Expanding the Subproteome of the Inner Mitochondria Using Protein Separation Technologies
Bibliographic record
Abstract
Currently no single proteomics technology has sufficient analytical power to allow for the detection of an entire proteome of an organelle, cell, or tissue. One approach that can be used to expand proteome coverage is the use of multiple separation technologies especially if there is minimal overlap in the proteins observed by the different methods. Using the inner mitochondrial membrane subproteome as a model proteome, we compared for the first time the ability of three protein separation methods (two-dimensional liquid chromatography using the ProteomeLab PF 2D Protein Fractionation System from Beckman Coulter, one-dimensional reversed phase high performance liquid chromatography, and two-dimensional gel electrophoresis) to determine the relative overlap in protein separation for these technologies. Data from these different methods indicated that a strikingly low number of proteins overlapped with less than 24% of proteins common between any two technologies and only 7% common among all three methods. Utilizing the three technologies allowed the creation of a composite database totaling 348 non-redundant proteins. 82% of these proteins had not been observed previously in proteomics studies of this subproteome, whereas 44% had not been identified in proteomics studies of intact mitochondria. Each protein separation method was found to successfully resolve a unique subset of proteins with the liquid chromatography methods being more suited for the analysis of transmembrane domain proteins and novel protein discovery. We also demonstrated that both the one- and two-dimensional LC allowed for the separation of the alpha-subunit of F1F0 ATP synthase that differed due to a change in pI or hydrophobicity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".