Do-it-yourself <i>de novo</i> antibody sequencing workflow that achieves complete accuracy of the variable regions
Bibliographic record
Abstract
ABSTRACT Antibodies are widely used as research tools or therapeutic agents. Knowing the sequences of the variable regions of an antibody—both the heavy chain and the light chain—is a prerequisite for the production of recombinant antibodies. Mass spectrometry-based de novo sequencing is a frequently used, and sometimes the only approach to gaining this information. Here, we describe a workflow that enables accurate sequence determination of monoclonal antibodies based on mass spectrometry data and freely available software tools. This workflow, which we developed using a homemade anti-FLAG monoclonal antibody as a reference sample, achieved 100% accuracy of the variable regions with clear distinction between leucine (L) and isoleucine (I). Using this workflow, we successfully decoded a monoclonal anti-HA antibody, for which we had no prior knowledge of its sequence. Based on the de novo sequencing result, we generated a recombinant anti-HA antibody, and demonstrated that it has the same specificity, sensitivity, and affinity as the commercial antibody. Abstract Figure
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.027 |
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".