Rational Design of Mechanism-Based Inhibitors and Activity-Based Probes for the Identification of Retaining α-<scp>l</scp>-Arabinofuranosidases
Bibliographic record
Abstract
Abstract Identifying and characterizing the enzymes responsible for an observed activity within a complex eukaryotic catabolic system remains one of the most significant challenges in the study of biomass-degrading systems. The debranching of both complex hemicellulosic and pectinaceous polysaccharides requires the production of α-l-arabinofuranosidases among a wide variety of coexpressed carbohydrate-active enzymes. To selectively detect and identify α-l-arabinofuranosidases produced by fungi grown on complex biomass, potential covalent inhibitors and probes which mimic α-l-arabinofuranosides were sought. The conformational free energy landscapes of free α-l-arabinofuranose and several rationally designed covalent α-l-arabinofuranosidase inhibitors were analyzed. A synthetic route to these inhibitors was subsequently developed based on a key Wittig–Still rearrangement. Through a combination of kinetic measurements, intact mass spectrometry, and structural experiments, the designed inhibitors were shown to efficiently label the catalytic nucleophiles of retaining GH51 and GH54 α-l-arabinofuranosidases. Activity-based probes elaborated from an inhibitor with an aziridine warhead were applied to the identification and characterization of α-l-arabinofuranosidases within the secretome of A. niger grown on arabinan. This method was extended to the detection and identification of α-l-arabinofuranosidases produced by eight biomass-degrading basidiomycete fungi grown on complex biomass. The broad applicability of the cyclophellitol-derived activity-based probes and inhibitors presented here make them a valuable new tool in the characterization of complex eukaryotic carbohydrate-degrading systems and in the high-throughput discovery of α-l-arabinofuranosidases.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".