DiSSiMiL: Diverse Small Size Mini‐Libraries applied to simple and rapid epitope mapping of a monoclonal antibody
Bibliographic record
Abstract
Methods for screening protein-protein interactions are useful in protein science and for the generation of drug leads. We set out to develop a simplified assay to rapidly test protein-protein interactions, with a library of 400 pentapeptides comprising the 20 natural amino acids at two variable positions followed by three glycines (NH2-X1X2GGG). The library was used to identify the epitope of monoclonal antibody (mAb) 10D11 directed against the HOXD4 protein. Three pentapeptide 'hits' were selected (VYGGG, PWGGG and WKGGG) from direct binding assays screening for pentapeptide-mAb interactions; and from assays using pentapeptides in solution to competitively block HOXD4-mAb interactions. Alignment of the three 'hit' pentapeptides to the HOXD4 sequence predicts the mAb 10D11 epitope as NH2-VYPWMK. Synthesis of NH2-VYPWMK hexapeptide confirmed this prediction; and an alanine scan of HOXD4 ablated binding by mAb 10D11 when amino acids in the putative epitope were mutated. We propose that these simplified but diverse libraries can be used for rapid epitope mapping of some mAbs, and for generating lead small peptide analogs that interfere with receptor-ligand or other protein-protein interactions, or with enzymatic activity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".