title: 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.
authors.affiliation: Advanced Analytics Institute, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia.|College of Computer, National University of Defense Technology, Changsha, 410073, China.
authors.forename: Jing
authors.initials: J
authors.lastname: Ren
authors.affiliation: Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia.|Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Melbourne, VIC 3800, Australia.
authors.forename: Jiangning
authors.initials: J
authors.lastname: Song
authors.affiliation: School of Life Sciences, University of Technology Sydney, Ultimo, NSW 2007, Australia.
authors.forename: John
authors.initials: J
authors.lastname: Ellis
authors.affiliation: Advanced Analytics Institute and Centre for Health Technologies, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia. jinyan.li@uts.edu.au.
authors.forename: Jinyan
authors.initials: J
authors.lastname: Li
mesh_terms: D000936:Antigen-Antibody Complex / Q000737:chemistry* / Q000276:immunology; D000941:Antigens / Q000737:chemistry* / Q000276:immunology; D001402:B-Lymphocytes / Q000737:chemistry / Q000276:immunology; D001665:Binding Sites; D018604:Epitope Mapping; D018985:Epitopes, B-Lymphocyte / Q000737:chemistry* / Q000276:immunology; D006801:Humans; D015233:Models, Statistical; D011485:Protein Binding; D000072756:Protein Conformation, alpha-Helical; D000072757:Protein Conformation, beta-Strand; D054730:Protein Interaction Domains and Motifs; D000069553:Supervised Machine Learning
chemical_list: D000936:Antigen-Antibody Complex; D000941:Antigens; D018985:Epitopes, B-Lymphocyte
keywords: B-cell epitope; Conformational epitope; Epitope prediction; Sequence-based; Staged heterogeneity learning