Intracellular Expression of Recombinant Antibody Fluorescent Protein Fusions for Localization of Target Antigens in <I>Schizosaccharomyces pombe</I><I>
Bibliographic record
Abstract
Intracellular localization is important for the characterization of a gene product. Microscopy of fluorescent protein fusions has become the method of choice to define the spatial and temporal behavior of a protein. We show here that recombinant antibody fluorescent protein fusions can be used to monitor the localization of intracellular antigens in fixed or living cells. A most successful application of phage-display technology has been the isolation of recombinant antibodies from large combinatorial repertoires. The most versatile antibody format is the single-chain Fv fragment (scFv) in which a flexible polypeptide linker joins the heavy- and light-chain antibody variable domains. Commercial systems are now available to produce scFv phage-display libraries encoding a large pool of binding specificities from which antibodies can be isolated and used as immunochemical or intracellular reagents. We designed a plasmid for ectopic expression of a recombinant antibody fused to a green fluorescent protein (GFP) under the control of an attenuated nmt1 promoter in Schizosaccharomyces pombe . The antibody binds to its target antigen without inhibiting protein function, allowing visualization of its intracellular location in fixed or living cells. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".