Characterization of a Bifunctional Glucoamylase <i>Af</i>GA from <i>Aspergillus fumigatus</i> with Dual Hydrolytic Activity on Starch and Chitosan
Bibliographic record
Abstract
Glucoamylase is essential for the hydrolysis of starch to glucose and has broad industrial applications. Although its catalytic domain shares similarities with GH8 family chitosanases, which are known for their bifunctional activity, no bifunctional glucoamylase has been reported to date. In this study, we identify and characterize Af GA, a glucoamylase from the pathogenic fungus Aspergillus fumigatus 293, which exhibits a dual hydrolytic activity toward both starch and chitosan. Af GA demonstrated efficient starch hydrolysis at 70 °C with a specific activity of 503.28 ± 1.3 U/mg and chitosan hydrolysis at 90 °C with a specific activity of 3.67 ± 0.1 U/mg. Molecular docking and dynamics simulations revealed that the enhanced catalytic activity and substrate binding of Af GA for starch are attributed to increased interactions within the substrate-binding pocket. The Δ Afga strain exhibited reduced growth, sporulation, and carbon utilization efficiency as well as hypersensitivity to cell wall-disrupting agents. These results highlight Afga ’s critical role in maintaining cell wall integrity and carbon metabolism in A. fumigatus . Our findings provide new insights into the substrate promiscuity of glycoside hydrolases and underscore the potential of Af GA in both industrial biocatalysis and fungal biology.
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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 | 0.000 | 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 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".