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Generation of photocaged nanobodies for in vivo applications using genetic code expansion and computationally guided protein engineering

Jack M. O’Shea, Angeliki Goutou, Cyrus Sethna, Christopher W. Wood, Sebastian Greiss
doi: https://doi.org/10.1101/2021.04.16.440193
Jack M. O’Shea
1Centre for Discovery Brain Sciences, University of Edinburgh, UK
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Angeliki Goutou
1Centre for Discovery Brain Sciences, University of Edinburgh, UK
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Cyrus Sethna
1Centre for Discovery Brain Sciences, University of Edinburgh, UK
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Christopher W. Wood
2Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, UK
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Sebastian Greiss
1Centre for Discovery Brain Sciences, University of Edinburgh, UK
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  • For correspondence: s.greiss@ed.ac.uk
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Abstract

Nanobodies are becoming increasingly popular as tools for manipulating and visualising proteins in vivo. The ability to control nanobody/antigen interactions using light could provide precise spatiotemporal control over protein function. We develop a general approach to engineer photo-activatable nanobodies using photocaged amino acids that are introduced into the target binding interface by genetic code expansion. Guided by computational alanine scanning and molecular-dynamics simulations, we tune nanobody/target binding affinity to eliminate binding before uncaging. Upon photo-activation, binding is restored. We use this approach to generate improved photocaged variants of two anti-GFP nanobodies. These variants exhibit photo-activatable binding triggered by illumination with 365nm light. We demonstrate that the photocaged nanobodies we have created are highly robust and function in a complex cellular environment. We apply them to control subcellular protein localisation in the nematode worm C. elegans. Our approach provides a rare example of computationally designed proteins being directly applied in living animals and demonstrates the importance of accounting for in vivo effects on protein-protein interactions.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted April 17, 2021.
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Generation of photocaged nanobodies for in vivo applications using genetic code expansion and computationally guided protein engineering
Jack M. O’Shea, Angeliki Goutou, Cyrus Sethna, Christopher W. Wood, Sebastian Greiss
bioRxiv 2021.04.16.440193; doi: https://doi.org/10.1101/2021.04.16.440193
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Generation of photocaged nanobodies for in vivo applications using genetic code expansion and computationally guided protein engineering
Jack M. O’Shea, Angeliki Goutou, Cyrus Sethna, Christopher W. Wood, Sebastian Greiss
bioRxiv 2021.04.16.440193; doi: https://doi.org/10.1101/2021.04.16.440193

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