Cross-Reactive HIV-1-Neutralizing Human Monoclonal Antibodies Identified from a Patient with 2F5-Like Antibodies
Bibliographic record
Abstract
The genes encoding broadly HIV-1-neutralizing human monoclonal antibodies (MAbs) are highly divergent from their germ line counterparts. We have hypothesized that such high levels of somatic hypermutation could pose a challenge for elicitation of the broadly neutralizing (bn) Abs and that identification of less somatically mutated bn Abs may help in the design of effective vaccine immunogens. In a quest for such bn Abs, phage- and yeast-displayed antibody libraries, constructed using peripheral blood mononuclear cells (PBMCs) from a patient with bn serum containing Abs targeting the epitope of the bn MAb 2F5, were panned against peptides containing the 2F5 epitope and against the HIV-1 gp140(JR-FL). Two MAbs (m66 and m66.6) were identified; the more mutated variant (m66.6) exhibited higher HIV-1-neutralizing activity than m66, although it was weaker than 2F5 in a TZM-bl cell assay. Binding of both MAbs to gp41 alanine substitution mutant peptides required the DKW(664-666) core of the 2F5 epitope and two additional upstream residues (L(660,663)). The MAbs have long (21-residue) heavy-chain third complementarity-determining regions (CDR-H3s), and m66.6 (but not m66) exhibited polyspecific reactivity to self- and non-self-antigens. Both m66 and m66.6 are significantly less divergent from their germ line Ab counterparts than 2F5--they have a total of 11 and 18 amino acid changes, respectively, from the closest VH and Vκ germ line gene products compared to 25 for 2F5. These new MAbs could help explore the complex maturation pathways involved in broad neutralization and its relationship with auto- and polyreactivity and may aid design of vaccine immunogens and development of therapeutics against HIV-1 infection.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".