Ancestral Protein Reconstruction and Circular Permutation for Improving the Stability and Dynamic Range of FRET Sensors

Ben E. Clifton, Jason H. Whitfield, Inmaculada Sanchez-Romero, Michel K. Herde, Christian Henneberger, Harald Janovjak, Colin J. Jackson

Research output: Chapter in Book/Conference paperChapterpeer-review

7 Citations (Scopus)

Abstract

Small molecule biosensors based on Forster resonance energy transfer (FRET) enable small molecule signaling to be monitored with high spatial and temporal resolution in complex cellular environments. FRET sensors can be constructed by fusing a pair of fluorescent proteins to a suitable recognition domain, such as a member of the solute-binding protein (SBP) superfamily. However, naturally occurring SBPs may be unsuitable for incorporation into FRET sensors due to their low thermostability, which may preclude imaging under physiological conditions, or because the positions of their N- and C-termini may be suboptimal for fusion of fluorescent proteins, which may limit the dynamic range of the resulting sensors. Here, we show how these problems can be overcome using ancestral protein reconstruction and circular permutation. Ancestral protein reconstruction, used as a protein engineering strategy, leverages phylogenetic information to improve the thermostability of proteins, while circular permutation enables the termini of an SBP to be repositioned to maximize the dynamic range of the resulting FRET sensor. We also provide a protocol for cloning the engineered SBPs into FRET sensor constructs using Golden Gate assembly and discuss considerations for in situ characterization of the FRET sensors.
Original languageEnglish
Title of host publicationSynthetic Protein Switches: Methods And Protocols
EditorsV Stein
PublisherSpringer Nature
Pages71-87
Number of pages17
Volume1596
ISBN (Electronic)978-1-4939-6940-1
ISBN (Print)978-1-4939-6938-8
DOIs
Publication statusPublished - 2017
Externally publishedYes

Publication series

NameMethods in Molecular Biology
Volume1596
ISSN (Print)1064-3745

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