Papers › Antibody interface prediction with 3D Zernike descriptors and SVM
Antibody interface prediction with 3D Zernike descriptors and SVM
Sebastian Daberdaku, Carlo Ferrari
Motivation Antibodies are a class of proteins capable of specifically recognizing and binding to a virtually infinite number of antigens. This binding malleability makes them the most valuable category of biopharmaceuticals for both diagnostic and therapeutic applications. The correct identification of the antigen-binding residues in the antibody is crucial for all antibody design and engineering techniques and could also help to understand the complex antigen binding mechanisms. However, the antibody-binding interface prediction field appears to be still rather underdeveloped. Results We present a novel method for antibody interface prediction from their experimentally solved structures based on 3D Zernike Descriptors. Roto-translationally invariant descriptors are computed from circular patches of the antibody surface enriched with a chosen subset of physico-chemical properties from the AAindex1 amino acid index set, and are used as samples for a binary classification problem. An SVM classifier is used to distinguish interface surface patches from non-interface ones. The proposed method was shown to outperform other antigen-binding interface prediction software. Availability and implementation Linux binaries and Python scripts are available at https://github.com/sebastiandaberdaku/AntibodyInterfacePrediction. The datasets generated and/or analyzed during the current study are available at https://doi.org/10.6084/m9.figshare.5442229. Supplementary information Supplementary data are available at Bioinformatics online.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Antibody-antigen binding prediction | PECAN | Daberdaku | AUC-PR | 0.545 | #5 of 5 | Archive leaderboard | report |
| Antibody-antigen binding prediction | PECAN | Daberdaku | AUC-ROC | 0.923 | #5 of 5 | Archive leaderboard | report |
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