Sequence‐ and activity‐based screening of microbial genomes for novel dehalogenases
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.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".