Identification of Proteolytic Products and Natural Protein N-Termini by Terminal Amine Isotopic Labeling of Substrates (TAILS)
Bibliographic record
Abstract
Determining the sequence of protein N-termini and their modifications functionally annotates proteins since translation isoforms, posttranslational modifications, and proteolytic truncations direct localization, activity, and the half-life of most proteins. Here we present in detail the steps required to perform our recently described approach we call Terminal Amine Isotopic Labeling of Substrates (TAILS), a combined N-terminomics and protease substrate discovery degradomics platform for the simultaneous quantitative and global analysis of the N-terminome and proteolysis in one MS/MS experiment. By a 3-day procedure with flexible α- and ɛ-amine labeling and blocking options, TAILS removes internal tryptic and C-terminal peptides by binding to a dendritic polyglycerol aldehyde polymer. Therefore, by negative selection, this enriches for both the N-terminal-labeled peptides and all forms of naturally blocked N-terminal peptides. In addition to providing valuable proteome annotation, the simultaneous analysis of the original mature N-terminal peptides enables these peptides to be used for higher confidence protein substrate identification by two or more different and unique peptides. Second, the analysis of the N-terminal peptides forms a statistical classifier to determine valid isotope ratio cutoffs in order to identify with high-confidence protease-generated neo-N-terminal peptides. Third, quantifying the loss of acetylated or cyclized N-terminal peptides that have been cleaved extends overall substrate coverage. Hence, TAILS allows for the global analysis of the N-terminome and determination of cleavage site motifs and substrates for protease including those with unknown or broad specificity.
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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".