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Record W2061731602 · doi:10.3389/fchem.2014.00050

Human isoprenoid synthase enzymes as therapeutic targets

2014· review· en· W2061731602 on OpenAlexaff
Jaeok Park, Alexios N. Matralis, Albert M. Berghuis, Youla S. Tsantrizos

Bibliographic record

VenueFrontiers in Chemistry · 2014
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant biochemistry and biosynthesis
Canadian institutionsMcGill University
Fundersnot available
KeywordsPrenylationFarnesyl pyrophosphateSqualeneGeranylgeranyl pyrophosphateMevalonate pathwayBiochemistryPrenyltransferaseMevalonic acidTerpenoidDrug discoveryIsopentenyl pyrophosphateBiologyEnzymeBiosynthesisChemistry

Abstract

fetched live from OpenAlex

In the human body, the complex biochemical network known as the mevalonate pathway is responsible for the biosynthesis of all isoprenoids, which consists of a vast array of metabolites that are vital for proper cellular functions. Two key isoprenoids, farnesyl pyrophosphate (FPP) and geranylgeranyl pyrophosphate (GGPP) are responsible for the post-translational prenylation of small GTP-binding proteins, and serve as the biosynthetic precursors to numerous other biomolecules. The down-stream metabolite of FPP and GGPP is squalene, the precursor to steroids, bile acids, lipoproteins, and vitamin D. In the past, interest in prenyl synthase inhibitors focused mainly on the role of the FPP in lytic bone diseases. More recently pre-clinical and clinical studies have strongly implicated high levels of protein prenylation in a plethora of human diseases, including non-skeletal cancers, the progression of neurodegenerative diseases and cardiovascular diseases. In this review, we focus mainly on the potential therapeutic value of down-regulating the biosynthesis of FPP, GGPP, and squalene. We summarize the most recent drug discovery efforts and the structural data available that support the current on-going studies.

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: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.005

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 head0.014
GPT teacher head0.282
Teacher spread0.268 · 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
GenreReview

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

Citations51
Published2014
Admission routes1
Has abstractyes

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