期刊
BIOINFORMATICS
卷 35, 期 11, 页码 1870-1876出版社
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/bty918
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- University of Padova [CPDR150813/15]
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 noninterface ones. The proposed method was shown to outperform other antigen-binding interface prediction software.
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