Evidence map›Paper›PMID 41835395›Full record

ArticlebioRxiv : the preprint server for biology2026

Molecular basis for protection and cross-protection by human antibodies targeting the parainfluenza virus hemagglutinin-neuraminidase protein.

Katelyn D McCaffrey, Behrouz Ghazi Esfahani, Mohamed A Elbehairy, Anna L McCormick, Jarrod J Mousa

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Katelyn D McCaffreyORCID 0009-0003-8581-5566
Behrouz Ghazi EsfahaniORCID 0000-0001-5954-8933
Mohamed A ElbehairyORCID 0000-0002-6100-174X
Anna L McCormick

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Human parainfluenza viruses (PIVs) are a leading cause of respiratory illness, particularly in vulnerable populations where infection can lead to severe disease. Despite their clinical impact, there are currently no licensed vaccines or effective antiviral treatments available. PIVs have two large surface proteins, the fusion and hemagglutinin-neuraminidase (HN) proteins, both of which are targets of neutralizing antibodies. In this study, we identified and characterized two human monoclonal antibodies (mAbs), 5217-2 and 5217-9, which bind recombinant PIV3 HN protein, bind PIV3-infected cells, and are neutralizing in vitro. We determined the binding epitopes of the PIV3 HN-specific mAbs via biolayer interferometry and found mAb 5217-9 targets a previously defined neutralizing epitope while mAb 5217-2 binds a unique epitope, enabling a more complete understanding of the antigenic landscape. To further understand the newly defined epitope, we determined a cryo-electron microscopy (cryo-EM) structure of mAb 5217-2, which revealed an epitope adjacent to the PIV3 HN protein active site. We also determined the structure of the previously discovered anti-PIV3 HN mAb PIV3HN-09, which was previously shown to be partially protective in vivo. In a hamster challenge model of PIV3, mAb 5217-2 was determined to significantly reduce lung viral titers, demonstrating its protective capacity. Furthermore, as the site 2-directed mAb PIV3HN-05 was previously shown to cross-neutralize PIV1, we evaluated its protective efficacy in an animal challenge model with PIV1, which demonstrated a reduction in lung viral titers. Overall, these findings provide new insights into the antigenic epitopes on the PIV3 HN protein to support structure-based vaccine design efforts and demonstrate new protective mAbs for both PIV3 and PIV1.

Identifiers

PMID41835395
PMCPMC12980380

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.