Antibody interface prediction with 3D Zernike descriptors and SVM
Abstract
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.
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Antibody i-Patch and Paratome were outperformed by machine learning approaches such as proABC, AntibodyInterfacePrediction, Parapred and AG-Fast-Parapred.
“Recently, Antibody i-Patch and Paratome were outperformed by machine learning approaches such as the random forest-based proABC [112], support-vector machine-based AntibodyInterfacePrediction [114] and the deep learning-based, Parapred [113] and AG-Fast-Parapred [115].”
From Computational approaches to therapeutic antibody design: established methods and emerging trends (2020) · cites this paper as [114]
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Computational approaches to therapeutic antibody design: established methods and emerging trends
Norman, R. A., Ambrosetti, F., Bonvin, A. M. J. J., Colwell, L. J., Kelm, S., Kumar, S., & Krawczyk, K. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbz095 · Cited by 219 · see citing works
“To the best of our knowledge AntibodyInterfacePrediction and AG-FastParapred are currently the best performing paratope prediction methods as compared to previously available methods; however, they were not compared against one another.”
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- Authors
- Daberdaku, S., & Ferrari, C.
- Affiliations
- University of Padua (IT)
- Corresponding author
- Daberdaku, Sebastian
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-11-05
- Volume
- 35
- Issue
- 11
- Pages
- 1870-1876
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2900437486
- Cited by
- 88
- References
- 48
- Citations by year
- 2025: 15 · 2024: 17 · 2023: 13 · 2022: 15 · 2021: 12 · 2020: 10 · 2019: 6
- FWCI
- 2.49
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- Padua Research Archive (University of Padova) (submittedVersion)
- Funders
- University of Padova (CPDR150813/15)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Keywords
- surface patches, support vector machine classification, antibody-antigen binding, protein structure-based prediction
- MeSH terms
- Amino Acids, Antibodies, Proteins, Software, Support Vector Machine
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