Bioorthogonal Non-Canonical Amino Acid Tagging (BONCAT) to Detect Newly Synthesized Proteins in Cells and their Secretome v1
Bibliographic record
Abstract
Cells respond to physiological or pathological stimuli by altering the composition of the proteins they produce. This adaptation includes changes to newly translated polypeptides that are destined for intracellular compartments or secretion. The secretome is relevant to cell physiology, as it promotes a noutocrine, paracrine, and endocrine signaling. These events control cell death, tissue repair and other regenerative processes. Uncovering the changes in de novo protein synthesis under different growth conditions requires reliable methods to identify and quantify newly synthesized proteins. Bioorthogonal Noncanonical Amino Acid Tagging (BONCAT) can generate this information with high spatiotemporal resolution. We developed a BONCAT-based protocol to characterize proteins synthesized de novo in mammalian cells. The current protocol employs L-azidohomoalanine as an L-methionine analog, which is incorporated into newly translated polypeptide chains. After the incubation period, cells and the growth medium, which contains the secretome, are processed separately. Specifically, proteins are alkylated, and L-azidohomoalanine is modified with a biotin affinity tag. Proteins are collected using a rapid precipitation method, which is compatible with the subsequent affinity purification of biotinylated polypeptides. The affinity-purified material can be used for diverse downstream applications, such as Western blotting. Our modified BONCAT protocol was developed to study newly produced proteins in growing cells and their secretome. This method will be useful to examine the proteome and secretome changes that are linked to the altered performance of cells, tissues, and organs during aging, disease, or other challenging conditions.
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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 | 0.001 | 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".