佩皮托：使用多个距离阈值和半球暴露改进的不连续B细胞表位预测

PEPITO: improved discontinuous B-cell epitope prediction using multiple distance thresholds and half sphere exposure.

作者信息

Sweredoski Michael J, Baldi Pierre

机构信息

Department of Computer Science and Institute for Genomics and Bioinformatics, University of California, Irvine, California 92697-3435, USA.

出版信息

Bioinformatics. 2008 Jun 15;24(12):1459-60. doi: 10.1093/bioinformatics/btn199. Epub 2008 Apr 28.

DOI:10.1093/bioinformatics/btn199

PMID:18443018

Abstract

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.

AVAILABILITY

PEPITO is available as part of the SCRATCH suite of protein structure predictors via www.igb.uci.edu.

CONTACT

pfbaldi@ics.uci.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

摘要

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，但大多数预测器专注于线性表位。即使抗原的三级结构已知，准确预测B细胞表位仍然具有挑战性。

结果

我们的预测器PEPITO使用氨基酸倾向得分和多个距离处的半球暴露值的组合，以实现领先的性能。PEPITO在Discotope数据集上的曲线下面积（AUC）达到75.4。此外，我们在更新的Epitome数据集上对PEPITO以及Discotope预测器进行了基准测试，其AUC分别为68.3和66.0。