SARS-CoV-2 Encodes a PPxY Late Domain Motif Known to Enhance Budding and Spread in Enveloped RNA Viruses
Bibliographic record
Abstract
ABSTRACT The current COVID-19 ( Co rona vi rus D isease-20 19 ) pandemic is affecting the health and/or socioeconomic welfare of almost everyone in the world. Finding vaccines and therapeutics is therefore urgent, but elucidation of the molecular mechanisms that allow some viruses to cross host species boundaries, becoming a threat to human health, must also be given close attention. Here, analysis of all proteins of SARS-CoV-2 revealed a unique PPxY Late (L) domain motif, 25 PPAY 28 , in a spike (S) protein inside a predicted hot disordered loop subject to phosphorylation and binding. PPxY motifs in enveloped RNA viruses are known to recruit Nedd4 E3 ubiquitin ligases and ultimately the ESCRT complex to enhance virus budding and release, resulting in higher viral loads, hence facilitating new infections. Interestingly, proteins of SARS-CoV-1 do not feature PPxY motifs, which could explain why SARS-CoV-2 is more contagious than SARS-CoV-1. Should an experimental assessment of this hypothesis show that the PPxY motif plays the same role in SARS-CoV-2 as it does in other enveloped RNA viruses, this motif will become a promising target for the development of novel host-oriented antiviral therapeutics for preventing S proteins from recruiting Nedd4 E3 ubiquitin ligase partners.
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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 | 0.001 |
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".