Quantitative assessment confirms deep proteome analysis by integrative top–down proteomics
Bibliographic record
Abstract
The goal of integrative top-down proteomics (i.e., two-dimensional gel electrophoresis [2DE] coupled with liquid chromatography and tandem mass spectrometry [LC/MS/MS]) is a routine analytical approach that fully addresses the breadth and depth of proteomes. To accomplish this, there should be no addition, removal, or modification to any constituent proteoforms. To address two-decade old claims of protein losses during front-end proteome resolution using 2DE, here we tested an alternate rehydration method for immobilized pH gradient strips prior to isoelectric focusing (IEF; i.e., faceup compared to facedown) and quantitatively assessed losses during the front-end of 2DE (rehydration and IEF). Using a well-established high-resolution, quantitative 2DE protocol, there were no detectable proteoform losses using the alternate faceup rehydration method. Although there is a <0.25% total loss of proteoforms during standard facedown rehydration, it is insignificant in terms of having any effect on overall proteome resolution (i.e., total spot count and total spot signal). This report is another milestone in integrative top-down proteomics, disproving long-held dogma in the field and confirming that quantitative front-end 2DE/LC/MS/MS is currently the only method to broadly and deeply analyze proteomes by resolving their constituent proteoforms.
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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 | 0.002 | 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".