Rangefinder: A semisynthetic FRET sensor design algorithm Journal Article

Author(s): Mitchell, Joshua A; Whitfield, Jason H; Zhang, William H; Henneberger, Christian; Janovjak, Harald; O'Mara, Megan L; Jackson, Colin J
Article Title: Rangefinder: A semisynthetic FRET sensor design algorithm
Affiliation IST Austria
Abstract: Optical sensors based on the phenomenon of Förster resonance energy transfer (FRET) are powerful tools that have advanced the study of small molecules in biological systems. However, sensor construction is not trivial and often requires multiple rounds of engineering or an ability to screen large numbers of variants. A method that would allow the accurate rational design of FRET sensors would expedite the production of biologically useful sensors. Here, we present Rangefinder, a computational algorithm that allows rapid in silico screening of dye attachment sites in a ligand-binding protein for the conjugation of a dye molecule to act as a Förster acceptor for a fused fluorescent protein. We present three ratiometric fluorescent sensors designed with Rangefinder, including a maltose sensor with a dynamic range of >300% and the first sensors for the most abundant sialic acid in human cells, N-acetylneuraminic acid. Provided a ligand-binding protein exists, it is our expectation that this model will facilitate the design of an optical sensor for any small molecule of interest.
Keywords: Fluorescent Dyes; Protein Engineering; Periplasmic Binding Proteins; Arginine; biosensors; FRET; maltose; Neu5Ac; solute binding protein
Journal Title: ACS SENSORS
Volume: 1
Issue 11
ISSN: 2379-3694
Publisher: ACS  
Date Published: 2016-11-10
Start Page: 1286
End Page: 1290
DOI: 10.1021/acssensors.6b00576
Notes: J.A.M., J.H.W., and W.H.Z. were supported by Australian Postgraduate Awards (APA), AS Sargeson Supplementary scholarships, and RSC supplementary scholarships. C.J.J. acknowledges support from a Human Frontiers in Science Young Investigator Award and a Discovery Project and Future Fellowship from the Australian Research Council. M.L.O. is supported by an Australian Research Council Discovery Project (DP130102153) and the Merit Allocation Scheme of the National Computational Infrastructure.
Open access: no