Wang Lian, Pan Danling, Hu Xihao, Xiao Jinyu, Gao Yangyang, Zhang Huifang, Zhang Yan, Liu Juan, Zhu Shanfeng
School of Computer, Wuhan University, Wuhan 430079, China.
J Genet Genomics. 2009 May;36(5):289-96. doi: 10.1016/S1673-8527(08)60117-4.
Effective identification of major histocompatibility complex (MHC) molecules restricted peptides is a critical step in discovering immune epitopes. Although many online servers have been built to predict class II MHC-peptide binding affinity, they have been trained on different datasets, and thus fail in providing a unified comparison of various methods. In this paper, we present our implementation of seven popular predictive methods, namely SMM-align, ARB, SVR-pairwise, Gibbs sampler, ProPred, LP-top2, and MHCPred, on a single web server named BiodMHC (http://biod.whu.edu.cn/BiodMHC/index.html, the software is available upon request). Using a standard measure of AUC (Area Under the receiver operating characteristic Curves), we compare these methods by means of not only cross validation but also prediction on independent test datasets. We find that SMM-align, ProPred, SVR-pairwise, ARB, and Gibbs sampler are the five best-performing methods. For the binding affinity prediction of class II MHC-peptide, BiodMHC provides a convenient online platform for researchers to obtain binding information simultaneously using various methods.
有效识别主要组织相容性复合体（MHC）分子限制性肽段是发现免疫表位的关键步骤。尽管已经构建了许多在线服务器来预测II类MHC-肽结合亲和力，但它们是在不同的数据集上进行训练的，因此无法对各种方法进行统一比较。在本文中，我们在一个名为BiodMHC（http://biod.whu.edu.cn/BiodMHC/index.html，软件可根据要求提供）的单一网络服务器上实现了七种流行的预测方法，即SMM-align、ARB、SVR-pairwise、吉布斯采样器、ProPred、LP-top2和MHCPred。使用AUC（受试者操作特征曲线下面积）这一标准指标，我们不仅通过交叉验证，还通过对独立测试数据集的预测来比较这些方法。我们发现SMM-align、ProPred、SVR-pairwise、ARB和吉布斯采样器是性能最佳的五种方法。对于II类MHC-肽的结合亲和力预测，BiodMHC为研究人员提供了一个方便的在线平台，使他们能够同时使用各种方法获取结合信息。
