Immobilized artificial membrane (IAM) liquid chromatography as a model for antimicrobial peptide partitioning into cell membranes: An evaluation
Bibliographic record
Abstract
Non-covalent immobilized artificial membrane reverse-phase high performance liquid chromatography was previously evaluated as a means whereby elution times for antimicrobial peptides from columns mimicking the lipid bilayers of different membrane systems might be used as a fast-screening method to compare relative binding effectiveness. Such a system would aid in the development of antimicrobial peptides that bind preferentially to model pathogenic systems and leave the host’s membranes reasonably unaffected. A non-covalent approach allows for flexibility in membrane composition but was found to be inadequate for analysis of most peptides due to significant lipid loss at high acetonitrile concentrations. A covalent approach where phosphatidylcholine was amide-linked to the silica surface was examined to evaluate its use as a fast-screening method and compare its data to that collected from the non-covalent columns. Initial work with a 1-cm column proved ineffective due to problems with balancing flow rates with retention times, and work was shifted to a longer 10-cm column. Results suggested that peptides bind much more strongly to covalent columns than non-covalent ones, with the binding especially enhanced by the presence of cationic residues. These columns had lipid packing densities much lower than true membranes, indicating that the peptides were partitioning deep into the bonded phase of the columns rather than into the interfacial region of the phosphate head groups, as expected in situations of biologically-relevant lipid packing densities.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".