B-174 Turning Polyclonal Antibodies into Monoclonals: A Proteomics-Driven Approach
Bibliographic record
Abstract
Abstract Background Polyclonal antibodies (pAb), derived from immunized animals, are essential reagents used in antigen-specific experiments across diagnostics and research. However, pAb production faces challenges such as batch-to-batch variability, hindering research reproducibility and requires continued animal use and culling. One potential solution is determining the sequences of dominant antibody forms within a polyclonal mixture, enabling the development of recombinant monoclonal antibody mixtures. This approach allows for recombinant expression, replicating the binding characteristics of the original pAbs and promoting reproducibility in clinical research and laboratory medicine. Methods In this study, we employed a novel mass spectrometry-based method using the REpAb® polyclonal sequencing platform to sequence polyclonal antibodies directly from a purified protein mixture, completely de novo, without relying on prior sequence knowledge or databases. First, a purified polyclonal mixture was subjected to multi-enzyme digestion to generate shorter, overlapping peptides. Both middle-down and top-down mass spectrometry methods were then applied to analyze these peptides. The resulting data was de novo sequenced by tandem mass spectrometry (MS/MS), and the peptides were subsequently assembled into full-length antibody chains with correct pairing of the heavy and light antibody chains using machine learning-based bioinformatics. Protein lysates underwent analysis using an Orbitrap EclipseTM Series instrument (ThermoFisher Scientific, CA, US) coupled with the LC Evosep One (Evosep, Denmark). Results Following de novo sequencing of the polyclonal antibodies, chain assembly, and chain pairing, the dominant antibody forms within the pAb mixture were recombinantly expressed as monoclonal antibodies (mAbs) based on the derived sequences. The recombinant mAbs exhibited affinities in the mid-picomolar to low-nanomolar range and demonstrated comparable activity to the original polyclonal sample. Conclusion This study demonstrates the application of mass spectrometry and de novo protein sequencing technology to generate recombinant polyclonal antibody mixtures directly from a purified protein mixture. This approach enhances the application of polyclonal antibodies in research, laboratory, and diagnostic settings where reproducibility is essential. High affinity, antigen-specific antibodies were successfully captured and derived without the need for cell lines or nucleotide sequence data. By converting a polyclonal mixture into recombinant monoclonal forms, good antibody products can be immortalized and produced at a large scale, eliminating the irreproducibility issue inherent with pAbs.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".