Unraveling dual fusion mechanisms in BmNPV GP64: critical roles of CARC motifs and signal peptide retention
Bibliographic record
Abstract
Viral membrane fusion is a critical process enabling viruses to invade host cells, driven by viral membrane fusion proteins (MFPs). Cholesterol plays a pivotal role in this process, which is essential for the infectivity of many enveloped viruses. The interaction between MFPs and cholesterol is often facilitated by specific amino acid motifs known as cholesterol recognition/interaction amino acid consensus (CRAC) motifs and reverse CARC motifs. In a previous study, we demonstrated that CRAC1 and CRAC2 in GP64 are required for Bombyx mori nucleopolyhedrovirus (BmNPV) infection. This study further investigates the role of CARC in the GP64 protein of BmNPV, revealing their complex interaction with cholesterol and the influence of signal peptide (SP) retention on viral infectivity. We identified six putative CARC motifs in GP64 and generated mutants to assess their function. Our findings show that CARC1, CARC2, CARC3, and CARC4 are indispensable for viral fusion and infection when the SP is retained, whereas only CARC2 and CARC3 remain essential after SP cleavage. In contrast, CARC1 and CARC4 are necessary for viral infection through a cholesterol-independent mechanism resulting from double mutations in the CRAC1 and CRAC2 motifs of GP64. These insights not only deepen our understanding of BmNPV GP64-mediated fusion but also highlight potential antiviral targets, underscoring the adaptability and resilience of viral fusion mechanisms.IMPORTANCEUnderstanding viral membrane fusion mechanisms is crucial for developing antiviral strategies. This study provides novel insights into the intricate roles of CARC and CRAC motifs in the GP64 protein of BmNPV, particularly their interaction with cholesterol and the influence of signal peptide retention. The discovery that certain CARC motifs are essential for cholesterol-dependent fusion, whereas others function in a cholesterol-independent context advances our understanding of viral fusion processes. These findings emphasize the potential of targeting CARC motifs for therapeutic interventions and underline the importance of cholesterol interactions in viral infections. This research not only deepens our understanding of BmNPV fusion mechanisms but also has broader implications for other enveloped viruses.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".