PEPITO: improved discontinuous B-cell epitope prediction using multiple distance thresholds and half sphere exposure.
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.
Sweredoski
Michael J
MJ
Department of Computer Science and Institute for Genomics and Bioinformatics, University of California, Irvine, California 92697-3435, USA.
Baldi
Pierre
P
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
D018985:Epitopes, B-Lymphocyte