4.1 Article

A new method for post-translationally labeling proteins in live cells for fluorescence imaging and tracking

Journal

PROTEIN ENGINEERING DESIGN & SELECTION
Volume 30, Issue 12, Pages 771-780

Publisher

OXFORD UNIV PRESS
DOI: 10.1093/protein/gzx059

Keywords

membrane protein; protein engineering; S. cerevisiae; single cell; SpyCatcher-SpyTag

Funding

  1. Raymond and Beverley Sackler Institute for Biological, Physical, and Engineering sciences
  2. National Institute of Health [GM118528, CA209992]
  3. Medical Research Council [MR/K001485]
  4. Leverhulme Trust
  5. Royal Society of Edinburgh [Caledonian Scholarship]
  6. Direct For Mathematical & Physical Scien
  7. Division Of Physics [1522467] Funding Source: National Science Foundation
  8. Medical Research Council [MR/K001485/1] Funding Source: researchfish
  9. MRC [MR/K001485/1] Funding Source: UKRI

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We present a novel method to fluorescently label proteins, post-translationally, within live Saccharomyces cerevisiae. The premise underlying this work is that fluorescent protein (FP) tags are less disruptive to normal processing and function when they are attached post-translationally, because target proteins are allowed to fold properly and reach their final subcellular location before being labeled. We accomplish this post-translational labeling by expressing the target protein fused to a short peptide tag (SpyTag), which is then covalently labeled in situ by controlled expression of an open isopeptide domain (SpyoIPD, a more stable derivative of the SpyCatcher protein) fused to an FP. The formation of a covalent bond between SpyTag and SpyoIPD attaches the FP to the target protein. We demonstrate the general applicability of this strategy by labeling several yeast proteins. Importantly, we show that labeling the membrane protein Pma1 in this manner avoids the mislocalization and growth impairment that occur when Pma1 is genetically fused to an FP. We also demonstrate that this strategy enables a novel approach to spatiotemporal tracking in single cells and we develop a Bayesian analysis to determine the protein's turnover time from such data.

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