Features of a Spatially Constrained Cystine Loop in the p10 FAST Protein Ectodomain Define a New Class of Viral Fusion Peptides
Bibliographic record
Abstract
The reovirus fusion-associated small transmembrane (FAST) proteins are the smallest known viral membrane fusion proteins. With ectodomains of only approximately 20-40 residues, it is unclear how such diminutive fusion proteins can mediate cell-cell fusion and syncytium formation. Contained within the 40-residue ectodomain of the p10 FAST protein resides an 11-residue sequence of moderately apolar residues, termed the hydrophobic patch (HP). Previous studies indicate the p10 HP shares operational features with the fusion peptide motifs found within the enveloped virus membrane fusion proteins. Using biotinylation assays, we now report that two highly conserved cysteine residues flanking the p10 HP form an essential intramolecular disulfide bond to create a cystine loop. Mutagenic analyses revealed that both formation of the cystine loop and p10 membrane fusion activity are highly sensitive to changes in the size and spatial arrangement of amino acids within the loop. The p10 cystine loop may therefore function as a cystine noose, where fusion peptide activity is dependent on structural constraints within the noose that force solvent exposure of key hydrophobic residues. Moreover, inhibitors of cell surface thioreductase activity indicate that disruption of the disulfide bridge is important for p10-mediated membrane fusion. This is the first example of a viral fusion peptide composed of a small, spatially constrained cystine loop whose function is dependent on altered loop formation, and it suggests the p10 cystine loop represents a new class of viral fusion peptides.
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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) | 0.000 | 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".