title: Prediction of linear B-cell epitopes of hepatitis C virus for vaccine development.
abstract: BACKGROUND
  High genetic heterogeneity in the hepatitis C virus (HCV) is the major challenge of the development of an effective vaccine. Existing studies for developing HCV vaccines have mainly focused on T-cell immune response. However, identification of linear B-cell epitopes that can stimulate B-cell response is one of the major tasks of peptide-based vaccine development. Owing to the variability in B-cell epitope length, the prediction of B-cell epitopes is much more complex than that of T-cell epitopes. Furthermore, the motifs of linear B-cell epitopes in different pathogens are quite different (e. g. HCV and hepatitis B virus). To cope with this challenge, this work aims to propose an HCV-customized sequence-based prediction method to identify B-cell epitopes of HCV.
  
  
  RESULTS
  This work establishes an experimentally verified dataset comprising the B-cell response of HCV dataset consisting of 774 linear B-cell epitopes and 774 non B-cell epitopes from the Immune Epitope Database. An interpretable rule mining system of B-cell epitopes (IRMS-BE) is proposed to select informative physicochemical properties (PCPs) and then extracts several if-then rule-based knowledge for identifying B-cell epitopes. A web server Bcell-HCV was implemented using an SVM with the 34 informative PCPs, which achieved a training accuracy of 79.7% and test accuracy of 70.7% better than the SVM-based methods for identifying B-cell epitopes of HCV and the two general-purpose methods. This work performs advanced analysis of the 34 informative properties, and the results indicate that the most effective property is the alpha-helix structure of epitopes, which influences the connection between host cells and the E2 proteins of HCV. Furthermore, 12 interpretable rules are acquired from top-five PCPs and achieve a sensitivity of 75.6% and specificity of 71.3%. Finally, a conserved promising vaccine candidate, PDREMVLYQE, is identified for inclusion in a vaccine against HCV.
  
  
  CONCLUSIONS
  This work proposes an interpretable rule mining system IRMS-BE for extracting interpretable rules using informative physicochemical properties and a web server Bcell-HCV for predicting linear B-cell epitopes of HCV. IRMS-BE may also apply to predict B-cell epitopes for other viruses, which benefits the improvement of vaccines development of these viruses without significant modification. Bcell-HCV is useful for identifying B-cell epitopes of HCV antigen to help vaccine development, which is available at http://e045.life.nctu.edu.tw/BcellHCV.
authors.forename: Wen-Lin
authors.initials: WL
authors.lastname: Huang
authors.forename: Ming-Ju
authors.initials: MJ
authors.lastname: Tsai
authors.forename: Kai-Ti
authors.initials: KT
authors.lastname: Hsu
authors.forename: Jyun-Rong
authors.initials: JR
authors.lastname: Wang
authors.forename: Yi-Hsiung
authors.initials: YH
authors.lastname: Chen
authors.forename: Shinn-Ying
authors.initials: SY
authors.lastname: Ho
mesh_terms: D000818:Animals; D055598:Chemical Phenomena; D019295:Computational Biology / Q000379:methods*; D018604:Epitope Mapping; D018985:Epitopes, B-Lymphocyte / Q000737:chemistry / Q000276:immunology*; D016174:Hepacivirus / Q000276:immunology*; D006801:Humans; D020407:Internet; D008958:Models, Molecular; D017433:Protein Structure, Secondary; D014765:Viral Vaccines / Q000276:immunology*
chemical_list: D018985:Epitopes, B-Lymphocyte; D014765:Viral Vaccines