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Record W2969366548 · doi:10.1039/c9ob01646e

Stabilization of the triple helix in collagen mimicking peptides

2019· review· en· W2969366548 on OpenAlexafffund
Vladimir Kubyshkin

Bibliographic record

VenueOrganic & Biomolecular Chemistry · 2019
Typereview
Languageen
FieldMaterials Science
TopicCollagen: Extraction and Characterization
Canadian institutionsUniversity of WinnipegUniversity of Manitoba
FundersCanada Excellence Research Chairs, Government of CanadaGovernment of Canada
KeywordsChemistryTriple helixCollagen helixBiophysicsStereochemistry

Abstract

fetched live from OpenAlex

Collagen mimics are peptides designed to reproduce structural features of natural collagen. A triple helix is the first element in the hierarchy of collagen folding. It is an assembly of three parallel peptide chains stabilized by packing and interchain hydrogen bonds. In this review we summarize the existing chemical approaches towards stabilization of this structure including the most recent developments. Currently proposed methods include manipulation of the amino acid composition, application of unnatural amino acid analogues, stimuli-responsive modifications, chain tethering approaches, peptide amphiphiles, modifications that target interchain interactions and more. This ability to manipulate the triple helix as a supramolecular self-assembly contributes to our understanding of the collagen folding. It also provides essential information needed to design collagen-based biomaterials of the future.

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: none
GenreCandidate signal: Review · Consensus signal: Review
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)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.024
GPT teacher head0.279
Teacher spread0.254 · 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
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

Citations42
Published2019
Admission routes2
Has abstractyes

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