A mechanistic study of anti-HIV activities of antifungal peptides
Bibliographic record
Abstract
HIV patients are constantly at risk of developing internal fungal infection and are thus regularly prescribed antifungal medications. Several classes of antifungal agents have been developed to combat ever increasing cases of resistant strains of fungi. Azoles, despite being the most popular clinical choice, are not devoid of side effects. Many antimicrobial peptides have also been tested in search of safe, nontoxic antifungals but none succeeded as a commercial alternative. Recent research attempts show continued interest in these compounds and the complexities associated. Some experimental observations indicate involvement of these antimicrobial peptides in enhancing the efficacy of anti-HIV agents. We present here an intertwined approach to deal with two fatal diseases, internal fungal infection and HIV infection. Several naturally occurring antimicrobial peptides have been studied for their possible interaction with the viral RNA primer binding site (template) through interactions other than the base pair – base pair type. Peptides have been prepared and docked into viral template utilizing extra precision, flexible ligand docking. Implicit solvent was added around the complex and MMGBSA interaction energies were computed. Druggability aspects were explored by calculating ADME-related properties. A peptidomimetic compound has been strategically designed to introduce some druggability features in the peptide maintaining its viral template inhibition capability. The designed peptidomimetic lead compound may help in obtaining nontoxic anti-HIV agents in the future. This is the first study to suggest a plausible explanation for the anti-HIV activity of antifungal peptides at the molecular level and corroborate experimental findings of synergistic effects of these peptides on anti-HIV agents.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".