Top-Down Proteomics on a High-field Fourier Transform Ion Cyclotron Resonance Mass Spectrometer
Bibliographic record
Abstract
Mass spectrometry is the tool of choice for sequencing peptides and determining the sites of posttranslational modifications; however, this bottom-up approach lacks in providing global information about the modification states of proteins including the number and types of isoforms and their stoichiometry. Recently, various techniques and mass spectrometers, such as high-field Fourier Transform Ion Cyclotron Resonance (FTICR) mass spectrometers, have been developed to study intact proteins (top-down proteomics). While the protein molecular mass and the qualitative and quantitative information about protein isoforms can be revealed by FTICR-MS analysis, their primary structure (including the identification of modifications and their exact locations in the amino acid sequence) can directly be determined using the MS/MS capability offered by the FTICR mass spectrometer. The distinct advantage of top-down methods are that modifications can be determined for a specific protein isoform rather than for peptides belonging to one or several isoforms. In this chapter, we describe different top-down proteomic approaches enabled by high-field (7, 9.4, and 12 T) FTICR mass spectrometers, and their applicability to answer biological and biomedical questions. We also describe the use of the free flow electrophoresis (FFE) to separate proteins prior to top-down mass spectrometric characterization.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".