Electron transfer dissociation (<scp>ETD</scp>): The mass spectrometric breakthrough essential for <i><scp>O</scp></i>‐Glc<scp>NA</scp>c protein site assignments—a study of the <i><scp>O</scp></i>‐Glc<scp>NA</scp>cylated protein <scp>H</scp>ost <scp>C</scp>ell <scp>F</scp>actor <scp>C</scp>1
Bibliographic record
Abstract
The development of electron-based, unimolecular dissociation MS, i.e. electron capture and electron transfer dissociation (ECD and ETD, respectively), has greatly increased the speed and reliability of labile PTM site assignment. The field of intracellular O-GlcNAc (O-linked N-acetylglucosamine) signaling has especially advanced with the advent of ETD MS. Only within the last five years have proteomic-scale experiments utilizing ETD allowed the assignment of hundreds of O-GlcNAc sites within cells and subcellular structures. Our ability to identify and unambiguously assign the site of O-GlcNAc modifications using ETD is rapidly increasing our understanding of this regulatory glycosylation and its potential interaction with other PTMs. Here, we discuss the advantages of using ETD, complimented with collisional-activation MS, in a study of the extensively O-GlcNAcylated protein Host Cell Factor C1 (HCF-1). HCF-1 is a transcriptional coregulator that forms a stable complex with O-GlcNAc transferase and controls cell cycle progression. ETD, along with higher energy collisional dissociation (HCD) MS, was employed to assign the PTMs of the HCF-1 protein isolated from HEK293T cells. These include 19 sites of O-GlcNAcylation, two sites of phosphorylation, and two sites bearing dimethylarginine, and showcase the residue-specific, PTM complexity of this regulator of cell proliferation.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".