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Protein farnesyltransferase inhibitors interfere with farnesyl diphosphate binding by rubber transferase

2003· article· en· W1481507254 on OpenAlexaff
Christopher J.D. Mau, Sylvie Garneau‐Tsodikova, Andrew A. Scholte, Jennifer E. Van Fleet, John C. Vederas, Katrina Cornish

Bibliographic record

VenueEuropean Journal of Biochemistry · 2003
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant biochemistry and biosynthesis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHevea brasiliensisAllylic rearrangementTransferaseChemistryCofactorNatural rubberFarnesyltransferaseFarnesyl diphosphate synthaseEnzymeBiochemistryStereochemistryPrenylationCatalysisBiosynthesisOrganic chemistry

Abstract

fetched live from OpenAlex

Rubber transferase, a cis-prenyltransferase, catalyzes the addition of thousands of isopentenyl diphosphate (IPP) molecules to an allylic diphosphate initiator, such as farnesyl diphosphate (FPP, 1), in the presence of a divalent metal cofactor. In an effort to characterize the catalytic site of rubber transferase, the effects of two types of protein farnesyltransferase inhibitors, several chaetomellic acid A analogs (2, 4-7) and alpha-hydroxyfarnesylphosphonic acid (3), on the ability of rubber transferase to add IPP to the allylic diphosphate initiator were determined. Both types of compounds inhibited the activity of rubber transferases from Hevea brasiliensis and Parthenium argentatum, but there were species-specific differences in the inhibition of rubber transferases by these compounds. Several shorter analogs of chaetomellic acid A did not inhibit rubber transferase activity, even though the analogs contained chemical features that are present in an elongating rubber molecule. These results indicate that the initiator-binding site in rubber transferase shares similar features to FPP binding sites in other enzymes.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.004
GPT teacher head0.178
Teacher spread0.174 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations12
Published2003
Admission routes1
Has abstractyes

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