Therapeutic Peptides: New Arsenal Against Drug Resistant Pathogens
Bibliographic record
Abstract
Our incessant tug-of-war with multidrug resistant pathogenic bacteria has prompted researchers to explore novel methods of designing therapeutics in order to defend ourselves against infectious diseases. Combined advances in whole genome analysis, bioinformatics algorithms, and biochemical techniques have led to the discovery and subsequent characterization of an abundant array of functional small peptides in microorganisms and multicellular organisms. Typically classified as having 10 to 100 amino acids, many of these peptides have been found to have dual activities, executing important defensive and regulatory functions in their hosts. In higher organisms, such as mammals, plants, and fungi, host defense peptides have been shown to have immunomodulatory and antimicrobial properties. In microbes, certain growth-inhibiting peptides have been linked to the regulation of diverse cellular processes. Examples of these processes include quorum sensing, stress response, cell differentiation, biofilm formation, pathogenesis, and multidrug tolerance. In this review, we will present a comprehensive overview of the discovery, characteristics, and functions of host- and bacteria-derived peptides with antimicrobial activities. The advantages and possible shortcomings of using these peptides as antimicrobial agents and targets will also be discussed. We will further examine current efforts in engineering synthetic peptides to be used as therapeutics and/or drug delivery vehicles.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".