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Record W2168901370 · doi:10.1002/0470028637.met278

The Organomercurial Lyase<scp>Mer</scp><scp>B</scp>

2004· other· en· W2168901370 on OpenAlexaff
Paola Di Lello, Julien Lafrance‐Vanasse, James G. Omichinski

Bibliographic record

VenueHandbook of Metalloproteins · 2004
Typeother
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsChemistryMercury (programming language)MethylmercuryIonic bondingStereochemistryLyaseEnzymeOrganic chemistryIon

Abstract

fetched live from OpenAlex

Abstract Mercury is introduced into the environment either from natural occurrences or from human activities, and consumption of mercury‐contaminated fish poses a serious human health issue. The three inorganic forms of mercury are elemental mercury, mercurous compounds, and mercuric compounds, while the most abundant organic form is methylmercury. Owing to its ability to permeate membranes and accumulate in organisms, methylmercury is more toxic than ionic mercury. Mercury‐resistant bacteria have developed a two enzyme system to convert both Hg (II) and methylmercury to the less toxic elemental mercury. The first enzyme is an organomercurial lyase (MerB) and the second enzyme is a mercuric ion reductase (MerA). MerB catalyzes the protonolysis of the carbon–mercury bond on a wide range of organomercurials, including methylmercury, resulting in a reduced carbon compound and ionic mercury. The cleavage of the carbon–mercury bond and the formation of the electrophile‐carbon bond are concerted (SE2). Structural studies demonstrated that MerB contains a unique fold and that significant conformational changes occur on binding of organomercurial substrates. On the basis of mutagenesis, structural, and computational studies, two cysteines and an aspartic acid residue in the active site are known to play key roles in the cleavage of the carbon–mercury bond.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.004

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.

Opus teacher head0.013
GPT teacher head0.233
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations2
Published2004
Admission routes1
Has abstractyes

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