Molecular Basis of Formaldehyde Detoxification
Bibliographic record
Abstract
The Escherichia coli genes frmB (yaiM) and yeiG encode two uncharacterized proteins that share 54% sequence identity and contain a serine esterase motif. We demonstrated that purified FrmB and YeiG have high carboxylesterase activity against the model substrates, p-nitrophenyl esters of fatty acids (C2-C6) and alpha-naphthyl acetate. However, both proteins had the highest hydrolytic activity toward S-formylglutathione, an intermediate of the glutathione-dependent pathway of formaldehyde detoxification. With this substrate, both proteins had similar affinity (Km = 0.41-0.43 mM), but FrmB was almost 5 times more active. Alanine replacement mutagenesis of YeiG demonstrated that Ser145, Asp233, and His256 are absolutely required for activity, indicating that these residues represent a serine hydrolase catalytic triad in this protein and in other S-formylglutathione hydrolases. This was confirmed by inspecting the crystal structure of the Saccharomyces cerevisiae S-formylglutathione hydrolase YJG8 (Protein Data Bank code 1pv1), which has 45% sequence identity to YeiG. The structure revealed a canonical alpha/beta-hydrolase fold and a classical serine hydrolase catalytic triad (Ser161, His276, Asp241). In E. coli cells, the expression of frmB was stimulated 45-75 times by the addition of formaldehyde to the growth medium, whereas YeiG was found to be a constitutive enzyme. The simultaneous deletion of both frmB and yeiG genes was required to increase the sensitivity of the growth of E. coli cells to formaldehyde, suggesting that both FrmB and YeiG contribute to the detoxification of formaldehyde. Thus, FrmB and YeiG are S-formylglutathione hydrolases with a Ser-His-Asp catalytic triad involved in the detoxification of formaldehyde in E. coli.
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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".