Functional characterization of fungal lytic polysaccharide monooxygenases for cellulose surface oxidation
Bibliographic record
Abstract
BACKGROUND: Microbial lytic polysaccharide monooxygenases (LPMOs) cleave diverse biomass polysaccharides, including cellulose and hemicelluloses, by initial oxidation at C1 or C4 of glycan chains. Within the Carbohydrate-Active Enzymes (CAZy) classification, Auxiliary Activity Family 9 (AA9) comprises the first and largest group of fungal LPMOs, which are often also found in tandem with non-catalytic carbohydrate-binding modules (CBMs). LPMOs originally attracted attention for their ability to potentiate complete biomass deconstruction to monosaccharides. More recently, LPMOs have been applied for selective surface modification of insoluble cellulose and chitin. RESULTS: To further explore the catalytic diversity of AA9 LPMOs, over 17,000 sequences were extracted from public databases, filtered, and used to construct a sequence similarity network (SSN) comprising 33 phylogenetically supported clusters. From these, 32 targets were produced successfully in the industrial filamentous fungus Aspergillus niger, 25 of which produced detectable LPMO activity. Detailed biochemical characterization of the eight most highly produced targets revealed individual C1, C4, and mixed C1/C4 regiospecificities of cellulose surface oxidation, different redox co-substrate preferences, and CBM targeting effects. Specifically, the presence of a CBM correlated with increased formation of soluble oxidized products and a more localized pattern of surface oxidation, as indicated by carbonyl-specific fluorescent labeling. On the other hand, LPMOs without native CBMs were associated with minimal release of soluble products and comparatively dispersed oxidation pattern. CONCLUSIONS: This work provides insight into the structural and functional diversity of LPMOs, and highlights the need for further detailed characterization of individual enzymes to identify those best suited for cellulose saccharification versus surface functionalization toward biomaterials applications.
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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) | 0.000 | 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 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".