Engineering a thermostable fungal GH10 xylanase, importance of N‐terminal amino acids
Bibliographic record
Abstract
Xylanases are used in many industrial processes including pulp bleaching, baking, detergent, and the hydrolysis of plant cell wall in biofuels production. In this work we have evolved a single domain GH10 xylanase, Xyn10A_ASPNG, from Aspergillus niger to improve its thermostability. We introduced a rational approach involving as the first step a computational analysis to guide the design of a mutagenesis library in targeted regions which identified thermal important residues that were subsequently randomly mutagenized through rounds of iterative saturation mutagenesis (ISM). Focusing on five residues, four rounds of ISM had generated a quintuple mutant 4S1 (R25W/V29A/I31L/L43F/T58I) which exhibited thermal inactivation half-life (t1/2 ) at 60°C that was prolonged by 30 folds in comparison with wild-type enzyme. Whereas the wild-type enzyme retained 0.2% of its initial activity after a heat treatment of 10 min at 60°C and was completely inactivated after 2 min at 65°C, 4S1 mutant retained 30% of its initial activity after 15 min heating at 65°C. Furthermore, the mutant melting temperature (Tm ) increased by 17.4°C compared to the wild type. Each of the five mutations in 4S1 was found to contribute to thermoresistance, but the dramatic improvement of enzyme thermoresistance of 4S1 was attributed to the synergistic effects of the five mutations. Comparison of biochemical data and model structure between 4S1 and the wild-type enzyme suggested that the N-terminal coil of the enzyme is important in stabilizing GH10 xylanase structure. Based on model structure analyses, we propose that enforced hydrophobic interactions within N-terminal elements and between N- and C-terminal ends are responsible for the improved thermostability of Xyn10A_ASPNG.
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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".