Journal
BIOINFORMATICS
Volume 24, Issue 12, Pages 1459-1460Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btn199
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Funding
- NLM NIH HHS [LM-07443-01] Funding Source: Medline
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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 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.
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