Localization, Annotation, and Comparison of the Escherichia coli K-12 Proteome under Two States of Growth
Bibliographic record
Abstract
Here we describe a proteomic analysis of Escherichia coli in which 3,199 protein forms were detected, and of those 2,160 were annotated and assigned to the cytosol, periplasm, inner membrane, and outer membrane by biochemical fractionation followed by two-dimensional gel electrophoresis and tandem mass spectrometry. Represented within this inventory were unique and modified forms corresponding to 575 different ORFs that included 151 proteins whose existence had been predicted from hypothetical ORFs, 76 proteins of completely unknown function, and 222 proteins currently without location assignments in the Swiss-Prot Database. Of the 575 unique proteins identified, 42% were found to exist in multiple forms. Using DIGE, we also examined the relative changes in protein expression when cells were grown in the presence and absence of amino acids. A total of 23 different proteins were identified whose abundance changed significantly between the two conditions. Most of these changes were found to be associated with proteins involved in carbon and amino acid metabolism, transport, and chemotaxis. Detailed information related to all 2,160 protein forms (protein and gene names, accession numbers, subcellular locations, relative abundances, sequence coverage, molecular masses, and isoelectric points) can be obtained upon request in either tabular form or as interactive gel images.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".