Targeting Mycobacterium tuberculosis and Other Microbial Pathogens Using Improved Synthetic Antibacterial Peptides
Bibliographic record
Abstract
The lack of effective therapies for treating tuberculosis (TB) is a global health problem. While Mycobacterium tuberculosis is notoriously resistant to most available antibiotics, we identified synthetic short cationic antimicrobial peptides that were active at low micromolar concentrations (less than 10 μM). These small peptides (averaging 10 amino acids) had remarkably broad spectra of antimicrobial activities against both bacterial and fungal pathogens and an indication of low cytotoxicity. In addition, their antimicrobial activities displayed various degrees of species specificity that were not related to taxonomy. For example, Candida albicans and Staphylococcus aureus were the best surrogates to predict peptide activity against M. tuberculosis, while Mycobacterium smegmatis was a poor surrogate. Principle component analysis of activity spectrum profiles identified unique features associated with activity against M. tuberculosis that reflect their distinctive amino acid composition; active peptides were more hydrophobic and cationic, reflecting increased tryptophan with compensating decreases in valine and other uncharged amino acids and increased lysine. These studies provide foundations for development of cationic antimicrobial peptides as potential new therapeutic agents for TB treatment.
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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 | 0.000 |
| 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) | 0.000 | 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".