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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".