Engineering a horseradish peroxidase C stable to radical attacks by mutating multiple radical coupling sites
Bibliographic record
Abstract
Peroxidases have great potential as industrial biocatalysts. In particular, the oxidative polymerization of phenolic compounds catalyzed by peroxidases has been extensively examined because of the advantage of this method over other conventional chemical methods. However, the industrial application of peroxidases is often limited because of their rapid inactivation by phenoxyl radicals during oxidative polymerization. In this work, we report a novel protein engineering approach to improve the radical stability of horseradish peroxidase isozyme C (HRPC). Phenylalanine residues that are vulnerable to modification by the phenoxyl radicals were identified using mass spectrometry analysis. UV-Vis and CD spectra showed that radical coupling did not change the secondary structure or the active site of HRPC. Four phenylalanine (Phe) residues (F68, F142, F143, and F179) were each mutated to alanine residues to generate single mutants to examine the role of these sites in radical coupling. Despite marginal improvement of radical stability, each single mutant still exhibited rapid radical inactivation. To further reduce inactivation by radical coupling, the four substitution mutations were combined in F68A/F142A/F143A/F179A. This mutant demonstrated dramatic enhancement of radical stability by retaining 41% of its initial activity compared to the wild-type, which was completely inactivated. Structure and sequence alignment revealed that radical-vulnerable Phe residues of HPRC are conserved in homologous peroxidases, which showed the same rapid inactivation tendency as HRPC. Based on our site-directed mutagenesis and biochemical characterization, we have shown that engineering radical-vulnerable residues to eliminate multiple radical coupling can be a good strategy to improve the stability of peroxidases against radical attack.
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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".