Improved elution strategy and new monoclonal anti-biotin antibody for LC-MS/MS characterization of protein biotinylation sites
Bibliographic record
Abstract
Biotin labeling in combination with mass spectrometry has been widely applied in large-scale biological studies, such as determination of protein partners, protein subcellular localization, and protein post-translational modifications. Previous studies have shown that immunoaffinity enrichment is a better method than streptavidin/avidin purification for site-specific studies of biotinylated molecules. In this study, we made a crucial improvement to the elution phase of the immunoaffinity enrichment step for biotinylated peptides, which involves the addition of a highly organic solvent, and developed a monoclonal anti-biotin antibody that improved the identification number for biotinylated peptides. We then demonstrated its application in the characterization of protein interaction sites for the β2 adrenergic receptor (β2AR) by proximity labeling in living cells. Our research provides an improved and reproducible immunoaffinity enrichment method for site-specific biotin-related 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".