De novo identification of VRC01 class HIV-1–neutralizing antibodies by next-generation sequencing of B-cell transcripts
Bibliographic record
Abstract
Next-generation sequencing of antibody transcripts provides a wealth of data, but the ability to identify function-specific antibodies solely on the basis of sequence has remained elusive. We previously characterized the VRC01 class of antibodies, which target the CD4-binding site on gp120, appear in multiple donors, and broadly neutralize HIV-1. Antibodies of this class have developmental commonalities, but typically share only ∼50% amino acid sequence identity among different donors. Here we apply next-generation sequencing to identify VRC01 class antibodies in a new donor, C38, directly from B cell transcript sequences. We first tested a lineage rank approach, but this was unsuccessful, likely because VRC01 class antibody sequences were not highly prevalent in this donor. We next identified VRC01 class heavy chains through a phylogenetic analysis that included thousands of sequences from C38 and a few known VRC01 class sequences from other donors. This "cross-donor analysis" yielded heavy chains with little sequence homology to previously identified VRC01 class heavy chains. Nonetheless, when reconstituted with the light chain from VRC01, half of the heavy chain chimeric antibodies showed substantial neutralization potency and breadth. We then identified VRC01 class light chains through a five-amino-acid sequence motif necessary for VRC01 light chain recognition. From over a million light chain sequences, we identified 13 candidate VRC01 class members. Pairing of these light chains with the phylogenetically identified C38 heavy chains yielded functional antibodies that effectively neutralized HIV-1. Bioinformatics analysis can thus directly identify functional HIV-1-neutralizing antibodies of the VRC01 class from a sequenced antibody repertoire.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".