PEPITO: improved discontinuous B-cell epitope prediction using multiple distance thresholds and half sphere exposure
PEPITO: improved discontinuous B-cell epitope prediction using multiple distance thresholds and half sphere exposure
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DOI：
10.1093/bioinformatics/btn199
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发表时间：
2008-06-15
期刊：
影响因子：
5.8
通讯作者：
Baldi, Pierre
中科院分区：
文献类型：
--
作者：
Sweredoski, Michael J.;Baldi, Pierre
动机：准确预测B细胞表位是计算免疫学的一个重要目标。高达90%的B细胞表位本质上是不连续的，但大多数预测因子集中在线性表位上。即使当抗原的三级结构是可用的，B细胞表位的准确预测仍然challenging.Results：我们的预测，PEPITO，使用的氨基酸倾向分数和半球曝光值在多个距离的组合，以实现国家的最先进的性能。PEPITO在Discotope数据集上的曲线下面积（AUC）为75.4。此外，我们在最近的Epitome数据集上对PEPITO和Discotope预测器进行了基准测试，分别达到了68.3和66.0的AUC。
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.