Bibliographic record
Abstract
In this study, the feasibility of using laccase from Trametes versicolor to catalyze the oxidation of aqueous phenolic substrates at initial concentrations ranging from 0.1 to 50 micromolar was assessed. In particular, the oxidation of substrates of environmental concern including phenol, estradiol, cumylphenol and triclosan was tested experimentally over a wide range of initial concentrations, enzyme concentrations and as a function of time. Moreover, to provide a means for predicting the kinetics of reactions of such substrates at low concentrations, a semi-empirical kinetic model was developed based on the known reactions of the catalytic cycle of laccase. This model accounted for the influence of unproductive side reactions that become important when substrates are at low concentrations. The model was initially developed, calibrated and validated for batch reactions of phenol. Phenol was the slowest of the substrates studied and did not cause inactivation of the enzyme. It was shown that the model accurately predicted the time course of reactions over a wide range of phenol and enzyme concentrations, even for reactant concentration and reaction times that were far outside of the range of calibration of the model. The general applicability of the model was subsequently demonstrated by extending it to reactions of estradiol, cumylphenol and triclosan, each of which are characterized by very different reaction rates with laccase. It was shown that during the oxidation of these substrates, laccase is inactivated and, as such, a new term was incorporated into the model to account for the kinetics of inactivation. After calibrating the five kinetic parameters for each of the substrates, the general model demonstrated its ability to accurately predict the time course of batch reactions of all phenolic substrates for substrate and enzyme concentrations that varied over three orders of magnitude and for concentrations outside of the range of its calibration. The utility of the model was demonstrated by showing how it could be used to estimate the quantities of enzyme and reaction time required to achieve various levels of conversion of each substrate over a range of initial concentrations and also to achieve residual concentrations that would ensure the protection of aquatic life in surface waters. This work was extended further in order to evaluate the impacts of the presence of other substrates in a mixture would have on the oxidation of a substrate that was targeted for oxidation. In general, it was shown that (1) non-inactivating secondary substrates have a significant negative impact on the rate of oxidation of the target substrate if both are very fast substrates of laccase and, furthermore, this impact will increase with increasing concentration of the secondary substrate relative to the target substrate; and (2) inactivating secondary substrates,have important negative impacts on the oxidation of the target substrate and these impacts increase with its concentration. As part of this study, the kinetic model described above was adapted further to model reactions of mixtures of substrates. It was shown that, without further calibration of the kinetic parameters beyond that which had been done in earlier studies of reactions of single substrates, the multi-substrate model was generally able to accurately model the time course of reactions of mixtures of phenols. The exception to this were reactions that simultaneously involved estradiol and triclosan where an additional source of laccase inactivation occurred that was not accounted for by the model. The utility of the multi-substrate model was demonstrated by showing how it could be used to predict the quantities of enzyme and reaction times required to accomplish the oxidation of substrates targeted for oxidation in various mixtures of other substrates.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 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".