Directed evolution of GFP with non-natural amino acids identifies residues for augmenting and photoswitching fluorescence. Issue 2 (21st November 2014)

• Record Type: Journal Article
• Authors: Reddington, Samuel C.; Baldwin, Amy J.; Thompson, Rebecca; Brancale, Andrea; Tippmann, Eric M.; Jones, D. Dafydd
• Is Part Of: Chemical science. Volume 6:Issue 2(2015:Feb.)
• Page Start: 1159
• Page End: 1166
• Publication Date: 2014-11-21
• Subjects: Chemistry -- Periodicals; 540.5
• DOI: 10.1039/c4sc02827a
• Languages: English
• ISSNs: 2041-6520

Abstract

Abstract : Genetic code reprogramming allows proteins to sample new chemistry through targeted introduction of non-natural amino acids. By combining with random codon replacement, residues traditionally overlooked can be identified as instilling new properties on a target protein. Abstract : Genetic code reprogramming allows proteins to sample new chemistry through the defined and targeted introduction of non-natural amino acids (nAAs). Many useful nAAs are derivatives of the natural aromatic amino acid tyrosine, with the para OH group replaced with useful but often bulkier substituents. Extending residue sampling by directed evolution identified positions in Green Fluorescent Protein tolerant to aromatic nAAs, including identification of novel sites that modulate fluorescence. Replacement of the buried L44 residue by photosensitive p -azidophenylalanine (azF) conferred environmentally sensitive photoswitching. In silico modelling of the L44azF dark state provided an insight into the mechanism of action through modulation of the hydrogen bonding network surrounding the chromophore. Targeted mutagenesis of T203 with aromatic nAAs to introduce π-stacking with the chromophore successfully generated red shifted versions of GFP. Incorporation of azF at residue 203 conferred high photosensitivity on sfGFP with even ambient light mediating a functional switch. Thus, engineering proteins with non-natural aromatic amino acids by surveying a wide residue set can introduce new and beneficial properties into a protein through the sampling of non-intuitive mutations. Coupled with retrospective in silico modelling, this will facilitate both our understanding of the impact of nAAs on protein structure and function, and future design endeavours.