Mechanistic insights into the small-molecule inhibition of influenza A virus entry
Bibliographic record
Abstract
Influenza A virus (IAV) is a zoonotic pathogen responsible for seasonal and pandemic flu. The extensive genetic and antigenic diversity within and between IAV phylogenetic groups presents major challenges for developing universal vaccines and broad-spectrum antiviral therapies. Current interventions provide limited protection due to the virus's high mutation rate and capacity for immune evasion. Recent advancements in viral hemagglutinin (HA)-targeting small-molecule entry inhibitors offer a promising avenue to overcome these limitations. Here, we present structural and functional analyses of two group 2 HA-specific small-molecule inhibitors recently identified by our team. Cryogenic electron microscopy (cryo-EM) structures revealed that these inhibitors bind a conserved pocket within the HA stalk, likely interfering with the conformational rearrangements necessary for membrane fusion and viral entry. Structure-guided mutagenesis confirmed the critical roles of key interacting residues and uncovered distinct resistance profiles between the two compounds, as well as in comparison to Arbidol, a previously reported HA inhibitor. Notably, our structural analysis highlights intrinsic barriers to achieving cross-group inhibition with current small-molecule designs. To address this, we propose an alternative strategy for broadening antiviral coverage. Together, these findings provide mechanistic insights into IAV entry inhibition and a foundation for the rational design of next-generation anti-influenza therapeutics.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".