NetBCE:An Interpretable Deep Neural Network for Accurate Prediction of Linear B-cell Epitopes
- Author:
Xu HAODONG
1
;
Zhao ZHONGMING
Author Information
1. Center for Precision Health,School of Biomedical Informatics,The University of Texas Health Science Center at Houston,Houston,TX 77030,USA
- Keywords:
B-cell epitope;
Immunotherapy;
Deep learning;
Machine learning;
Vaccine development
- From:
Genomics, Proteomics & Bioinformatics
2022;20(5):1002-1012
- CountryChina
- Language:Chinese
-
Abstract:
Identification of B-cell epitopes(BCEs)plays an essential role in the development of pep-tide 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 achieved high accuracy and robust performance with the average area under the curve(AUC)value of 0.8455 in five-fold cross-validation through automatically learning the informative classification features.NetBCE substantially outperformed the conventional ma-chine learning algorithms and other tools,with more than 22.06％improvement of AUC value com-pared to other tools using an independent dataset.Through investigating the output of important network modules in NetBCE,epitopes and non-epitopes tended to be presented in distinct regions with efficient feature representation along the network layer hierarchy.The NetBCE is freely avail-able at https://github.com/bsml320/NetBCE.
