Investigating amino acid contribution to mOrange and mStrawberry spectra
Bibliographic record
Abstract
Fluorescent proteins (FP) contain a unique fluorochrome, usually composed of 3 amino acids, which can absorb and emit light. FPs are widely used in cell biology by fusing them with target proteins and imaging the chimeric protein using fluorescence microscopy. Thus, FPs allow biologists to monitor cellular processes in vivo, for example, visualizing cytoskeletal components or track moving proteins and/or organelles. Directed evolution of fluorescence proteins has created a variety of colors, allowing multiple cellular components to be distinguished simultaneously within the same cell. While mOrange has a lower wavelength emission compared to dsRED, mStrawberry has a higher wavelength. There are seven amino acid differences between these FP variants, some of which are believed to be directly responsible for the change in emission, while the contribution of others is less clear. This project investigates contributions two of these residues found at position 62 and 213. Using site-directed mutagenesis was used to change glutamine at position 213 to leucine (Q213L) and serine at position 62 to threonine (S62T). Residue 62 lies within the chromophore of mOrange, thus changing the amino acid here will directly alter the chromophore. We predict that Q213L will alter the microenvironment near the fluorochrome due to its proximity to it (within 5 A). We expect the net result to be a red-shift of mOrange towards mStrawberry. Research in this area can provide new information about how these proteins produce chromophores and the effects of the microenvironment within the protein on the fluorochrome. * Indicates faculty mentor.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".