Random forest algorithm reveals novel sites in HA protein that shift receptor binding preference of the H9N2 avian influenza virus
Bibliographic record
Abstract
A switch from avian-type α-2,3 to human-type α-2,6 receptors is an essential element for the initiation of a pandemic from an avian influenza virus. Some H9N2 viruses exhibit a preference for binding to human-type α-2,6 receptors. This identifies their potential threat to public health. However, our understanding of the molecular basis for the switch of receptor preference is still limited. In this study, we employed the random forest algorithm to identify the potentially key amino acid sites within hemagglutinin (HA), which are associated with the receptor binding ability of H9N2 avian influenza virus (AIV). Subsequently, these sites were further verified by receptor binding assays. A total of 12 substitutions in the HA protein (N158D, N158S, A160 N, A160D, A160T, T163I, T163V, V190T, V190A, D193 N, D193G, and N231D) were predicted to prefer binding to α-2,6 receptors. Except for the V190T substitution, the other substitutions were demonstrated to display an affinity for preferential binding to α-2,6 receptors by receptor binding assays. Especially, the A160T substitution caused a significant upregulation of immune-response genes and an increased mortality rate in mice. Our findings provide novel insights into understanding the genetic basis of receptor preference of the H9N2 AIV. • Random forest algorithm was used to predict amino acid sites in H9N2 HA that are associated with the receptor binding ability. • Twelve substitutions in the HA gene were predicted to prefer binding to α2, 6 receptors. • Receptor binding assays prove that 11 of the predicted sites were true. • We provide a novel approach to identifying AA sites related to the receptor binding capacity of AIVs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".