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Record W2595011487 · doi:10.1021/acs.jmedchem.6b01499

Positional Scanning Identifies the Molecular Determinants of a High Affinity Multi-Leucine Inhibitor for Furin and PACE4

2017· article· en· W2595011487 on OpenAlexafffund
Izabela Małuch, Christine Levesque, Anna Kwiatkowska, Frédéric Couture, Kévin Ly, Roxane Desjardins, Witold Neugebauer, Adam Prahl, Robert Day

Bibliographic record

VenueJournal of Medicinal Chemistry · 2017
Typearticle
Languageen
FieldMedicine
TopicPeptidase Inhibition and Analysis
Canadian institutionsCentre Hospitalier Universitaire de SherbrookeUniversité de Sherbrooke
FundersCanadian Cancer Society Research InstituteCanadian Institutes of Health ResearchProstate Cancer CanadaNarodowe Centrum NaukiMovember Foundation
KeywordsFurinProprotein ConvertasesChemistryPeptideBiochemistryLeucineAmino acidEnzymeResidue (chemistry)

Abstract

fetched live from OpenAlex

The proprotein convertase family of enzymes includes seven endoproteases with significant redundancy in their cleavage activity. We previously described the peptide Ac-LLLLRVK-Amba that displays potent inhibitory effects on both PACE4 and prostate cancer cell lines proliferation. Herein, the molecular determinants for PACE4 and furin inhibition were investigated by positional scanning using peptide libraries that substituted its leucine core with each natural amino acid. We determined that the incorporation of basic amino acids led to analogues with improved inhibitory potency toward both enzymes, whereas negatively charged residues significantly reduced it. All the remaining amino acids were in general well tolerated, with the exemption of the P6 position. However, not all of the potent PACE4 inhibitors displayed antiproliferative activity. The best analogues were obtained by the incorporation of the Ile residue at the P5 and P6 positions. These substitutions led to inhibitors with increased PACE4 selectivity and potent antiproliferative effects.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.069
Threshold uncertainty score0.301

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
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)0.0000.000

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 head0.023
GPT teacher head0.327
Teacher spread0.303 · 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.

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

Citations11
Published2017
Admission routes2
Has abstractyes

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