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Record W1479986601 · doi:10.1002/elps.200305654

Capillary electrophoresis of amphipathic α‐helical peptide diastereomers

2004· article· en· W1479986601 on OpenAlexaff
Traian Popa, Colin T. Mant, Robert S. Hodges

Bibliographic record

VenueElectrophoresis · 2004
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of Alberta
FundersNational Institute of General Medical Sciences
KeywordsDiastereomerCapillary electrophoresisAmphiphilePeptideChemistryElectrophoresisChromatographyStereochemistryBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

We have made a rigorous assessment of the ability of capillary electrophoresis to resolve peptide diastereomers through its application to the separation of a series of synthetic 18-residue, amphipathic alpha-helical monomeric peptide analogues, where a single site in the centre of the hydrophobic face of the alpha-helix is substituted by 19 L- or D-amino acids. Such L- and D-peptide pairs have the same mass-to-charge ratio, amino acid sequence and intrinsic hydrophobicity, varying only in the stereochemistry of one residue. CE approaches assessed in their ability to separate diastereomeric peptide pairs included capillary zone electrophoresis (uncoated capillary), micellar electrokinetic chromatography (uncoated capillary in the presence of 3-[(3-cholamidopropyl)dimethylammonio]-1-propanesulfonate, CHAPS), open-tubular capillary electrochromatography (C(8)-coated capillary in the presence of 25% 2,2,2-trifluoroethanol (TFE) or 25% ethanol). Overall, the OT-CEC methods were the most effective at separating the most peptide pairs, particularly for those containing hydrophilic side chains. However, the MEKC approach proved most effective for separation of peptide pairs containing hydrophobic or aromatic side chains.

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 categoriesMeta-epidemiology (narrow)
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 score0.043
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.005
GPT teacher head0.188
Teacher spread0.183 · 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

Citations24
Published2004
Admission routes1
Has abstractyes

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