Development of Cysteine-Free Fluorescent Proteins for the Oxidative Environment
Bibliographic record
Abstract
Molecular imaging employing fluorescent proteins has been widely used to highlight specific reactions or processes in various fields of the life sciences. Despite extensive improvements of the fluorescent tag, this technology is still limited in the study of molecular events in the extracellular milieu. This is partly due to the presence of cysteine in the fluorescent proteins. These proteins almost cotranslationally form disulfide bonded oligomers when expressed in the endoplasmic reticulum (ER). Although single molecule photobleaching analysis showed that these oligomers were not fluorescent, the fluorescent monomer form often showed aberrant behavior in folding and motion, particularly when fused to cysteine-containing cargo. Therefore we investigated whether it was possible to eliminate the cysteine without losing the brightness. By site-saturated mutagenesis, we found that the cysteine residues in fluorescent proteins could be replaced with specific alternatives while still retaining their brightness. cf(cysteine-free)SGFP2 showed significantly reduced restriction of free diffusion in the ER and marked improvement of maturation when fused to the prion protein. We further applied this approach to TagRFP family proteins and found a set of mutations that obtains the same level of brightness as the cysteine-containing proteins. The approach used in this study to generate new cysteine-free fluorescent tags should expand the application of molecular imaging to the extracellular milieu and facilitate its usage in medicine and biotechnology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".