Influenza virus and pneumococcal neuraminidases enhance catalysis by similar yet distinct sialic acid–binding strategies
Bibliographic record
Abstract
Influenza A viruses and the bacterium Streptococcus pneumoniae (pneumococci) both express neuraminidases that catalyze release of sialic acid residues from oligosaccharides and glycoproteins. Although these respiratory pathogen neuraminidases function in a similar environment, it remains unclear if these enzymes use similar mechanisms for sialic acid cleavage. Here, we compared the enzymatic properties of neuraminidases from two influenza A subtypes (N1 and N2) and the pneumococcal strain TIGR4 (NanA, NanB, and NanC). Insect cell-produced N1 and N2 tetramers exhibited calcium-dependent activities and stabilities that varied with pH. In contrast, E. coli-produced NanA, NanB, and NanC were isolated as calcium insensitive monomers with stabilities that were more resistant to pH changes. Using a synthetic substrate (MUNANA), all neuraminidases showed similar pH optimums (pH 6-7) that were primarily defined by changes in catalytic rate rather than substrate binding affinity. Upon using a multivalent substrate (fetuin sialoglycans), much higher specific activities were observed for pneumococcal neuraminidases that contain an additional lectin domain. In virions, N1 and especially N2 also showed enhanced specific activity toward fetuin that was lost upon the addition of detergent, indicating the sialic acid-binding capacity of neighboring hemagglutinin molecules likely contributes to catalysis of natural multivalent substrates. These results demonstrate that influenza and pneumococcal neuraminidases have evolved similar yet distinct strategies to optimize their catalytic activity.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".