Bibliographic record
Abstract
PURPOSE OF REVIEW: By determining metalloproteomes via high-throughput methodology, metalloproteomics provides a research strategy for investigating nutritional and metabolic issues relating to metals. In this review, we examine recent developments in metalloproteomics since its early days approximately 12 years ago, when we utilized metalloproteomics to investigate copper disposition in hepatocytes in relation to Wilson disease. RECENT FINDINGS: A metalloproteome is the set of proteins that have metal-binding capacity by being metalloproteins or manifesting metal-binding sites. Like all proteomes, a metalloproteome is determined within the context of a well defined system. It can be ascertained for a single metal or multiple metals in that system. Apart from major technological advances in analytical techniques, recent work has examined metalloproteomes for metals other than copper, notably nickel, zinc and manganese. Given the importance of microbiomes to metabolism, microbial metalloproteomics is a rapidly expanding and promising new field. SUMMARY: Metals play key roles in metabolic processes. Sufficient technological progress has taken place in the past decade to make metalloproteomics an exciting and innovative type of research in nutrition and metabolism. It elucidates how metals contribute to metabolic physiology across the phyla, including in microbes. For humans, it may clarify mechanisms as well as identify informative diagnostic or prognostic biomarkers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".