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Record W2016347013 · doi:10.1021/bc800066b

Tyrosinase-Catalyzed Synthesis of a Universal Coil-Chitosan Bioconjugate for Protein Immobilization

2008· article· en· W2016347013 on OpenAlexaff
A. Demolliens, Cyril Boucher, Yves Durocher, Mario Jolicœur, Michael D. Buschmann, Grégory De Crescenzo

Bibliographic record

VenueBioconjugate Chemistry · 2008
Typearticle
Languageen
FieldMaterials Science
TopicSilk-based biomaterials and applications
Canadian institutionsPolytechnique MontréalNational Research Council CanadaBiotechnology Research Institute
Fundersnot available
KeywordsChitosanChemistryConjugateBioconjugationTyrosinasePeptideCovalent bondSurface plasmon resonanceCombinatorial chemistryEpidermal growth factorDrug deliveryBiochemistryNanotechnologyOrganic chemistryNanoparticleReceptorEnzyme

Abstract

fetched live from OpenAlex

Chitosan has been reported as a promising material for gene and drug delivery as well as for tissue engineering and regenerative medicine. We report here the conjugation of a de novo designed coil peptide (Kcoil) to chitosan ( M(n) = 200 kDa) to achieve a universal Kcoil-chitosan scaffold for subsequent immobilization of proteins tagged with the Kcoil partner, i.e., the Ecoil peptide. Kcoil-chitosan conjugate was synthesized using a tyrosinase-catalyzed protocol. Extensive UV/vis and IR characterization demonstrated that Kcoil peptide was covalently grafted to amines of chitosan. The ability of Kcoil-chitosan conjugate to recruit Ecoil tagged epidermal growth factor (EGF) was assessed by surface plasmon resonance measurements (SPR). Despite nonspecific interactions between chitosan and EGF, the specific formation of an E/K coiled coil complex was observed at slightly acidic pH and high salt concentration conditions, demonstrating that grafting to chitosan did not negatively impact binding characteristics of Kcoil peptide. Finally, the benefits of such bioconjugates for biomedical applications are discussed.

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 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 score0.002
Threshold uncertainty score0.962

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)0.0000.000

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 head0.017
GPT teacher head0.231
Teacher spread0.214 · 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

Citations42
Published2008
Admission routes1
Has abstractyes

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