NetBCE: An Interpretable Deep Neural Network for Accurate Prediction of Linear B-cell Epitopes.

ABSTRACT: Identification of B-cell epitopes (BCEs) plays an essential role in the development of peptide vaccines and immuno-diagnostic reagents, as well as antibody design and production. In this work, we generated a large benchmark dataset comprising 124,879 experimentally supported linear epitope-containing regions in 3567 protein clusters from over 1.3 million B cell assays. Analysis of this curated dataset showed large pathogen diversity covering 176 different families. The accuracy in linear BCE prediction was found to strongly vary with different features, while all sequence-derived and structural features were informative. To search more efficient and interpretive feature representations, a ten-layer deep learning framework for linear BCE prediction, namely NetBCE, was developed. NetBCE achi

SUBMITTER: Xu H 

PROVIDER: S-EPMC10025766 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature