A novel strategy for efficient production of anti-V3 human scFvs against HIV-1 clade C
Bibliographic record
Abstract
BACKGROUND: Production of human monoclonal antibodies that exhibit broadly neutralizing activity is needed for preventing HIV-1 infection, however only a few such antibodies have been generated till date. Isolation of antibodies by the hybridoma technology is a cumbersome process with fewer yields. Further, the loss of unstable or slowly growing clones which may have unique binding specificities often occurs during cloning and propagation and the strongly positive clones are often lost. This has been avoided by the process described in this paper, wherein, by combining the strategy of EBV transformation and recombinant DNA technology, we constructed human single chain variable fragments (scFvs) against the third variable region (V3) of the clade C HIV-1 envelope. RESULTS: An antigen specific phage library of 7000 clones was constructed from the enriched V3- positive antibody secreting EBV transformed cells. By ligation of the digested scFv DNA into phagemid vector and bio panning against the HIV-1 consensus C and B V3 peptides followed by random selection of 40 clones, we identified 15 clones that showed V3 reactivity in phage ELISA. DNA fingerprinting analysis and sequencing showed that 13 out of the 15 clones were distinct. Expression of the positive clones was tested by SDS-PAGE and Western blot. All the 13 anti-V3 scFvs showed cross-reactivity against both the clade C and B V3 peptides and did not show any reactivity against other unrelated peptides in ELISA. Preliminary neutralization assays indicated varying degrees of neutralization of clade C and B viruses. EBV transformation, followed by antigen selection of lines to identify specific binders, enabled the selection of phage from un-cloned lines for scFv generation, thus avoiding the problems of hybridoma technology. Moreover, as the clones were pretested for antigen binding, a comparatively small library sufficed for the selection of a considerable number of unique antigen binding phage. After selection, the phage clones were propagated in a clonal manner. CONCLUSIONS: This strategy can be efficiently used and is cost effective for the generation of diverse recombinant antibodies. This is the first study to generate anti-V3 scFvs against HIV-1 Clade C.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".