A Structure-Based B-cell Epitope Prediction Model Through Combing Local and Global Features.

ABSTRACT: B-cell epitopes (BCEs) are a set of specific sites on the surface of an antigen that binds to an antibody produced by B-cell. The recognition of BCEs is a major challenge for drug design and vaccines development. Compared with experimental methods, computational approaches have strong potential for BCEs prediction at much lower cost. Moreover, most of the currently methods focus on using local information around target residue without taking the global information of the whole antigen sequence into consideration. We propose a novel deep leaning method through combing local features and global features for BCEs prediction. In our model, two parallel modules are built to extract local and global features from the antigen separately. For local features, we use Graph Convolutional Networks (GC

SUBMITTER: Lu S 

PROVIDER: S-EPMC9283778 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature