Free‐flow electrophoresis for top‐down proteomics by Fourier transform ion cyclotron resonance mass spectrometry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it