Expression, purification, and renaturation of a recombinant peptide-based HIV vaccine in <i>Escherichia coli</i>
Bibliographic record
Abstract
To design an epitope-based vaccine for Human immunodeficiency virus (HIV), we previously predicted 20 potential HIV epitopes using bioinformatics approaches. The combination of these 20 epitopes has a theoretical coverage of 98.1% of the population for both the prevalent HIV genotypes and Chinese human leukocyte antigen DR types. To test the immunogenicity of this vaccine in vivo, a corresponding antigen needs to be prepared. To this end, we constructed a recombinant plasmid containing DNA encoding the epitopes and GPGPG spacers and a 6-His tag for verification of protein expression and ease of purification, and then transformed Escherichia coli cells with the plasmid. After IPTG induction, the recombinant protein was expressed in the form of mainly inclusion bodies. To stabilize the structure of denatured inclusion bodies for efficient purification and renaturation in vitro, we transferred the dissolved inclusion bodies from 7 mol/L guanidine hydrochloride to 8 mol/L urea. Under denaturing conditions, the vaccine protein was purified by a 3-step process including ion-exchange chromatography and affinity column, and then renatured by stepwise dialysis. Together, the above described procedures generated 43 mg of vaccine protein per litre of fermentation medium, and the final product reached approximately 95% purity. The purified protein was capable of eliciting antigen-specific T-cell responses in immunized mice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".