title: Tertiary structure-based prediction of conformational B-cell epitopes through B factors.
abstract: BACKGROUND
  B-cell epitope is a small area on the surface of an antigen that binds to an antibody. Accurately locating epitopes is of critical importance for vaccine development. Compared with wet-lab methods, computational methods have strong potential for efficient and large-scale epitope prediction for antigen candidates at much lower cost. However, it is still not clear which features are good determinants for accurate epitope prediction, leading to the unsatisfactory performance of existing prediction methods.
  
  
  RESULTS
  We propose a much more accurate B-cell epitope prediction method. Our method uses a new feature B factor (obtained from X-ray crystallography), combined with other basic physicochemical, statistical, evolutionary and structural features of each residue. These basic features are extended by a sequence window and a structure window. All these features are then learned by a two-stage random forest model to identify clusters of antigenic residues and to remove isolated outliers. Tested on a dataset of 55 epitopes from 45 tertiary structures, we prove that our method significantly outperforms all three existing structure-based epitope predictors. Following comprehensive analysis, it is found that features such as B factor, relative accessible surface area and protrusion index play an important role in characterizing B-cell epitopes. Our detailed case studies on an HIV antigen and an influenza antigen confirm that our second stage learning is effective for clustering true antigenic residues and for eliminating self-made prediction errors introduced by the first-stage learning.
  
  
  METHODS
  Source codes are available on request.
authors.affiliation: Advanced Analytics Institute and Centre for Health Technologies and Department of Molecular Science, University of Technology Sydney, Broadway, NSW 2007, Australia.
authors.forename: Jing
authors.initials: J
authors.lastname: Ren
authors.affiliation: Advanced Analytics Institute and Centre for Health Technologies and Department of Molecular Science, University of Technology Sydney, Broadway, NSW 2007, Australia.
authors.forename: Qian
authors.initials: Q
authors.lastname: Liu
authors.affiliation: Advanced Analytics Institute and Centre for Health Technologies and Department of Molecular Science, University of Technology Sydney, Broadway, NSW 2007, Australia.
authors.forename: John
authors.initials: J
authors.lastname: Ellis
authors.affiliation: Advanced Analytics Institute and Centre for Health Technologies and Department of Molecular Science, University of Technology Sydney, Broadway, NSW 2007, Australia.
authors.forename: Jinyan
authors.initials: J
authors.lastname: Li
mesh_terms: D001185:Artificial Intelligence; D018360:Crystallography, X-Ray; D018985:Epitopes, B-Lymphocyte / Q000737:chemistry*; D015699:HIV Envelope Protein gp120 / Q000737:chemistry / Q000276:immunology; D019267:Hemagglutinin Glycoproteins, Influenza Virus / Q000737:chemistry / Q000276:immunology; D008958:Models, Molecular; D020836:Protein Structure, Quaternary; D017434:Protein Structure, Tertiary
chemical_list: D018985:Epitopes, B-Lymphocyte; D015699:HIV Envelope Protein gp120; D019267:Hemagglutinin Glycoproteins, Influenza Virus; C514793:gp120 protein, Human immunodeficiency virus 1