Capillary electrophoresis of amphipathic α‐helical peptide diastereomers
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".