Site-Directed Mutagenesis of Position 204 Threonine to Isoleucine Failed to Generate Acid-Tolerant EGFP
Bibliographic record
Abstract
Protonation of green fluorescent protein (GFP) in acidic conditions prevents the emission of fluorescent light and limits the ability to visualize, localize, and study acidic organelles. Therefore, it is critical to introduce mutations into enhanced GFP (EGFP) to generate acid-tolerant fluorescent proteins. This experiment aimed to replicate a threonine to isoleucine mutation at position 204 in EGFP and identify if acid-stable fluorescent proteins would be produced in the BL21(DE3) Escherichia coli system. Site-directed mutagenesis was utilized to generate T204I mutant EGFP. SDS-PAGE and fluorescence microscopy were employed to analyze induction success and fluorescence. Spectrofluorophotometry was used to determine the excitation and emission spectra of T204I mutant EGFP and whether acid-tolerant proteins were generated. Results illustrated that the mutation of threonine to isoleucine at position 204 produced fluorescent proteins at pH 7. However, at pH 6 and 5, proteins failed to fluoresce. The replication of the T204I mutation failed to generate acid-stable EGFP proteins in the BL21(DE3) E. coli system. There is significance in generating acid-stable cellular markers, as currently, no cellular markers thrive in acidic conditions. This limits the ability to study acidic organelles and acidic cellular processes. Creating acid-tolerant markers will permit a greater range of biological research.
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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".