Sequence‐ and activity‐based screening of microbial genomes for novel dehalogenases
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Dehalogenases are environmentally important enzymes that detoxify organohalogens by cleaving their carbon-halogen bonds. Many microbial genomes harbour enzyme families containing dehalogenases, but a sequence-based identification of genuine dehalogenases with high confidence is challenging because of the low sequence conservation among these enzymes. Furthermore, these protein families harbour a rich diversity of other enzymes including esterases and phosphatases. Reliable sequence determinants are necessary to harness genome sequencing-efforts for accelerating the discovery of novel dehalogenases with improved or modified activities. In an attempt to extract dehalogenase sequence fingerprints, 103 uncharacterized potential dehalogenase candidates belonging to the α/β hydrolase (ABH) and haloacid dehalogenase-like hydrolase (HAD) superfamilies were screened for dehalogenase, esterase and phosphatase activity. In this first biochemical screen, 1 haloalkane dehalogenase, 1 fluoroacetate dehalogenase and 5 l-2-haloacid dehalogenases were found (success rate 7%), as well as 19 esterases and 31 phosphatases. Using this functional data, we refined the sequence-based dehalogenase selection criteria and applied them to a second functional screen, which identified novel dehalogenase activity in 13 out of only 24 proteins (54%), increasing the success rate eightfold. Four new L-2-haloacid dehalogenases from the HAD superfamily were found to hydrolyse fluoroacetate, an activity never previously ascribed to enzymes in this superfamily.
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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 it