A High Throughput Method for the Detection of Metalloproteins on a Microgram Scale
Bibliographic record
Abstract
Proteins that bind transition metals make up a substantial portion of the proteome, and the identification of a metal cofactor in a protein can greatly facilitate its functional assignment and help place it in the context of known cellular pathways. Existing methods for the detection of metalloproteins generally consume large amounts of protein, require expensive equipment, or are very labor intensive, rendering them unsuitable for use in high throughput proteomic initiatives. Here we present a method for the identification of metalloproteins that contain iron, copper, manganese, cobalt, nickel, and/or zinc that is sensitive, quick, robust, inexpensive, and can be performed with standard laboratory equipment. The assay is based on a combination of chemiluminescence and colorimetric detection methods, it typically consumes only 10 microg of protein, and most common chemical components of protein solutions do not interfere with metal detection. Analysis of 52 protein samples was compared with the results from inductively coupled plasma-atomic emission spectrometry to verify the accuracy and sensitivity of the method. The assay is conducted in a 384-well format and requires about 3 h for completion, including a 2-h wait; so whole proteomes can be assayed for metal content in a matter of days.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.026 |
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".