<i>de Novo</i> Sequencing of Antibodies for Identification of Neutralizing Antibodies in Human Plasma Post SARS-CoV-2 Vaccination
Bibliographic record
Abstract
Abstract We present a method for sequencing polyclonal IgG enriched from human plasma, employing a combination of de novo sequencing, proteomics, bioinformatics, protein separation, sequencing, and peptide separations. Our study analyzes a single patient’s IgG antibody response triggered by the Moderna Spikevax mRNA COVID-19 vaccine. From the sequencing data of the natural polyclonal response to vaccination, we generated 12 recombinant antibodies. Six derived recombinant antibodies, including four generated with de novo sequencing, exhibited similar or higher binding affinities than the original natural polyclonal antibody. Our neutralization tests revealed that the six antibodies possess neutralizing capabilities against the target antigen. This research provides insights into sequencing polyclonal IgG antibodies while highlighting the effectiveness and potential of our approach in generating recombinant antibodies with robust binding affinity and neutralization capabilities. Our proposed approach is an advancement in characterizing the IgG response by directly investigating the circulating pool of IgG without relying exclusively on the B-cell repertoire or population. This is crucial as the B-cell analysis may not accurately represent the circulating antibodies. Interestingly, a large proportion (80 to 90%) of the human antibody sequences generated against SARS-CoV-2 in the literature have been derived solely from B-cell analysis. Therefore, the ability to offer a different perspective is crucial in gaining a comprehensive understanding of the IgG response. Significance Statement We investigate human IgG targeting the receptor binding domain using de novo proteomics. The peripheral B-cell repertoire may not adequately cover all the circulating IgG for human IgG sequencing. Our approach overcomes this limitation by using a de novo protein sequencing on top of standard proteomics. We obtained distinct de novo sequences, showcasing our method’s potential. The recombinant proteins we generate possess traits comparable to or surpassing the naturally occurring polyclonal antibodies (pAbs). This study highlights similarities and differences between IgG populations in blood and circulating B-cells, which is crucial for future biologics development.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".