A combinatorial multi-site directed mutagenesis solution for improved thermal stability of <i>Lactobacillus plantarum</i> tannase
Bibliographic record
Abstract
This study used a modified flapless (FLT) version of tannase from Lactobacillus plantarum, ( LpTan) to explore the effects of “stacking” site mutations predicted by Protein Repair One Stop Shop (PROSS) to increase stability. Four different LpTan structural-state models (including apo, substrate- and product- bound as well as FLT) were comparatively applied, yielding 143 predicted mutations. Of these, eight mutations (including Q63T, A65D, A184Y, A257D, V276Y, T321G, G421D, and G439D (FLT numbering)) were selected to stack, based on conservation of the prediction across all four structural states. Combinatorial screening of the arising 256-member library yielded a selection of possible hits, of which four were further characterized. Variant P6H7 contained 7 of the 8 mutations (excluding V276Y) and showed the highest significant kcat, 17% higher than FLT and 30% higher than LpTan, and a 4.5 °C increase in Tm . Variant P8E5 with 6 of 8 mutations (excluding A257D and G439D), yielded a 6.5 °C increase in Tm compared to FLT. The two other variants showed more moderate increases, albeit still greater than FLT or LpTan. Overall, the ability to design thermal stabilized versions of a tannase is emphasized. Putative mechanisms underlying the stabilization imparted by the highlighted variations are discussed.
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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) | 0.001 | 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".