The Remarkable Cationic Peptides: A Boon to Pharmaceutical Sciences?
Bibliographic record
Abstract
In this opinion article, the authors discuss a number of interesting, beneficial properties of naturally occurring and synthetic cationic antimicrobial peptides (AMPs) with the prospective aim of bringing these compounds into therapeutic use to avoid antibiotic resistance and utilize their numerous properties. The structural diversity and the conformational freedom of these compounds adversely affects their mechanistic elucidation. Our molecular level mechanistic exploration of these peptides has shown their ion carriage properties and systematically explains their antibiotic activity through disruption of bacterial cell homeostasis and inhibition of 14-α demethylase enzyme. We have also shown self-assemblage in AMPs in different nanoparticulate and tubular forms. Some AMPs possess cell penetration capability and their co-administration with drug enhances antibacterial activity through a non-disruptive mechanism. The anti-HIV activity of AMPs has been explained based on their non-covalent, non-base-pair base-pair type interactions with HIV viral ssRNA template. Design of peptidomimetic compounds with enhanced druggability based on our mechanistic explorations will definitely lead to better non-toxic drugs with antibacterial, anti-HIV activity and may contribute towards development of efficient drug delivery systems. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".