1. Leveraging Sequential and Spatial Neighbors Information by Using CNNs Linked With GCNs for Paratope Prediction
- Author
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Shoutao Zhang, Yuguang Li, Xiaofei Nan, Fei Wang, and Shuai Lu
- Subjects
chemistry.chemical_classification ,Artificial neural network ,biology ,Computer science ,Applied Mathematics ,Proteins ,Sequence (biology) ,Computational biology ,Antibodies ,Convolution ,Amino acid ,Biological drugs ,Immune system ,chemistry ,Antigen ,Genetics ,biology.protein ,Graph (abstract data type) ,Paratope ,Binding Sites, Antibody ,Neural Networks, Computer ,Amino acid residue ,Antibody ,Algorithms ,Biotechnology - Abstract
Antibodies consisting of variable and constant regions, are a special type of proteins playing a vital role in immune system of the vertebrate. They have the remarkable ability to bind a large range of diverse antigens with extraordinary affinity and specificity. This malleability of binding makes antibodies an important class of biological drugs and biomarkers. In this article, we propose a method to identify which amino acid residues of an antibody directly interact with its associated antigen based on the features from sequence and structure. Our algorithm uses convolution neural networks (CNNs) linked with graph convolution networks (GCNs) to make use of information from both sequential and spatial neighbors to understand more about the local environment of target amino acid residue. Furthermore, we process the antigen partner of an antibody by employing an attention layer. Our method improves on the state-of-the-art methodology.
- Published
- 2022
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