Data from: How a paramyxovirus fusion/entry complex adapts to escape a neutralizing antibody
Bibliographic record
Abstract
Paramyxoviruses including measles, Nipah, and parainfluenza viruses are public health threats with pandemic potential. Human parainfluenza virus type 3 (HPIV3) is a leading cause of illness in pediatric, elderly, and immunocompromised populations. There are no approved vaccines or therapeutics for HPIV3. Neutralizing antibodies that target viral fusion are a potential strategy for mitigating paramyxovirus infection, however their utility may be curtailed by viral evolution that leads to resistance. Paramyxoviruses enter cells by fusing with the cell membrane in a process mediated by a complex consisting of a receptor binding protein (HN) and a fusion protein (F). Existing atomic resolution structures fail to reveal physiologically relevant interactions during viral entry. We present cryo-ET structures of pre-fusion HN-F complexes in situ on the surfaces of authentic virions that evolved resistance to an anti-HPIV3 F neutralizing mAb. Single mutations in F abolish mAb binding and neutralization. In these complexes, the HN protein that normally restrains F triggering has shifted to uncap the F apex. These complexes are more readily triggered to fuse. These structures shed light on the adaptability of the pre-fusion HN-F complex and mechanisms of paramyxoviral resistance to mAbs, and help define potential barriers to resistance for the design of mAbs.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.064 | 0.030 |
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".