Faculty, Staff and Student Publications
Publication Date
10-1-2022
Journal
Genomics, Proteomics & Bioinformatics
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 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 machine learning algorithms and other tools, with more than 22.06% improvement of AUC value compared 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 available at https://github.com/bsml320/NetBCE.
Keywords
Humans, Epitopes, B-Lymphocyte, Neural Networks, Computer, Algorithms, B-cell epitope, Immunotherapy, Deep learning, Machine learning, Vaccine development
DOI
10.1016/j.gpb.2022.11.009
PMID
36526218
PMCID
PMC10025766
PubMedCentral® Posted Date
12-13-2022
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
