Isolation by multistep chromatography improves the consistency of secreted recombinant influenza neuraminidase antigens
Bibliographic record
Abstract
Recombinant protein-based approaches are ideally suited for producing vaccine antigens that are not overly abundant in viruses, such as influenza neuraminidase (NA). However, obtaining sufficient quantities of recombinant viral surface antigens remains challenging, often resulting in the use of chimeric proteins with affinity tags that can invariably impact the antigen’s properties. Here, we developed multistep chromatography approaches for purifying secreted recombinant NA (rNA) antigens that are derived from recent H1N1 and H3N2 viruses and produced using insect cells. Analytical analyses showed that these isolation procedures yielded homogenous tetrameric rNA preparations with consistent specific activities that were not possible from a common immobilized metal affinity chromatography purification procedure. The use of classical chromatography improved the rNA tetramer homogeneity by removing the requirement of the N-terminal poly-histidine affinity tag that was shown to promote higher order rNA oligomer formation. In addition, these procedures reduced the specific activity variation by eliminating the exposure to Ni2+ ions and imidazole, with the latter showing pH and NA subtype dependent effects. Together, these results demonstrate that purification by multistep chromatography improves the homogeneity of secreted rNAs and eliminates the need for affinity tag-based approaches that can potentially alter the properties of these recombinant antigens.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".