High Throughput Screening of Purified Proteins for Enzymatic Activity
Bibliographic record
Abstract
Understanding the functions of every protein in the proteome is one of the great challenges of the postgenomic era. Global genome sequencing efforts revealed that in any genome 30-50% of genes encode proteins with unknown function (hypothetical proteins). To directly test purified hypothetical proteins for catalytic activity, the authors have designed a series of general and specific enzymatic screens. The described screens are designed to detect hydrolases (phosphatases, phosphodiesterases, proteases, and esterases), and oxidoreductases (dehydrogenases and oxidases). The general screens use either general chromogenic substrates or pools of substrates. The positive hits with the model substrates are then tested in the secondary screens with a set of potential natural substrates, or the substrate pools can be deconvoluted to identify the preferred in vitro substrate. The identification of a biochemical activity of a hypothetical protein helps to determine its cellular role.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".