The Comparative Analysis of Wild-Type and F242Y/F242A Mutant Porcine D-Amino Acid Oxidase
Bibliographic record
Abstract
D-amino acid oxidase (DAO) is a flavoenzyme that utilizes D-amino acids from the peptidoglycan layer of bacterial cell walls to generate hydrogen peroxide, a known antibacterial agent. The objective of this study is to further explore the currently unknown potential of DAO as an antibacterial agent by examining its activity through active site mutations. In this study, F242Y and F242A porcine DAO mutants were created by site directed mutagenesis (SDM) and splicing by overlap extension (SOE) PCR respectively. Spectrophotometric assays were utilized to determine the effectiveness of wild type DAO in producing hydrogen peroxide with D-alanine, D-serine and L-alanine. It was determined that D-alanine, the natural substrate of DAO, contained the greatest hydrogen peroxide producing capability. D-serine also produced hydrogen peroxide but not to the extent of D-alanine, while L-alanine demonstrated no production. By utilizing the activity of wild type DAO as a baseline, we hope to create potent DAO mutants that are effective against a greater variety of peptidoglycan D-amino acids. Future implications of this research include the development of DAO injections or orally administered drugs that can combat currently resistant infectious diseases. Keywords – Porcine D-Amino Acid Oxidase, mutagenesis methods, bactericidal abilities, peroxide production abilities, antibacterial properties.
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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".