Selective reversed-phase high-performance liquid chromatography method for the determination of intact SARS-CoV-2 spike protein
Bibliographic record
Abstract
Protein-based vaccines are playing an increasingly important role in the COVID-19 pandemic. As late-stage clinical data are finalized and released, the number of protein-based vaccines expected to enter the market will increase significantly. Most protein-based COVID-19 vaccines are based on the SARS-CoV-2 spike protein (S-protein), which plays a major role in viral attachment to human cells and infection. As a result, in order to develop and manufacture quality vaccines consistently, it is imperative to have access to selective and efficient methods for the bioanalytical assessment of S-protein. In this study, samples of recombinant S-protein (hexS-protein) and commercial S-protein were used to develop a selective reversed-phase HPLC (RP-HPLC) method that enabled elution of the intact S-protein monomer as a single peak on a wide pore, C8-bonded chromatographic column. The S-protein subunits, S1 and S2 subunits, were clearly separated from intact S-protein and identified. The results of this study set the foundation for reversed-phase HPLC method development and analysis for selective and efficient separation of S-protein monomer from its subunits.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".