-yun, LIU Feng, and LI Na
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  • [in Chinese]
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    DOI: 10.3964/j.issn.1000-0593(2017)01-0013-08 Cite this Article
    WU Xi, PEI Xiao-jing, LIN Ruo-yun, LIU Feng, LI Na. Fluorescence Anisotropy and Applications Based on Functional Nucleic Acid Recognition[J]. Spectroscopy and Spectral Analysis, 2017, 37(1): 13 Copy Citation Text show less

    Abstract

    Based on the difference of the depolarization of the emitted light before and after the interaction of molecules, fluorescence anisotropy, also known as fluorescence polarization, can be beneficial for the study of interactions and the detection of the targets. In 1950s, Gregorio Weber first studied the interactions between dansyl chloride and bovine serum albumin or ovalbumin with fluorescence anisotropy method, which paved the way for fluorescence anisotropy in biochemical applications. Since the early 1990s, functional nucleic acids (FNAs, including aptamers, and nucleic acid enzymes) were discovered and synthesized, which have been widely used in functional nucleic acid-based sensing. Aptamers are oligonucleotides that can recognize and bind specifically to various molecular targets. The fluorescence anisotropy methods which are based on aptamer recognition have the advantages of high selectivity, sensitivity and through-put. They play an important role in the study of interaction with protein, nucleic acids and small molecules. However, the way of enhancing the fluorescence anisotropy change of small molecules associated with the binding events is challenging. This paper reviews the basic principles and designs of fluorescence anisotropy methods bases on functional nucleic acid recognition for study and detection of proteins, nucleic acids and small molecules that play an important role in the life activity.
    WU Xi, PEI Xiao-jing, LIN Ruo-yun, LIU Feng, LI Na. Fluorescence Anisotropy and Applications Based on Functional Nucleic Acid Recognition[J]. Spectroscopy and Spectral Analysis, 2017, 37(1): 13
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