Huang Jian, Honda Wataru, Kanehisa Minoru
Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho Uji, Kyoto 611-0011, Japan.
Genome Inform. 2007;19:40-9.
We evaluate the performance of six amino acid indices in B cell epitope residue prediction using the classical sliding window method on five data sets. Four of the indices: i.e. relative connectivity, clustering coefficient, closeness and betweenness are newly derived from the topological parameters of residue networks. The other two are Parker's hydrophilicity and Levitt's index, known as the best indices so far for B cell epitope prediction. On four of the data sets, the performance of all the indices was comparable and poor in general. When applied to one well-annotated data set, the performances improved and the 4 network based indices showed better performance than that of Parker's hydrophilicity and Levitt's index. When using the relative connectivity index on this data set, the prediction accuracy, sensitivity and specificity reached 73.6%, 73.0% and 75.0% respectively, with an area under the curve about 0.796. Thus, we suggested that this index is a good choice for B cell epitope prediction. It also indicates that the low performance of B cell epitope prediction is not only due to the methods and amino acid indices used, but also the data set as well. Interestingly, on the well-annotated data set, the performance of B cell epitope residue prediction is very similar to that of protein surface residue prediction, especially at the 10 and 20 A2 cutoffs. It is suggested that the performance in surface residue prediction might form a theoretical upper limit for the performance of B cell epitope residue prediction methods.
我们使用经典滑动窗口方法在五个数据集上评估了六种氨基酸指数在B细胞表位残基预测中的性能。其中四种指数，即相对连通性、聚类系数、紧密性和中介性，是从残基网络的拓扑参数中新推导出来的。另外两种是帕克亲水性指数和莱维特指数，它们是目前已知的用于B细胞表位预测的最佳指数。在四个数据集上，所有指数的性能总体上相当且较差。当应用于一个注释良好的数据集时，性能有所提高，并且基于网络的四种指数表现优于帕克亲水性指数和莱维特指数。在这个数据集上使用相对连通性指数时，预测准确率、灵敏度和特异性分别达到73.6%、73.0%和75.0%，曲线下面积约为0.796。因此，我们认为该指数是B细胞表位预测的一个不错选择。这也表明B细胞表位预测的低性能不仅归因于所使用的方法和氨基酸指数，还与数据集有关。有趣的是，在注释良好的数据集上，B细胞表位残基预测的性能与蛋白质表面残基预测的性能非常相似，尤其是在10埃和20埃的截止值时。有人认为表面残基预测的性能可能构成B细胞表位残基预测方法性能的理论上限。
