Dataset Information

Staged heterogeneity learning to identify conformational B-cell epitopes from antigen sequences.

ABSTRACT: The broad heterogeneity of antigen-antibody interactions brings tremendous challenges to the design of a widely applicable learning algorithm to identify conformational B-cell epitopes. Besides the intrinsic heterogeneity introduced by diverse species, extra heterogeneity can also be introduced by various data sources, adding another layer of complexity and further confounding the research.This work proposed a staged heterogeneity learning method, which learns both characteristics and heterogeneity of data in a phased manner. The method was applied to identify antigenic residues of heterogenous conformational B-cell epitopes based on antigen sequences. In the first stage, the model learns the general epitope patterns of each kind of propensity from a large data set containing computationally defined epitopes. In the second stage, the model learns the heterogenous complementarity of these propensities from a relatively small guided data set containing experimentally determined epitopes. Moreover, we designed an algorithm to cluster the predicted individual antigenic residues into conformational B-cell epitopes so as to provide strong potential for real-world applications, such as vaccine development. With heterogeneity well learnt, the transferability of the prediction model was remarkably improved to handle new data with a high level of heterogeneity. The model has been tested on two data sets with experimentally determined epitopes, and on a data set with computationally defined epitopes. This proposed sequence-based method achieved outstanding performance - about twice that of existing methods, including the sequence-based predictor CBTOPE and three other structure-based predictors.The proposed method uses only antigen sequence information, and thus has much broader applications.

SUBMITTER: Ren J

PROVIDER: S-EPMC5374683 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Staged heterogeneity learning to identify conformational B-cell epitopes from antigen sequences.

Ren Jing J Song Jiangning J Ellis John J Li Jinyan J

BMC genomics 20170314 Suppl 2

<h4>Background</h4>The broad heterogeneity of antigen-antibody interactions brings tremendous challenges to the design of a widely applicable learning algorithm to identify conformational B-cell epitopes. Besides the intrinsic heterogeneity introduced by diverse species, extra heterogeneity can also be introduced by various data sources, adding another layer of complexity and further confounding the research.<h4>Results</h4>This work proposed a staged heterogeneity learning method, which learns ...[more]

PMID: 28361709

Similar Datasets

Project description:BackgroundPrecise determination of conformational epitopes of neutralizing antibodies represents a key step in the rational design of novel vaccines. A powerful experimental method to gain insights on the physical chemical nature of conformational epitopes is the selection of linear peptides that bind with high affinities to a monoclonal antibody of interest by phage display technology. However, the structural characterization of conformational epitopes from these mimotopes is not straightforward, and in the past the interpretation of peptide sequences from phage display experiments focused on linear sequence analysis to find a consensus sequence or common sequence motifs.ResultsWe present a fully automated search method, EpiSearch that predicts the possible location of conformational epitopes on the surface of an antigen. The algorithm uses peptide sequences from phage display experiments as input, and ranks all surface exposed patches according to the frequency distribution of similar residues in the peptides and in the patch. We have tested the performance of the EpiSearch algorithm for six experimental data sets of phage display experiments, the human epidermal growth factor receptor-2 (HER-2/neu), the antibody mAb Bo2C11 targeting the C(2) domain of FVIII, antibodies mAb 17b and mAb b12 of the HIV envelope protein gp120, mAb 13b5 targeting HIV-1 capsid protein and 80R of the SARS coronavirus spike protein. In all these examples the conformational epitopes as determined by the X-ray crystal structures of the antibody-antigen complexes, were found within the highest scoring patches of EpiSearch, covering in most cases more than 50% residues of experimental observed conformational epitopes. Input options of the program include mapping of a single peptide or a set of peptides on the antigen structure, and the results of the calculation can be visualized on our interactive web server.AvailabilityUsers can access the EpiSearch from our web server http://curie.utmb.edu/episearch.html.

Dataset Information

Staged heterogeneity learning to identify conformational B-cell epitopes from antigen sequences.

Publications

Staged heterogeneity learning to identify conformational B-cell epitopes from antigen sequences.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets