Enzyme genomics: Application of general enzymatic screens to discover new enzymes
Bibliographic record
Abstract
In all sequenced genomes, a large fraction of predicted genes encodes proteins of unknown biochemical function and up to 15% of the genes with "known" function are mis-annotated. Several global approaches are routinely employed to predict function, including sophisticated sequence analysis, gene expression, protein interaction, and protein structure. In the first coupling of genomics and enzymology, Phizicky and colleagues undertook a screen for specific enzymes using large pools of partially purified proteins and specific enzymatic assays. Here we present an overview of the further developments of this approach, which involve the use of general enzymatic assays to screen individually purified proteins for enzymatic activity. The assays have relaxed substrate specificity and are designed to identify the subclass or sub-subclasses of enzymes (phosphatase, phosphodiesterase/nuclease, protease, esterase, dehydrogenase, and oxidase) to which the unknown protein belongs. Further biochemical characterization of proteins can be facilitated by the application of secondary screens with natural substrates (substrate profiling). We demonstrate here the feasibility and merits of this approach for hydrolases and oxidoreductases, two very broad and important classes of enzymes. Application of general enzymatic screens and substrate profiling can greatly speed up the identification of biochemical function of unknown proteins and the experimental verification of functional predictions produced by other functional genomics approaches.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 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 | 0.003 |
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".