Antibody interface prediction with 3D Zernike descriptors and SVM.
Authors of this article are:
Daberdaku S, Ferrari C.
A summary of the article is shown below:
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 physicochemical 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: Linux binaries and Python scripts are available at https://github.com/sebastiandaberdaku/AntibodyInterfacePrediction. The datasets generated and/or analysed 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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