Synthetic host defense peptide inhibits SARS-CoV-2 replication <i>in vitro</i>
Bibliographic record
Abstract
ABSTRACT Although myriads of potential antiviral agents have been tested against SARS-CoV-2, only a handful have proven to be effective in clinical trials. During the COVID-19 pandemic, many known or novel peptides were evaluated for their ability to inhibit SARS-CoV-2 replication; however, testing of D-enantiomers that resist body and viral proteases has been limited. Here, we characterized the ability of D-3006, a D-enantiomeric synthetic host defense peptide, to inhibit SARS-CoV-2 replication in vitro . A battery of authentic SARS-CoV-2 variants (ancestral, Mu, Delta, and Omicron BA.1) and a comprehensive panel of β-coronavirus spike pseudotyped lentiviruses were used to demonstrate that D-3006 safely (CC 50 value = 430 µg/mL) blocked spike-mediated entry (EC 50 values ranging from 1.57 to 5.37 µg/mL) and also had synergistic anti-SARS-CoV-2 activity in vitro when combined with the viral polymerase inhibitor remdesivir. We also showed that D-3006 inhibited influenza A virus (H1N1) replication in vitro , suggesting that this synthetic host defense peptide could have potential broad antiviral activity against multiple enveloped viruses. These data, together with negative-stain transmission electron microscopy analysis, suggest that the mechanism of action of D-3006 is associated with non-specific binding to the viral membrane, most likely causing virus aggregation and interfering with virus attachment and entry. The potential broad-spectrum antiviral activity of D-3006, its innate resistance to host proteases, as well as the possibility of being used in combination with other antiviral drugs suggest that this host synthetic peptide could be developed as a candidate for the treatment of SARS-CoV-2 and/or other respiratory viral infections.
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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) | 0.001 | 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".