High Resolution Parallel Reaction Monitoring with Electron Transfer Dissociation for Middle-Down Proteomics
Bibliographic record
Abstract
In recent years, middle-down proteomics has emerged as a popular technique for the characterization and quantification of proteins not readily amenable to typical bottom-up approaches. So far, all high resolution middle-down approaches are done in data-dependent acquisition mode, using both collision-induced dissociation or electron capture/transfer dissociation techniques. Here, we explore middle-down proteomics with electron transfer dissociation using a targeted acquisition mode, parallel reaction monitoring (PRM), on an Orbitrap Fusion. As an example of a highly modified protein, we used histone H3 fractions from untreated and DMSO-treated Murine ErythroLeukemia (MEL) cells. We first determined optimized instrument parameters to obtain high sequence coverage using a synthetic standard peptide. We then setup a combined method of both MS1 scans and PRM scans of the 20 most abundant combinations of methylation and acetylation of the +10 charge state of the N-terminal tail of H3. Weak cation exchange hydrophilic interaction chromatography was used to separate the N-terminal H3 tail, primarily, by its acetylation and, to a secondary degree, by its methylation status, which aided in the interpretation of the results. After deconvolution of the highly charged ions, peaks were annotated to a minimum set of 254 H3 proteoforms in the untreated and treated samples. Upon DMSO treatment, global quantitation changes from the MS1 level show a relative decrease of 2, 3, 4, and 5 acetylations and an increase of 0 and 1 acetylations. A fragment ion map was developed to visualize specific differences between treated and untreated samples. Taken together, the data presented here show that middle-down proteomics with electron transfer dissociation using PRM is a novel, attractive method for the effective analysis and quantification of large and highly modified peptides.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".