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Record W2066760760 · doi:10.1021/bi4001326

Modification of a Designed Amphipathic Cell-Penetrating Peptide and Its Effect on Solubility, Secondary Structure, and Uptake Efficiency

2013· article· en· W2066760760 on OpenAlexaff
Mousa Jafari, D. Nedra Karunaratne, Chad M. Sweeting, P. Chen

Bibliographic record

VenueBiochemistry · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPeptideAmphiphileSolubilityChemistryChinese hamster ovary cellBiophysicsCytotoxicityCell-penetrating peptideProtein secondary structureMembraneCombinatorial chemistryPeptide sequenceAqueous solutionBiochemistryIn vitroOrganic chemistryBiologyGenePolymer

Abstract

fetched live from OpenAlex

The development of safe and efficient nonviral gene delivery carriers has received a great deal of attention in the past decade. A class of amphipathic peptides has shown to be able to cross cell membranes and deliver cargo to the intracellular environment. Here, we introduce an 18-mer amphipathic peptide, C6M1, as a modified version of peptide C6 for short interfering RNA (siRNA) delivery. The importance of tryptophan residues and the effect of peptide sequence modification on its solubility, secondary structure, cytotoxicity, and uptake efficiency were investigated. The solubility of C6M1 in aqueous solutions was greatly enhanced compared to that of C6, confirmed by surface tension and anilinonaphthalene-8-sulfonic acid fluorescence measurements. C6M1 had a random/helical structure in water with the ability to attain a helical conformation in the presence of anionic components or membrane-mimicking environments. The modification significantly reduced the cytotoxicity of the peptide, making it a safer carrier for siRNA delivery. C6M1 was also found ∼90% more efficient than C6 in delivering Cy3-labeled siRNA in Chinese hamster ovary cells.

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 score0.009
Threshold uncertainty score0.634

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 head0.011
GPT teacher head0.237
Teacher spread0.226 · 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

Citations31
Published2013
Admission routes1
Has abstractyes

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