Transforming chemigenetic bimolecular fluorescence complementation systems into chemical dimerizers using chemistry
Kumar, P.; Gutu, A.; Waring, A.; Brown, T. A.; Lavis, L. D.; Tebo, A. G.
Show abstract
Chemigenetic tags are versatile labels for fluorescence microscopy that combine some of the advantages of genetically encoded tags with small molecule fluorophores. The Fluorescence Activating and absorbance Shifting Tags (FASTs) bind a series of highly fluorogenic and cell-permeable chromophores. Furthermore, FASTs can be used in complementation-based systems for detecting or inducing protein-protein interactions, depending on the exact FAST protein variant chosen. In this study, we systematically explore substitution patterns on FAST fluorogens and generate a series of fluorogens that bind to FAST variants, thereby activating their fluorescence. This effort led to the discovery of a novel fluorogen with superior properties, as well as a fluorogen that transforms splitFAST systems into a fluorogenic dimerizer, eliminating the need for additional protein engineering.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Fluorescent and bioluminescent calcium indicators with tuneable colors and affinities 95%
- Bioluminescence Imaging of Potassium Ion Using a Sensory Luciferin and an Engineered Luciferase 94%
- Strategic Modulation of Polarity and Viscosity Sensitivity of Bimane Molecular Rotor-Based Fluorophores for Imaging α-Synuclein 94%
Similar papers in this journal
Similar papers in this journal
- A photoswitchable HaloTag for spatiotemporal control of fluorescence in living cells 95%
- Rendering Proteins Fluorescent Inconspicuously: Genetically Encoded 4-Cyanotryptophan Conserves Their Structure and Enables the Detection of Ligand Binding Sites 95%
- Next Generation Opto-Jasplakinolides Enable Local Remodeling of Actin Networks 94%
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.