Optimized Incorporation of Alkynyl Fatty Acid Analogs for the Detection of Fatty Acylated Proteins using Click Chemistry
Bibliographic record
Abstract
Fatty acylation, the covalent addition of saturated fatty acids to protein substrates, is important in regulating a myriad of cellular functions in addition to its implications in cancer and neurodegenerative diseases. Recent developments in fatty acylation detection methods have enabled efficient and non-hazardous detection of fatty acylated proteins, particularly through the use of click chemistry with bio-orthogonal labeling. However, click chemistry detection can be limited by the poor solubility and potential toxic effects of adding long chain fatty acids to cell culture. Described here is a labeling approach with optimized delivery using saponified fatty acids in combination with fatty-acid free BSA, as well as delipidated media, which can improve detection of hard to detect fatty acylated proteins. This effect was most pronounced with the alkynyl-stearate analog, 17-ODYA, which has been the most commonly used fatty acid analog in click chemistry detection of acylated proteins. This modification will improve cellular incorporation and increase sensitivity to acylated protein detection. In addition, this approach can be applied in a variety of cell types and combined with other assays such as pulse-chase analysis, stable isotope labeling with amino acids in cell culture, and mass spectrometry for quantitative profiling of fatty acylated proteins.
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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".