Proteomic Profiling of <i>Leishmania donovani</i> Promastigote Subcellular Organelles
Bibliographic record
Abstract
To facilitate a greater understanding of the biological processes in the medically important Leishmania donovani parasite, a combination of differential and density-gradient ultracentrifugation techniques were used to achieve a comprehensive subcellular fractionation of the promastigote stage. An in-depth label-free proteomic LC-MS/MS analysis of the density gradients resulted in the identification of ∼50% of the Leishmania proteome (3883 proteins detected), which included ∼645 integral membrane proteins and 1737 uncharacterized proteins. Clustering and subcellular localization of proteins was based on a subset of training Leishmania proteins with known subcellular localizations that had been determined using biochemical, confocal microscopy, or immunoelectron microscopy approaches. This subcellular map will be a valuable resource that will help dissect the cell biology and metabolic processes associated with specific organelles of Leishmania and related kinetoplastids.
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How this classification was reachedexpand
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 | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".