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Record W2913744061 · doi:10.1039/c9ob00070d

Promotion of the collagen triple helix in a hydrophobic environment

2019· article· en· W2913744061 on OpenAlexaff
Vladimir Kubyshkin, Nediljko Budiša

Bibliographic record

VenueOrganic & Biomolecular Chemistry · 2019
Typearticle
Languageen
FieldMaterials Science
TopicCollagen: Extraction and Characterization
Canadian institutionsUniversity of Manitoba
FundersDeutsche Forschungsgemeinschaft
KeywordsTriple helixChemistryPromotion (chess)Collagen helixHelix (gastropod)StereochemistryZoology

Abstract

fetched live from OpenAlex

In contrast to many other water-soluble peptide arrangements, the formation of a triple helix in collagen proceeds inside out: polar glycyl residues form the interior, whereas nonpolar prolyl side chains constitute the exterior. In our work, we decided to exploit this aspect of the peptide architecture in order to create hyperstable collagen mimicking peptides (CMPs). The key element of this study is the environment. Given that the peptide assembles in a nonpolar medium, the collapse of the polar peptide backbone into the triple helix should become more favorable. Following this idea, we prepared CMPs based on hydrophobic proline analogues. The synthesis was performed by a combination of liquid- and solid-phase approaches: first, hexapeptides were prepared in solution, and then these were launched into conventional Fmoc-based peptide synthesis on a solid support. The resulting peptides showed an excellent signal of the triple helix in the model nonpolar solvent (octanol) according to circular dichroism observations. In a study of a series of oligomers, we found that the minimal length of the peptides required for triple helical assembly is substantially lower compared to water-soluble CMPs. Our results suggest further explorations of the CMPs in hydrophobic media; in particular, we highlight the suggestion that collagen could be converted into a membrane protein.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.007
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0070.000

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 head0.004
GPT teacher head0.181
Teacher spread0.177 · 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 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

Citations9
Published2019
Admission routes1
Has abstractyes

Explore more

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