Purification and functional characterization of gag-spike virus-like particles: Process optimization for efficient vaccine production
Bibliographic record
Abstract
Virus-like particles (VLPs) displaying the SARS-CoV-2 Spike protein represent a promising vaccine platform due to their safety and immunogenicity. This study focuses on developing a scalable downstream process for the purification of Gag-Spike VLPs produced in suspension HEK293 cells. A tangential flow filtration (TFF) step was optimized by varying transmembrane pressure (TMP) and shear rates to maximize permeate flux while preserving particle integrity and functionality. Several chromatographic resins including SepFast DUO 5000, SepFast DUO 700, CaptoCore™ 700, CIMmultus QA monolith, and HiTrap Q were evaluated for their capacity to recover VLPs and reduce host cell proteins and DNA. Sequential purification approaches were assessed to improve recovery and purity, and the final product was subsequently passed through a sterile-filter to ensure sterility. The purified VLPs were analyzed using SDS-PAGE, dynamic light scattering, and flow-virometry. These analyses confirmed the removal of impurities, size homogeneity, and maintenance of Spike protein on the VLP surface. Flow-virometry demonstrated that a substantial portion of the VLP population retained functional Spike proteins capable of binding ACE2 and Spike antibodies. This work establishes a robust, scalable purification strategy for processing VLPs with preserved structural and functional properties suitable for vaccine development.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| 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".