rPTMDetermine: A Fully Automated Methodology for Endogenous Tyrosine Nitration Validation, Site-Localization, and Beyond
Bibliographic record
Abstract
We present herein rPTMDetermine, an adaptive and fully automated methodology for validation of the identification of rarely occurring post-translational modifications (PTMs), using a semisupervised approach with a linear discriminant analysis (LDA) algorithm. With this strategy, verification is enhanced through similarity scoring of tandem mass spectrometry (MS/MS) comparisons between modified peptides and their unmodified analogues. We applied rPTMDetermine to (1) perform fully automated validation steps for modified peptides identified from an in silico database and (2) retrieve potential yet-to-be-identified modified peptides from raw data (that had been missed through conventional database searches). In part (1), 99 of 125 3-nitrotyrosyl-containing (nitrated) peptides obtained from a ProteinPilot search were validated and localized. Twenty nitrated peptides were falsely assigned because of incorrect monoisotopic peak assignments, leading to erroneous identification of deamidation and nitration. Five additional nitrated peptides were, however, validated after performing nonmonoisotopic peak correction. In part (2), an additional 236 unique nitrated peptides were retrieved and localized, containing 113 previously unreported nitration sites; 25 endogenous nitrated peptides with novel sites were selected and verified by comparison with synthetic analogues. In summary, we identified and confidently validated 296 unique nitrated peptides—collectively representing the largest number of endogenously identified 3-nitrotyrosyl-containing peptides from the cerebral cortex proteome of a Macaca fascicularis model of stroke. Furthermore, we harnessed the rPTMDetermine strategy to complement conventional database searching and enhance the confidence of assigning rarely occurring PTMs, while recovering many missed peptides. In a final demonstration, we successfully extended the application of rPTMDetermine to peptides featuring tryptophan oxidation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".