A Mechanism‐Based Approach to Screening Metagenomic Libraries for Discovery of Unconventional Glycosidases
Bibliographic record
Abstract
Functional metagenomics has opened new opportunities for enzyme discovery. To exploit the full potential of this new tool, the design of selective screens is essential, especially when searching for rare enzymes. To identify novel glycosidases that employ cleavage strategies other than the conventional Koshland mechanisms, a suitable screen was needed. Focusing on the unsaturated glucuronidases (UGLs), it was found that use of simple aryl glycoside substrates did not allow sufficient discrimination against β-glucuronidases, which are widespread in bacteria. While conventional glycosidases cannot generally hydrolyze thioglycosides efficiently, UGLs follow a distinct mechanism that allows them to do so. Thus, fluorogenic thioglycoside substrates featuring thiol-based self-immolative linkers were synthesized and assessed as selective substrates. The generality of the approach was validated with another family of unconventional glycosidases, the GH4 enzymes. Finally, the utility of these substrates was tested by screening a small metagenomic library.
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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) | 0.001 | 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".