Antibody interface prediction with 3D Zernike descriptors and SVM
0301 basic medicine
03 medical and health sciences
Support Vector Machine
Proteins
Amino Acids
Antibodies
Software
DOI:
10.1093/bioinformatics/bty918
Publication Date:
2018-11-01T20:54:42Z
AUTHORS (2)
ABSTRACT
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.
SUPPLEMENTAL MATERIAL
Coming soon ....
REFERENCES (48)
CITATIONS (87)
EXTERNAL LINKS
PlumX Metrics
RECOMMENDATIONS
FAIR ASSESSMENT
Coming soon ....
JUPYTER LAB
Coming soon ....