A Mammalian Cell Based FACS-Panning Platform for the Selection of HIV-1 Envelopes for Vaccine Development
Bibliographic record
Abstract
An increasing number of broadly neutralizing monoclonal antibodies (bnMAb) against the HIV-1 envelope (Env) protein has been discovered recently. Despite this progress, vaccination efforts with the aim to re-elicit bnMAbs that provide protective immunity have failed so far. Herein, we describe the development of a mammalian cell based FACS-panning method in which bnMAbs are used as tools to select surface-exposed envelope variants according to their binding affinity. For that purpose, an HIV-1 derived lentiviral vector was developed to infect HEK293T cells at low multiplicity of infection (MOI) in order to link Env phenotype and genotype. For proof of principle, a gp145 Env model-library was established in which the complete V3 domain was substituted by five strain specific V3 loop sequences with known binding affinities to nMAb 447-52D, respectively. Env genes were recovered from selected cells by PCR, subcloned into a lentiviral vector (i) to determine and quantify the enrichment nMAb binders and (ii) to generate a new batch of transduction competent particles. After 2 selection cycles the Env variant with highest affinity was enriched 20-fold and represented 80% of the remaining Env population. Exploiting the recently described bnMAbs, this procedure might prove useful in selecting Env proteins from large Env libraries with the potential to elicit bnMAbs when used as vaccine candidates.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".