A de Novo Designed Template for Generating Conformation-specific Antibodies That Recognize α-Helices in Proteins
Bibliographic record
Abstract
The generation of antibodies directed toward the surface-exposed regions of a protein using synthetic peptides as immunogens representing surface loops and turns has been widely successful. However, peptides representing alpha-helical regions are typically unstructured in solution and unable to produce antibodies that recognize alpha-helices in native proteins. We describe a de novo designed parallel two-stranded alpha-helical coiled-coil template for immunization to prepare antibodies that recognize alpha-helical protein sequences in the native protein. This template was designed for maximum stability through an Ile/Leu hydrophobic core and an interchain disulfide bridge. Surface-exposed helical residues are inserted into the template and used for immunization to generate polyclonal antibodies. To demonstrate the feasibility of this approach, 15 residues of the yeast transcription factor GCN4 were inserted into this template, and the resultant antibodies were screened for conformational specificity. Peptide antigens that contain the same surface-exposed residues but differ in structure were used as competitors in a competition assay. Direct competition between the capture peptide immobilized on a biosensor chip, the peptide antigens, and the antibodies generated by the template demonstrated that the antibodies were specific for helical structure in the native coiled-coil (synthetic GCN4 residues 250-280). These antibodies were unable to recognize the same inserted sequence in an unstructured analog. The helix-specific antibodies were also able to identify native GCN4 (31.3 kDa) from yeast whole cell extracts.
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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".