The Secret Life of Viral Entry Glycoproteins: Moonlighting in Immune Evasion
Bibliographic record
Abstract
Survival of infection with Ebola virus (EBOV) depends on the ability of the host to mount early and strong immune responses [1], [2]. However, given that EBOV cases are associated with 40%–90% human mortality, EBOV has developed intricate solutions to human immunological defenses. Enveloped viruses, like EBOV, must deposit their genetic material within a cell to ensure their propagation. The roles of viral envelope glycoproteins in mediating virus attachment to host cells and catalyzing the subsequent fusion of the viral and host plasma membranes have been well described (reviewed in [3]). Given the limited number of genes in EBOV and other viruses, it stands to reason that these conformationally labile glycoproteins are also involved in more than just the initial steps of a productive infection. There is strong evidence that viral entry glycoproteins (GP) are modulators of host antiviral defenses (Table 1). In this article, we discuss our current structural understanding of the functions of envelope entry glycoproteins in immune evasion using EBOV as our example. Table 1 Viral entry glycoprotein-mediated immune evasion strategies in other viral families.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 | 0.002 |
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".