Precision Mapping of O-Linked <i>N</i>-Acetylglucosamine Sites in Proteins Using Ultraviolet Photodissociation Mass Spectrometry
Bibliographic record
Abstract
-acetylglucosamine (O-GlcNAc) post-translational modification (PTM) sites in proteins by mass spectrometry (MS) remains a considerable technical challenge. This is due in part to cleavage of the glycosidic bond occurring prior to the peptide backbone during collisionally activated dissociation (CAD), which leads to generation of characteristic oxocarbenium ions and impairs glycosite localization. Herein, we leverage CAD-induced oxocarbenium ion generation to trigger ultraviolet photodissociation (UVPD), an alternate high-energy deposition method that offers extensive fragmentation of peptides while leaving the glycosite intact. Upon activation using UV laser pulses, efficient photodissociation of glycopeptides is achieved with production of multiple sequence ions that enable robust and precise localization of O-GlcNAc sites. Application of this method to tryptic peptides originating from O-GlcNAcylated proteins TAB1 and Polyhomeotic confirmed previously reported O-GlcNAc sites in TAB1 (S395 and S396) and uncovered new sites within both proteins. We expect this strategy will complement existing MS/MS methods and be broadly useful for mapping O-GlcNAcylated residues of both proteins and proteomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".