Hemozymes Peroxidase Activity Of Artificial Hemoproteins Constructed From the <i>Streptomyces lividans</i> Xylanase A and Iron(III)-Carboxy-Substituted Porphyrins
Bibliographic record
Abstract
To develop artificial hemoproteins that could lead to new selective oxidation biocatalysts, a strategy based on the insertion of various iron-porphyrin cofactors into Xylanase A (Xln10A) was chosen. This protein has a globally positive charge and a wide enough active site to accommodate metalloporphyrins that possess negatively charged substituents such as microperoxidase 8 (MP8), iron(III)-tetra-alpha4-ortho-carboxyphenylporphyrin (Fe(ToCPP)), and iron(III)-tetra-para-carboxyphenylporphyrin (Fe(TpCPP)). Coordination chemistry of the iron atom and molecular modeling studies showed that only Fe(TpCPP) was able to insert deeply into Xln10A, with a KD value of about 0.5 microM. Accordingly, Fe(TpCPP)-Xln10A bound only one imidazole molecule, whereas Fe(TpCPP) free in solution was able to bind two, and the UV-visible spectrum of the Fe(TpCPP)-Xln10A-imidazole complex suggested the binding of an amino acid of the protein on the iron atom, trans to the imidazole. Fe(TpCPP)-Xln10A was found to have peroxidase activity, as it was able to catalyze the oxidation of typical peroxidase cosubstrates such as guaiacol and o-dianisidine by H2O2. With these two cosubstrates, the KM value measured with the Fe(TpCPP)-Xln10A complex was higher than those values observed with free Fe(TpCPP), probably because of the steric hindrance and the increased hydrophobicity caused by the protein around the iron atom of the porphyrin. The peroxidase activity was inhibited by imidazole, and a study of the pH dependence of the oxidation of o-dianisidine suggested that an amino acid with a pKA of around 7.5 was participating in the catalysis. Finally, a very interesting protective effect against oxidative degradation of the porphyrin was provided by the protein.
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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 | 0.001 |
| 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".