title: ElliPro: a new structure-based tool for the prediction of antibody epitopes.
abstract: BACKGROUND
  Reliable prediction of antibody, or B-cell, epitopes remains challenging yet highly desirable for the design of vaccines and immunodiagnostics. A correlation between antigenicity, solvent accessibility, and flexibility in proteins was demonstrated. Subsequently, Thornton and colleagues proposed a method for identifying continuous epitopes in the protein regions protruding from the protein's globular surface. The aim of this work was to implement that method as a web-tool and evaluate its performance on discontinuous epitopes known from the structures of antibody-protein complexes.
  
  
  RESULTS
  Here we present ElliPro, a web-tool that implements Thornton's method and, together with a residue clustering algorithm, the MODELLER program and the Jmol viewer, allows the prediction and visualization of antibody epitopes in a given protein sequence or structure. ElliPro has been tested on a benchmark dataset of discontinuous epitopes inferred from 3D structures of antibody-protein complexes. In comparison with six other structure-based methods that can be used for epitope prediction, ElliPro performed the best and gave an AUC value of 0.732, when the most significant prediction was considered for each protein. Since the rank of the best prediction was at most in the top three for more than 70% of proteins and never exceeded five, ElliPro is considered a useful research tool for identifying antibody epitopes in protein antigens. ElliPro is available at http://tools.immuneepitope.org/tools/ElliPro.
  
  
  CONCLUSIONS
  The results from ElliPro suggest that further research on antibody epitopes considering more features that discriminate epitopes from non-epitopes may further improve predictions. As ElliPro is based on the geometrical properties of protein structure and does not require training, it might be more generally applied for predicting different types of protein-protein interactions.
authors.affiliation: San Diego Supercomputer Center, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA. jpon@sdsc.edu
authors.forename: Julia
authors.initials: J
authors.lastname: Ponomarenko
authors.forename: Huynh-Hoa
authors.initials: HH
authors.lastname: Bui
authors.forename: Wei
authors.initials: W
authors.lastname: Li
authors.forename: Nicholas
authors.initials: N
authors.lastname: Fusseder
authors.forename: Philip E
authors.initials: PE
authors.lastname: Bourne
authors.forename: Alessandro
authors.initials: A
authors.lastname: Sette
authors.forename: Bjoern
authors.initials: B
authors.lastname: Peters
mesh_terms: D000465:Algorithms; D001666:Binding Sites, Antibody / Q000235:genetics; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D018604:Epitope Mapping / Q000379:methods*; D018985:Epitopes, B-Lymphocyte / Q000737:chemistry*; D006801:Humans; D020407:Internet; D018448:Models, Immunological; D011487:Protein Conformation; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software; D014584:User-Computer Interface
chemical_list: D018985:Epitopes, B-Lymphocyte