Proteomics of Metal Transport and Metal‐Associated Diseases
Bibliographic record
Abstract
Proteomics technology has the potential to identify groups of proteins that have similar biological function. However, few attempts have been made to identify and characterize metal-binding proteins by using proteomics strategies. Many transition metals are essential to sustain life. Copper, iron, and zinc are the most abundant transition metals relevant to biological systems. In addition to their important biological functions, metals can also catalyze the formation of damaging free radical species. Hence, their intracellular transport is tightly regulated. Despite recent insights into the intracellular transport of copper and other metals, our overall understanding of intracellular metal metabolism remains incomplete and it is likely that many metal-binding proteins remain undiscovered. Furthermore, the protein targets for metals during metal-associated disease states or during exposure to toxic levels of environmental metals are yet to be unravelled. A proteomics strategy for the analysis of metal-transporting or metal-binding proteins has the potential to uncover how a large number of proteins function in normal or metal-associated diseased states. Here we discuss the principal aspects of metal metabolism, and the recent developments in the area of the proteomics of metal transport.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".