{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/antibody-interface-prediction-with-3d-zernike","title":"Antibody interface prediction with 3D Zernike descriptors and SVM","arxiv_id":null,"date":"2018-10-05","proceeding":"Bioinformatics 2018 10","authors":["Sebastian Daberdaku","Carlo Ferrari"],"abstract":"Motivation\r\nAntibodies 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.\r\n\r\nResults\r\nWe 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.\r\n\r\nAvailability and implementation\r\nLinux 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.\r\n\r\nSupplementary information\r\nSupplementary data are available at Bioinformatics online.","url_abs":"https://academic.oup.com/bioinformatics/article/35/11/1870/5161081","url_pdf":"https://academic.oup.com/bioinformatics/article-pdf/35/11/1870/48934927/bioinformatics_35_11_1870.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"antibody-interface-prediction-with-3d-zernike","repo_url":"https://github.com/sebastiandaberdaku/AntibodyInterfacePrediction","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"antibody-antigen-binding-prediction","task_name":"Antibody-antigen binding prediction"},{"task_slug":"binary-classification","task_name":"Binary Classification"},{"task_slug":"diagnostic","task_name":"Diagnostic"},{"task_slug":"prediction","task_name":"Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/antibody-antigen-binding-prediction-on-pecan","task":"Antibody-antigen binding prediction","dataset":"PECAN","model":"Daberdaku","rank_in_archive_order":5,"of":5,"metrics":{"AUC-PR":"0.545","AUC-ROC":"0.923"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}