AI-Powered Identification of Human Cell Surface Protein Interactors of the Hemagglutinin Glycoprotein of High-Pandemic-Risk H5N1 Influenza Virus
Bibliographic record
Abstract
H5N1 is a highly pathogenic avian influenza virus of major global concern. Since 2023, it has circulated widely among wild and farmed birds, with increasing spillover into mammals, including minks, seals, and cattle, and sporadic infections in humans in Chile, the UK, and the USA. The risk of a future pandemic is considered high because ongoing viral evolution could enable efficient human-to-human transmission. The hemagglutinin (HA) glycoprotein is the principal determinant of host range, mediating viral attachment and entry through interactions with sialylated glycans and potentially additional host surface proteins. Here, we developed an artificial intelligence (AI)-based pipeline integrating structural modeling, protein-protein interaction prediction, and biological filtering to identify human cell surface proteins with high likelihood of interacting with H5N1 HA. These interactions may contribute to viral entry and tropism and therefore represent promising candidates for experimental validation and therapeutic targeting. Our findings highlight the utility of AI-driven pipelines in accelerating the discovery of host factors relevant to pandemic influenza 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".