Expanding the Global Map of Protein Post Translational Modifications with Immunoaffinity Enrichment and nDIA Analysis on the Orbitrap Astral Mass Spectrometer
Bibliographic record
Abstract
Post-translational modifications (PTMs) contribute greatly to the diversity of the human proteome by affecting protein structure, function, interactions, stability, localization, and more. The study of PTMs is essential to understand various cellular functions, disease mechanisms, and aid in the development of biomarkers and design of therapeutic targets. Owing to their diversity, dynamic nature, and low stoichiometry compared to unmodified proteome counterparts, the analysis of PTMs remains challenging. In this study, immunoaffinity enrichment of PTM peptides was combined with analysis using data dependent acquisition and narrow window Data Independent Acquisition (nDIA) on the Orbitrap Astral Mass Spectrometer (Orbitrap Astral MS) as well as comparative analysis using the Orbitrap Fusion Lumos Mass Spectrometer (Orbitrap Fusion Lumos MS) for ubiquitination, phosphorylation, acetylation, succinylation, and methylation. Human cell lines and mouse tissue samples at various input peptide amounts were immuno-enriched and mass spectrometry data was acquired on both instruments to assess depth of coverage and number of novel sites identified. The study identified a total of 106152 unique ubiquitin sites, 64397 phosphorylation sites (43721 phosphoserine, 8414 phosphothreonine and 12262 phosphotyrosine), 14245 acetylation, 5272 succinylation and 1461 mono-methylation sites. In half the acquisition time, nDIA analysis of immuno-enriched samples on Orbitrap Astral MS provided much greater depth of coverage for all PTMs compared to data dependent acquisition analysis on Orbitrap Fusion Lumos MS, with up to 33-fold more PTM peptides identified and quantified. Overall, the data presented in this study demonstrates the need for enrichment for PTM detection and the utility of combining antibody-based peptide capture and nDIA analysis on the Orbitrap Astral MS as powerful tools for discovery and profiling of protein post-translational modifications in cells and tissues.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".