Oxygen-independent chemogenetic protein tags for live-cell fluorescence microscopy
Iyer, A.; Baranov, M.; Foster, A. J.; Chordia, S.; Roelfes, G.; van den Bogaart, G.; Poolman, B.
Show abstract
Fluorescent proteins enable targeted visualization of biomolecules in living cells, but their maturation is oxygen-dependent and they are susceptible to aggregation and/or suffer from poor photophysical properties. Organic fluorophores are oxygen-independent with superior photophysical properties, but targeting biomolecules in vivo is challenging. Here, we introduce two oxygen-independent chemogenetic protein (OICP) tags that impart fluorogenicity and fluorescence lifetime enhancement to bound organic dyes. We present a photo- and physicochemical characterization of thirty fluorophores interacting with two OICPs and conclude that aromatic planar structures bind with high specificity to the hydrophobic pockets of the proteins. The binding specificity of the tags and the superior photophysical properties of organic fluorophores enable microscopy of living bacterial and eukaryotic cells. The exchange of photobleached dye for unbleached fluorophore enables prolonged live-cell imaging. Our protein tags provide a general tool for investigating (sub)cellular protein localization and dynamics, protein-protein interactions, and microscopy applications under strictly oxygen-free conditions.
Matching journals
The top 11 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Super-resolution imaging of proteins inside live mammalian cells with mLIVE-PAINT 94%
- Cell Surface β-Lactamase Recruitment: A Facile Selection to Identify Protein-Protein Interactions 94%
- Flash properties of Gaussia Luciferase are the result of covalent inhibition after a limited number of cycles 93%
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.