Proteomic Complex Detection Using Sedimentation
Bibliographic record
Abstract
Protein-protein interactions are important in many cellular processes, but there are still relatively few methods to screen for novel protein complexes. Here we present a quantitative proteomics technique called ProCoDeS (Proteomic Complex Detection using Sedimentation) for profiling the sedimentation of a large number of proteins through a rate zonal centrifugation gradient. Proteins in a putative complex can be identified since they sediment faster than predicted from their monomer molecular weight. Using solubilized mitochondrial membrane proteins from Arabidopsis thaliana, the relative protein abundance in fractions of a rate zonal gradient was measured with the isotopic labeling reagent ICAT and electrospray mass spectrometry. Subunits of the same protein complex had very similar gradient distribution profiles, demonstrating the reproducibility of the quantitation method. The approximate size of the unknown complex can be inferred from its sedimentation rate relative to known protein complexes. ProCoDeS will be of use in screening extracts of tissues, cells, or organelle fractions to identify specific proteins in stable complexes that can be characterized by subsequent targeted techniques such as affinity tagging.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".