Bibliographic record
Abstract
ABSTRACT The high affinity of biotin to streptavidin has made it one of the most widely used affinity tags in proteomics. Early methods used biotin for enrichment alone and mostly ignored the biotin labeled peptide. Recent advances in labeling led to an increase in biotinylation efficiency and shifted the interest to detection of the site of biotinylation. This increased confidence in identification and provides additional structural information yet it requires efficient release of the biotinylated protein/peptide and sensitive separation and detection of biotinylated peptides by LC-MS/MS. Despite its long use in affinity proteomics the effect of biotinylation on the chromatographic, ionization, and fragmentation behaviour and ultimate detection of peptides is not well understood. To address this we compare two commercially-available biotin labels EZ-Link Sulfo-NHS-Biotin and Sulfo-NHS-SS-Biotin, the latter one containing a labile linker to efficiently release biotin to determine the effects of peptide modification on peptide detection. We describe an increase of hydrophobicity and charge reduction with increasing number of biotin labels attached. Based on our data we recommend gradient optimization to account for more hydrophobic biotinylated peptides and include singly charged precursors to account for charge reduction by biotin.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".