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Record W2897135952 · doi:10.3390/biom8040112

The Multiple Roles of Peptidyl Prolyl Isomerases in Brain Cancer

2018· review· en· W2897135952 on OpenAlexafffund
Stefano Stifani

Bibliographic record

VenueBiomolecules · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSignaling Pathways in Disease
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
FundersCanadian Institutes of Health Research
KeywordsBrain cancerIsomeraseProlyl isomerasePIN1FKBPNeuroscienceBiologyComputational biologyCancerGeneticsGene

Abstract

fetched live from OpenAlex

Peptidyl prolyl isomerases (PPIases) are broadly expressed enzymes that accelerate the cis-trans isomerization of proline peptide bonds. The most extensively studied PPIase family member is protein interacting with never in mitosis A1 (PIN1), which isomerizes phosphorylated serine/threonine–proline bonds. By catalyzing this specific cis-trans isomerization, PIN1 can alter the structure of its target proteins and modulate their activities in a number of different ways. Many proteins are targets of proline-directed phosphorylation and thus PIN1-mediated isomerization of proline bonds represents an important step in the regulation of a variety of cellular mechanisms. Numerous other proteins in addition to PIN1 are endowed with PPIase activity. These include other members of the parvulin family to which PIN1 belongs, such as PIN4, as well as several cyclophilins and FK506-binding proteins. Unlike PIN1, however, these other PPIases do not isomerize phosphorylated serine/threonine–proline bonds and have different substrate specificities. PIN1 and other PPIases are overexpressed in many types of cancer and have been implicated in various oncogenic processes. This review will discuss studies providing evidence for multiple roles of PIN1 and other PPIases in glioblastoma and medulloblastoma, the most frequent adult and pediatric primary brain tumors.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.955
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.033
GPT teacher head0.347
Teacher spread0.314 · 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 teacher head, not a consensus.

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

Citations11
Published2018
Admission routes2
Has abstractyes

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