Automated Identification of Complementarity Determining Regions (CDRs) Reveals Peculiar Characteristics of CDRs and B Cell Epitopes
Bibliographic record
Abstract
Exact identification of complementarity determining regions (CDRs) is crucial for understanding and manipulating antigenic interactions. One way to do this is by marking residues on the antibody that interact with B cell epitopes on the antigen. This, of course, requires identification of B cell epitopes, which could be done by marking residues on the antigen that bind to CDRs, thus requiring identification of CDRs. To circumvent this vicious circle, existing tools for identifying CDRs are based on sequence analysis or general biophysical principles. Often, these tools, which are based on partial data, fail to agree on the boundaries of the CDRs. Herein we present an automated procedure for identifying CDRs and B cell epitopes using consensus structural regions that interact with the antigens in all known antibody-protein complexes. Consequently, we provide the first comprehensive analysis of all CDR-epitope complexes of known three-dimensional structure. The CDRs we identify only partially overlap with the regions suggested by existing methods. We found that the general physicochemical properties of both CDRs and B cell epitopes are rather peculiar. In particular, only four amino acids account for most of the sequence of CDRs, and several types of amino acids almost never appear in them. The secondary structure content and the conservation of B cell epitopes are found to be different than previously thought. These characteristics of CDRs and epitopes may be instrumental in choosing which residues to mutate in experimental search for epitopes. They may also assist in computational design of antibodies and in predicting B cell epitopes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".