This title appears in the Scientific Report :
2018
Please use the identifier:
http://dx.doi.org/10.1021/acssensors.8b00143 in citations.
Genetically Encoded Förster Resonance Energy Transfer-Based Biosensors Studied on the Single-Molecule Level
Genetically Encoded Förster Resonance Energy Transfer-Based Biosensors Studied on the Single-Molecule Level
Genetically encoded Förster resonance energy transfer (FRET)-based biosensors for the quantification of ligand molecules change the magnitude of FRET between two fluorescent proteins upon binding a target metabolite. When highly sensitive sensors are being designed, extensive sensor optimization is...
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Personal Name(s): | Höfig, Henning |
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Otten, Julia / Steffen, Victoria / Pohl, Martina / Boersma, Arnold J. / Fitter, Joerg (Corresponding author) | |
Contributing Institute: |
Biotechnologie; IBG-1 Molekulare Biophysik; ICS-5 |
Published in: | ACS sensors, 3 (2018) 8, S. 1462–1470 |
Imprint: |
Washington, DC
ACS Publications
2018
|
DOI: |
10.1021/acssensors.8b00143 |
PubMed ID: |
29979038 |
Document Type: |
Journal Article |
Research Program: |
Functional Macromolecules and Complexes |
Publikationsportal JuSER |
Genetically encoded Förster resonance energy transfer (FRET)-based biosensors for the quantification of ligand molecules change the magnitude of FRET between two fluorescent proteins upon binding a target metabolite. When highly sensitive sensors are being designed, extensive sensor optimization is essential. However, it is often difficult to verify the ideas of modifications made to a sensor during the sensor optimization process because of the limited information content of ensemble FRET measurements. In contrast, single-molecule detection provides detailed information and higher accuracy. Here, we investigated a set of glucose and crowding sensors on the single-molecule level. We report the first comprehensive single-molecule study of FRET-based biosensors with reasonable counting statistics and identify characteristics in the single-molecule FRET histograms that constitute fingerprints of sensor performance. Hence, our single-molecule approach extends the toolbox of methods aiming to understand and optimize the design of FRET-based biosensors. |