An improved dual‐expression concept, generating high‐quality antibodies for proteomics research
Bibliographic record
Abstract
A novel, improved dual bacterial-expression system, designed for large-scale generation of high-quality polyclonal antibody preparations intended for proteomics research, is presented. The concept involves parallel expression of cDNA-encoded proteins, as a fusion with two different tags in two separate vector systems. Both systems enable convenient blotting procedures for expression screening on crude bacterial cell cultures and single-step affinity purification under denaturing conditions. One of the fusion proteins is used to elicit antibodies, and the second fusion protein is used in an immobilized form as an affinity ligand to enrich antibodies with selective reactivity to the cDNA-encoded part, common for the two fusion proteins. To evaluate the system, four cDNA clones from putative nuclear proteins from the non-biting midge Chironomus tentans were expressed. Antibodies to these cDNA-encoded proteins were generated, enriched and used in blotting and immunofluorescence procedures to determine expression patterns for the native proteins corresponding to the cDNAs. The four antibody preparations showed specific reactivity to the corresponding recombinant cDNA-encoded proteins, and three of the four antibodies gave specific staining in Western-blot analysis of nuclear cell extracts. Furthermore, two of the antibody preparations gave specific staining in immunofluorescence analysis of C. tentans cells. We conclude that the dual-vector concept presented offers a highly stringent strategy for the generation of monospecific polyclonal antibodies, which are useful in proteomics research.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".