Abstract

Wavelength-selective fluorescence comprises a set of approaches based on the red edge effect in fluorescence spectroscopy, which can be used to monitor directly the environment and dynamics around a fluorophore in a complex biological system. A shift in the wavelength of maximum fluorescence emission toward higher wavelengths, caused by a shift in the excitation wavelength toward the red edge of the absorption band, is termed red edge excitation shift (REES). This effect is mostly observed with polar fluorophores in motionally restricted media such as very viscous solutions or condensed phases where the dipolar relaxation time for the solvent shell around a fluorophore is comparable to or longer than its fluorescence lifetime. REES arises from slow rates of solvent relaxation (reorientation) around an excited state fluorophore, which is a function of the motional restriction imposed on the solvent molecules in the immediate vicinity of the fluorophore. Utilizing this approach, it becomes possible to probe the mobility parameters of the environment itself (which is represented by the relaxing solvent molecules) using the fluorophore merely as a reporter group. Furthermore, since the ubiquitous solvent for biological systems is water, the information obtained in such cases will come from the otherwise “optically silent” water molecules. This makes REES and related techniques extremely useful since hydration plays a crucial modulatory role in a large number of important cellular events including lipid-protein interactions and ion transport. The application of REES and related techniques (wavelength-selective fluorescence approach) as a powerful tool to monitor organization and dynamics of probes and peptides bound to membranes and membrane-mimetic medium such as micelles is discussed.KeywordsTryptophan ResidueSolvent RelaxationBulk Aqueous PhaseExcited State Dipole MomentRestricted MediumThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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