Protein “purity,” proteoforms, and the albuminome: critical observations on proteome and systems complexity
Bibliographic record
Abstract
Introduction: The identification of effective, selective biomarkers and therapeutics is dependent on truly deep, comprehensive analysis of proteomes at the proteoform level. Methods: Bovine serum albumin (BSA) isolated by two different protocols, cold ethanol fractionation and heat shock fractionation, was resolved and identified using Integrative Top-down Proteomics, the tight coupling of two-dimensional gel electrophoresis (2DE) with liquid chromatography and tandem mass spectrometry (LC-MS/MS). Results and discussion: Numerous proteoforms were identified in both "purified" samples, across a broad range of isoelectric points and molecular weights. The data highlight several concerns regarding proteome analyses using currently popular analytical approaches and what it means to (i) purify a "protein" if the isolate consists of a wide variety of proteoforms and/or co-purifying species; and (ii) use these preparations as analytical standards or therapeutics. Failure to widely recognize and accept proteome complexity has likely delayed the identification of effective biomarkers and new, more selective drug targets. iTDP is the most logical available analytical technique to effectively provide the necessary critical depth and breadth for complex proteome analyses. Routine analyses at the level of proteoforms will provide the much-needed data for the development and validation of selective biomarkers and drugs, including biologics.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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 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".