Fluorescent Protein-Based Methods for On-Plate Screening of Gene Insertion
Bibliographic record
Abstract
BACKGROUND: Unlike the commonly used method of blue-white screening for gene insertion, a fluorescent protein-based screening method offers a gain-of-function screening process without using any co-factors and a gene fusion product with a fluorescent protein reporter that is further useful in cell imaging studies. However, complications related to protein-folding efficiencies of the gene insert in fusion with fluorescent protein reporters prevent effective on-plate bacterial colony selection leading to its limited use. METHODOLOGY/PRINCIPAL FINDINGS: Here, we present three methods to tackle this problem. Our first method promotes the folding of the gene insert by using an N-terminal protein such as calmodulin that is well folded and expressed. Under this method, fluorescence was increased more than 30x over control allowing for enhanced screening. Our second method creates a fluorescent protein that is N-terminal to the gene upon insertion, thereby reducing the dependency of the fluorescent protein reporter on the folding of the gene insert. Our third method eliminates any dependence of the fluorescent protein reporter on the folding of the gene insert by using a stop and start sequence for protein translation. CONCLUSIONS/SIGNIFICANCE: The three methods together will expand the usefulness of fluorescence on-plate screening and offer a powerful alternative to blue-white screening.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.011 |
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".