Modulating glyoxalase I metal selectivity by deletional mutagenesis: underlying structural factors contributing to nickel activation profiles
Bibliographic record
Abstract
Metabolically produced methylglyoxal is a cytotoxic compound that can lead to covalent modification of cellular DNA, RNA and protein. One pathway to detoxify this compound is via the glyoxalase enzyme system. The first enzyme of this detoxification system, glyoxalase I (GlxI), can be divided into two classes according to its metal activation profile, a Zn(2+)-activated class and a Ni(2+)-activated class. In order to elucidate some of the key structural features required for selective metal activation by these two classes of GlxI, deletional mutagenesis was utilized to remove, in a step-wise fashion, a key α-helix (residues 73-87) and two small loop regions (residues 99-103 and 111-114) from the Zn(2+)-activated Pseudomonas aeruginosa GlxI (GloA3) in order to mimic the smaller Ni(2+)-activated GlxI (GloA2) from the same organism. This approach was observed to clearly shift the metal activation profile of a Zn(2+)-activated class GlxI into a Ni(2+)-activated class GlxI enzyme. The α-helix structural component was found to contribute significantly toward GlxI metal specificity, while the two small loop regions were observed to play a more crucial role in the magnitude of the enzymatic activity. The current study should provide additional information on the fundamental relationship of protein structure to metal selectivity in these metalloenzymes.
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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".