Expanding the Plant GSTome Through Directed Evolution: DNA Shuffling for the Generation of New Synthetic Enzymes With Engineered Catalytic and Binding Properties
Bibliographic record
Abstract
Glutathione transferases (GSTs, EC. 2.5.1.18) are inducible multifunctional enzymes that are essential in the detoxification and degradation of toxic compounds. GSTs have considerable biotechnological potential. In the present work, a new method for the generation of synthetic GSTs was developed. Abiotic stress treatment of Phaseolus vulgaris and Glycine max plants led to the induction of total GST activity and allowed the creation of a GST-enriched cDNA library using degenerated GST-specific primers and reverse transcription-PCR. This library was further diversified by employing directed evolution through DNA shuffling. Activity screening of the evolved library led to the identification of a novel tau class GST enzyme (PvGmGSTUG). The enzyme was purified by affinity chromatography, characterized by kinetic analysis, and its structure was determined by X-ray crystallography. Interestingly, PvGmGSTUG displayed enhanced glutathione hydroperoxidase activity, which was significantly greater than that reported so far for natural tau class GSTs. In addition, the enzyme displayed unusual cooperative kinetics towards 1-chloro-2,4-dinitrochlorobenzene but not towards glutathione. The present work provides an easy approach for the simultaneous shuffling of GST genes from different plants, thus allowing the directed evolution of plants GSTome. This may permit the generation of new synthetic enzymes with interesting properties that are valuable in biotechnology.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".