Structural and Biochemical Characterization of Organotin and Organolead Compounds Binding to the Organomercurial Lyase MerB Provide New Insights into Its Mechanism of Carbon–Metal Bond Cleavage
Bibliographic record
Abstract
Abstract The organomercurial lyase MerB has the unique ability to cleave carbon–Hg bonds, and structural studies indicate that three residues in the active site (C96, D99, and C159 in E. coli MerB) play important roles in the carbon–Hg bond cleavage. However, the role of each residue in carbon–metal bond cleavage has not been well-defined. To do so, we have structurally and biophysically characterized the interaction of MerB with a series of organotin and organolead compounds. Studies with two known inhibitors of MerB, dimethyltin (DMT) and triethyltin (TET), reveal that they inhibit by different mechanisms. In both cases the initial binding is to D99, but DMT subsequently binds to C96, which induces a conformation change in the active site. In contrast, diethyltin (DET) is a substrate for MerB and the SnIV product remains bound in the active site in a coordination similar to that of HgII following cleavage of organomercurial compounds. The results with analogous organolead compounds are similar in that trimethyllead (TML) is not cleaved and binds only to D99, whereas diethyllead (DEL) is a substrate and the PbIV product remains bound in the active site. Binding and cleavage is an exothermic reaction, while binding to D99 has negligible net heat flow. These results show that initial binding of organometallic compounds to MerB occurs at D99 followed, in some cases, by cleavage and loss of the organic moieties and binding of the metal ion product to C96, D99, and C159. The N-terminus of MerA is able to extract the bound PbVI but not the bound SnIV. These results suggest that MerB could be utilized for bioremediation applications, but certain organolead and organotin compounds may present an obstacle by inhibiting the enzyme.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".