O01.1 Identification and functional characterization of antimicrobial peptides from treponema pallidum
Bibliographic record
Abstract
Background Antimicrobial peptides (AMPs) are low molecular weight proteins involved in pathogen elimination. Bacteria, in particular Gram-positive commensals, have been shown to produce AMPs to inhibit and kill competing microbes. The observations that Treponema pallidum can survive and establish infection within polymicrobial sites that are abundant in commensals and/or pathogens, and ~ 6–9% of the T. pallidum proteome is predicted to be composed of ‘miniproteins’ (≤150 amino acids in size) of unknown function provided a rationale for investigating whether these small T. pallidum proteins function as AMPs. Methods A bioinformatics pipeline comprised of six AMP prediction servers was developed for identifying potential treponemal AMPs and their critical core regions (AMPCCRs) in T. pallidum miniproteins of unknown function. Selected AMPCCR candidates were chemically synthesized and assessed for antimicrobial activity against a panel of biologically and clinically relevant bacterial species using broth microdilution and a modified agar dilution method. Results Four potential AMPCCRs (Tp0451a_N, Tp0451a_C, Tp0749_N and Tp0749_C) exhibited bacteriostatic and bactericidal activity (MIC and MBC ranges of 1.0 – 256 µg/ml) against Escherichia coli (Tp0749_C), Pseudomonas aeruginosa (Tp0749_C), Streptococcus pyogenes (Tp0451a_C), Mycobacterium species (Tp0451a_N, Tp0451a_C, Tp0749_N, Tp0749_C), and Neisseria gonorrhoeae (Tp0451a_C). Conclusion Our findings are consistent with the novel concept that AMP production is an important, previously undiscovered mechanism that may contribute to survival of T. pallidum within the host. These investigations have established proof-of-concept for our AMP discovery bioinformatics pipeline via the experimental identification of the first AMPs from T. pallidum. This novel research approach has the potential to reveal an important survival mechanism that is more widespread in pathogenic bacteria than current data suggest.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".