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Record W2272495819 · doi:10.7717/peerj.1516

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

2015· article· en· W2272495819 on OpenAlexaff
Alexander M. Brannan, William Whelan, Emma Cole, Valerie Booth

Bibliographic record

VenuePeerJ · 2015
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntimicrobial Peptides and Activities
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsRibosomeEscherichia coliPeptideDifferential scanning calorimetryBiologyBiochemistryRibosomal RNABiophysicsAntimicrobial peptidesChemistryRNAGene

Abstract

fetched live from OpenAlex

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 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 score0.007
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
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.017
GPT teacher head0.224
Teacher spread0.207 · 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

Citations26
Published2015
Admission routes1
Has abstractyes

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