Free‐flow electrophoresis for top‐down proteomics by Fourier transform ion cyclotron resonance mass spectrometry
Bibliographic record
Abstract
High-efficiency prefractionation of complex protein mixtures is critical for top-down proteomics, i.e., the analysis of intact proteins by MS. Free-flow electrophoresis (FFE) can be used for IEF to separate proteins within a pH gradient according to their pIs. In an FFE system, this separation is performed entirely in the liquid phase, without the need for particulate chromatographic media, gels, or membranes. Herein, we demonstrated the compatibility of IEF-FFE with ESI-Fourier transform ICR MS (ESI-FTICR-MS) for top-down experiments. We demonstrated that IEF-FFE of intact proteins were highly reproducible between FFE instruments, between laboratories, and between analyses. Applying native (0.2% hydroxypropylmethyl cellulose) IEF-FFE to an enzyme resulted in no decrease in enzyme activity; applying either native or denaturing (8 M urea) IEF-FFE to a four-protein mixture with different pIs resulted in isolation of each protein into separate fractions in a 96-well plate. After desalting, each protein was sequenced by top-down MS/MS. As an application of this technique, chicken erythrocyte histone H2A-IV and its major modified forms were enriched by IEF-FFE. Top-down analysis revealed Lys-5 to be a major acetylation site, in addition to N-terminal acetylation.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.008 | 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".