Wang Hsin-Wei, Lin Ya-Chi, Pai Tun-Wen, Chang Hao-Teng
Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung, Taiwan.
J Biomed Biotechnol. 2011;2011:432830. doi: 10.1155/2011/432830. Epub 2011 Aug 23.
Epitopes are antigenic determinants that are useful because they induce B-cell antibody production and stimulate T-cell activation. Bioinformatics can enable rapid, efficient prediction of potential epitopes. Here, we designed a novel B-cell linear epitope prediction system called LEPS, Linear Epitope Prediction by Propensities and Support Vector Machine, that combined physico-chemical propensity identification and support vector machine (SVM) classification. We tested the LEPS on four datasets: AntiJen, HIV, a newly generated PC, and AHP, a combination of these three datasets. Peptides with globally or locally high physicochemical propensities were first identified as primitive linear epitope (LE) candidates. Then, candidates were classified with the SVM based on the unique features of amino acid segments. This reduced the number of predicted epitopes and enhanced the positive prediction value (PPV). Compared to four other well-known LE prediction systems, the LEPS achieved the highest accuracy (72.52%), specificity (84.22%), PPV (32.07%), and Matthews' correlation coefficient (10.36%).
表位是抗原决定簇，因其能诱导B细胞产生抗体并刺激T细胞活化而具有重要作用。生物信息学能够实现对潜在表位的快速、高效预测。在此，我们设计了一种名为LEPS（基于倾向和支持向量机的线性表位预测）的新型B细胞线性表位预测系统，该系统结合了物理化学倾向识别和支持向量机（SVM）分类。我们在四个数据集上对LEPS进行了测试：AntiJen、HIV、一个新生成的PC数据集以及由这三个数据集组合而成的AHP数据集。首先，将具有全局或局部高物理化学倾向的肽段识别为原始线性表位（LE）候选序列。然后，基于氨基酸片段的独特特征，使用支持向量机对候选序列进行分类。这减少了预测表位的数量，并提高了阳性预测值（PPV）。与其他四个著名的LE预测系统相比，LEPS实现了最高的准确率（72.52%）、特异性（84.22%）、PPV（32.07%）和马修斯相关系数（10.36%）。
