Epitope Mapping of Protein Antigens by Expression-PCR (E-PCR)
Bibliographic record
Abstract
It is often the case that one has a cloned gene for a protein and a monoclonal antibody (MAb) that recognizes an epitope within that protein. Determination of the amino acid sequences constituting an epitope recognized by the MAb can be problematic. Producing ever and ever smaller fragments of the protein in vivo using expression systems, such as bacteria, yeast, or baculovirus, leads to problems of having the expression system influence the protein, i.e., correct folding, glycosylation, and degradation. In some cases, the foreign protein may in fact be toxic to the cell, thereby making synthesis impossible. In addition, each new fragment in the study has to be constructed, usually by PCR from the gene, cloned, and then sequenced to check for fidelity against the parent sequence to avoid PCR errors. To detect the newly expressed protein by immunoprecipitation, radiolabeled amino acids are usually incorporated into the new product, but at the same time this label goes into all other host proteins. This can be further complicated if MAb crossreactivity or coprecipitation occurs. Most of these problems can be avoided by epitope mapping using proteins synthesized by expression-PCR (E-PCR) ( 1 ). E-PCR is a rapid and simple method for the in vitro production of proteins without having to go through the rigors of cloning. The resulting radiochemically pure proteins are useful for a variety of purposes that include studies on the subunit structure of proteins, epitope mapping, and protein mutagenesis. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.013 |
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".