How Cell Concentrations Are Implicated in Cell Selectivity of Antimicrobial Peptides
Bibliographic record
Abstract
Antimicrobial peptides (AMPs) are known to selectively bind to and kill microbes over host cells. Contrary to a conventional view, there is now evidence that AMP's cell selectivity varies with cell densities and is not uniquely determined. Using a coarse-grained model, we study how the cell selectivity of membrane-lytic AMPs, defined as the ratio between their minimum hemolytic (MHC) and minimum inhibitory concentrations (MIC), depends on cell densities or on the way it is measured. A general picture emerging from our study is that the selectivity better captures peptide's intrinsic properties at low cell densities. The selectivity, however, decreases and becomes less intrinsic as the cell density increases, as long as it is chosen to be the same for both types of cells. Importantly, our results show that the selectivity can be excessively overestimated if higher host cell concentrations are used; in contrast, it becomes mistakenly small if measured for a mixture of both types of cells, even with similar choices of cell densities (i.e., higher host cell densities). Our approach can be used as a fitting model for relating the intrinsic selectivity to the apparent (cell-density-dependent) one.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".