MétaCan
Menu
Back to cohort
Record W128683111 · doi:10.1007/978-1-61779-204-5_3

The Proprotein Convertases, 20 Years Later

2011· review· en· W128683111 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMethods in molecular biology · 2011
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEndoplasmic Reticulum Stress and Disease
Canadian institutionsMontreal Clinical Research Institute
FundersCanadian Institutes of Health Research
KeywordsProprotein ConvertasesComputational biologyBiologyEndocrinology

Abstract

fetched live from OpenAlex

The proprotein convertases (PCs) are secretory mammalian serine proteinases related to bacterial subtilisin-like enzymes. The family of PCs comprises nine members, PC1/3, PC2, furin, PC4, PC5/6, PACE4, PC7, SKI-1/S1P, and PCSK9 (Fig. 3.1). While the first seven PCs cleave after single or paired basic residues, the last two cleave at non-basic residues and the last one PCSK9 only cleaves one substrate, itself, for its activation. The targets and substrates of these convertases are very varied covering many aspects of cellular biology and communication. While it took more than 22 years to begin to identify the first member in 1989-1990, in less than 14 years they were all characterized. So where are we 20 years later in 2011? We have now reached a level of maturity needed to begin to unravel the mechanisms behind the complex physiological functions of these PCs both in health and disease states. We are still far away from comprehensively understanding the various ramifications of their roles and to identify their physiological substrates unequivocally. How do these enzymes function in vivo? Are there other partners to be identified that would modulate their activity and/or cellular localization? Would non-toxic inhibitors/silencers of some PCs provide alternative therapies to control some pathologies and improve human health? Are there human SNPs or mutations in these PCs that correlate with disease, and can these help define the finesses of their functions and/or cellular sorting? The more we know about a given field, the more questions will arise, until we are convinced that we have cornered the important angles. And yet the future may well reserve for us many surprises that may allow new leaps in our understanding of the fascinating biology of these phylogenetically ancient eukaryotic proteases (Fig. 3.2) implicated in health and disease, which traffic through the cells via multiple sorting pathways (Fig. 3.3).

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.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.994
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.030
GPT teacher head0.398
Teacher spread0.368 · 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