High efficiency reduction capability for the formation of Fab׳ antibody fragments from F(ab)2 units
Bibliographic record
Abstract
Antibodies have widespread applications in areas ranging from therapeutics to chromatography and protein microarrays. Certain applications require only the fragment antigen-binding (Fab) units of the protein. This study compares the cleavage efficacy of dithiothreitol (DTT), mercaptoethylamine (MEA), and dithiobutylamine (DTBA) – a relatively new reducing agent synthesized in 2012. Pseudo-first order kinetic analyses show DTBA to be ~213 times faster than DTT and ~71 times faster than MEA in the formation of Fab׳ antibody fragments from polyclonal rabbit antibodies. Monoclonal mouse antibodies were also used to show the feasibility of the reduction process on antibodies from a different species and with a different clonality. DTBA cleaved the monoclonal mouse F(ab)2 units most efficiently, ~2 times faster than DTT ~10 times faster than MEA. Due to the extremely quick reactivity of all the reducing agents in the first five minutes of monoclonal antibody reductions as well as for the DTBA reductions of the polyclonal rabbit antibodies, the pseudo-first order kinetic analyses should be interpreted qualitatively for these results. Nucleophilic sulfides on Fab׳ fragments are preserved in the DTBA reduction process, demonstrated by their reactivity with Ellman׳s reagent. Degradation of the Fab׳ fragments was observed with the monoclonal mouse antibodies after reduction with DTBA or DTT. In conclusion, DTBA is the more efficient reducing agent compared to DTT and MEA, however, the reduction process should be optimized as degradation of the Fab׳ fragments is possible.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".