Variation in Ligand Binding Specificities of a Novel Class of Poxvirus-encoded Tumor Necrosis Factor-binding Protein
Bibliographic record
Abstract
The Yatapoxviruses encode a distinct class of secreted TNF-binding protein (TNF-BP) that resembles an MHC class I heavy chain but distinct from any other known TNF inhibitor. Characterization of these viral TNF inhibitors from Tanapox virus, Yaba monkey tumor virus (YMTV) and a closely related version from Swinepox virus revealed dramatically differential TNF binding specificities for different mammalian species. The Tanapox virus 2L protein (TPV-2L) formed inhibitory complexes with human TNF, and interacted with monkey and canine TNF with high affinity but rabbit TNF with low affinity. On the other hand, YMTV-2L bound human and monkey TNF with high affinity but rabbit TNF with only low affinity. The TNF-BP from swinepox virus (SPV003/148) only interacted with porcine TNF with high affinity. The observed TNF binding analysis mirrored the biological activity of these TNF-binding protein to block TNF-induced cellular cytolysis. TPV-2L and YMTV-2L also inhibited the human TNF-mediated signaling in cells but TPV-2L exhibited higher affinity for human TNF (KD, 43 pm) compared with monkey (KD, 120 pm) whereas for YMTV-2L, the affinities were reversed (human TNF KD, 440 pm; monkey TNF KD, 230 pm). The interaction domain of human TNF with TNF-binding proteins is significantly different from that of TNFRs, as determined using human TNF mutants. We conclude that these poxvirus TNF-binding proteins represent a new class of TNF inhibitors and are distinct from the viral TNF receptor homologues characterized to date.
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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".