Differential scanning calorimetry of whole <i>Escherichia coli</i> treated with the antimicrobial peptide MSI-78 indicate a multi-hit mechanism with ribosomes as a novel target
Bibliographic record
Abstract
Differential Scanning Calorimetry (DSC) of intact Escherichia coli (E. coli) was used to identify non-lipidic targets of the antimicrobial peptide (AMP) MSI-78. The DSC thermograms revealed that, in addition to its known lytic properties, MSI-78 also has a striking effect on ribosomes. MSI-78's effect on DSC scans of bacteria was similar to that of kanamycin, an antibiotic drug known to target the 30S small ribosomal subunit. An in vitro transcription/translation assay helped confirm MSI-78's targeting of ribosomes. The scrambled version of MSI-78 also affected the ribosome peak of the DSC scans, but required greater amounts of peptide to cause a similar effect to the unscrambled peptide. Furthermore, the effect of the scrambled peptide was not specific to the ribosomes; other regions of the DSC thermogram were also affected. These results suggest that MSI-78's effects on E. coli are at least somewhat dependent on its particular structural features, rather than a sole function of its overall charge and hydrophobicity. When considered along with earlier work detailing MSI-78's membrane lytic properties, it appears that MSI-78 operates via a multi-hit mechanism with multiple targets.
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 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 | 0.000 |
| 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) | 0.001 | 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".