Optimizing bio-orthogonal non-canonical amino acid tagging (BONCAT) for low-disruption labeling of Arabidopsis proteins in vivo
Bibliographic record
Abstract
Plants require robust responses in protein synthesis to adapt to variable environmental conditions. Measurement of newly synthesized proteins across several organisms has been successfully facilitated with Bio-Orthogonal Non-Canonical Amino acid Tagging (BONCAT). Here, we use noncanonical amino acids (NCAAs) L-azidohomoalanine (AHA) or L-homopropargylglycine (HPG) incorporation in place of methionine residues into the actively translating Arabidopsis (Arabidopsis thaliana) proteome, allowing for that subset of proteins to be enriched for mass spectrometry quantification. Although this technique has seen occasional use in plants, optimization of the protocol to maximize functionality while minimizing organismal stress remains to be established. Here, we provide evidence for successful implementation through the liquid immersion of seedlings in AHA- or HPG-containing media that functions with significantly lower concentrations than the literature standard. Our approach splits acute exposure and the incorporation phase of labeling to mitigate potential negative impacts of prolonged NCAA exposure without compromising effective enrichment capacity. This method results in an unperturbed growth phenotype for AHA-treated seedlings. Finally, we demonstrate the capacity of this modified approach to enrich newly synthesized proteins from the whole proteome under standard stress conditions. These improvements allow for a broader use of BONCAT technologies in molecular plant research, affording a deeper understanding of the newly synthesized proteome without negatively impacting plant health.
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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) | 0.001 | 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 | 0.000 |
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 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".