title: PEPITO: improved discontinuous B-cell epitope prediction using multiple distance thresholds and half sphere exposure.
abstract: BACKGROUND
  Accurate prediction of B-cell epitopes is an important goal of computational immunology. Up to 90% of B-cell epitopes are discontinuous in nature, yet most predictors focus on linear epitopes. Even when the tertiary structure of the antigen is available, the accurate prediction of B-cell epitopes remains challenging.
  
  
  RESULTS
  Our predictor, PEPITO, uses a combination of amino-acid propensity scores and half sphere exposure values at multiple distances to achieve state-of-the-art performance. PEPITO achieves an area under the curve (AUC) of 75.4 on the Discotope dataset. Additionally, we benchmark PEPITO as well as the Discotope predictor on the more recent Epitome dataset, achieving AUCs of 68.3 and 66.0, respectively.
  
  
  BACKGROUND
  PEPITO is available as part of the SCRATCH suite of protein structure predictors via www.igb.uci.edu.
  
  
  BACKGROUND
  pfbaldi@ics.uci.edu
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Computer Science and Institute for Genomics and Bioinformatics, University of California, Irvine, California 92697-3435, USA.
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Sweredoski
authors.forename: Pierre
authors.initials: P
authors.lastname: Baldi
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D018604:Epitope Mapping / Q000379:methods*; D018985:Epitopes, B-Lymphocyte / Q000737:chemistry* / Q000276:immunology*; D008969:Molecular Sequence Data; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software
chemical_list: D018985:Epitopes, B-Lymphocyte