Bioinformatics Advance Access published online on April 28, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn199
PEPITO: Improved Discontinuous B-Cell Epitope Prediction Using Multiple Distance Thresholds and Half Sphere Exposure
1Department of Computer Science 2Institute for Genomics and Bioinformatics
*To whom correspondence should be addressed. Prof. Pierre Baldi, E-mail: pfbaldi{at}ics.uci.edu
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Motivation: 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 whenith 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 datasaset, achieving AUCs of 68.3 and 66.0 respectively.
Availability: PEPITO is available as part of the SCRATCH suite of protein structure predictors via www.igb.uci.edu
Associate Editor: Prof. Anna Tramontano
Received on March 3, 2008; revised on April 18, 2008; accepted on April 20, 2008