Literature DB >> 23811334

Estimating the distance separating fluorescent protein FRET pairs.

Steven S Vogel1, B Wieb van der Meer2, Paul S Blank3.   

Abstract

Förster resonance energy transfer (FRET) describes a physical phenomenon widely applied in biomedical research to estimate separations between biological molecules. Routinely, genetic engineering is used to incorporate spectral variants of the green fluorescent protein (GFPs), into cellular expressed proteins. The transfer efficiency or rate of energy transfer between donor and acceptor FPs is then assayed. As appreciable FRET occurs only when donors and acceptors are in close proximity (1-10nm), the presence of FRET may indicate that the engineered proteins associate as interacting species. For a homogeneous population of FRET pairs the separations between FRET donors and acceptors can be estimated from a measured FRET efficiency if it is assumed that donors and acceptors are randomly oriented and rotate extensively during their excited state (dynamic regime). Unlike typical organic fluorophores, the rotational correlation-times of FPs are typically much longer than their fluorescence lifetime; accordingly FPs are virtually static during their excited state. Thus, estimating separations between FP FRET pairs is problematic. To overcome this obstacle, we present here a simple method for estimating separations between FPs using the experimentally measured average FRET efficiency. This approach assumes that donor and acceptor fluorophores are randomly oriented, but do not rotate during their excited state (static regime). This approach utilizes a Monte-Carlo simulation generated look-up table that allows one to estimate the separation, normalized to the Förster distance, from the average FRET efficiency. Assuming a dynamic regime overestimates the separation significantly (by 10% near 0.5 and 30% near 0.75 efficiencies) compared to assuming a static regime, which is more appropriate for estimates of separations between FPs.
Copyright © 2013. Published by Elsevier Inc.

Entities:  

Keywords:  GFP; Kappa squared

Mesh:

Substances:

Year:  2013        PMID: 23811334      PMCID: PMC3964137          DOI: 10.1016/j.ymeth.2013.06.021

Source DB:  PubMed          Journal:  Methods        ISSN: 1046-2023            Impact factor:   3.608


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