Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE, USA.
Methods Mol Biol. 2020;2131:299-307. doi: 10.1007/978-1-0716-0389-5_17.
Identifying protein antigenic epitopes recognizable by antibodies is the key step for new immuno-diagnostic reagent discovery and vaccine design. To facilitate this process and improve its efficiency, computational methods were developed to predict antigenic epitopes. For the linear B-cell epitope prediction, many methods were developed, including BepiPred, ABCPred, AAP, BCPred, BayesB, BEOracle/BROracle, BEST, and SVMTriP. Among these methods, SVMTriP, a frontrunner, utilized Support Vector Machine by combining the tri-peptide similarity and Propensity scores. Applied on non-redundant B-cell linear epitopes extracted from IEDB, SVMTriP achieved a sensitivity of 80.1% and a precision of 55.2% with a five-fold cross-validation. The AUC value was 0.702. The combination of similarity and propensity of tri-peptide subsequences can improve the prediction performance for linear B-cell epitopes. A webserver based on this method was constructed for public use. The server and all datasets used in the corresponding study are available at http://sysbio.unl.edu/SVMTriP . This chapter describes the webserver of SVMTriP.
鉴定可被抗体识别的蛋白质抗原表位是发现新的免疫诊断试剂和疫苗设计的关键步骤。为了促进这一过程并提高其效率，开发了计算方法来预测抗原表位。对于线性 B 细胞表位预测，已经开发了许多方法，包括 BepiPred、ABCPred、AAP、BCPred、BayesB、BEOracle/BROracle、BEST 和 SVMTriP。在这些方法中，SVMTriP 是一个领先的方法，它通过结合三肽相似性和倾向分数来利用支持向量机。在从 IEDB 提取的非冗余 B 细胞线性表位上应用，SVMTriP 在五重交叉验证中达到了 80.1%的灵敏度和 55.2%的精度。AUC 值为 0.702。三肽子序列的相似性和倾向的组合可以提高线性 B 细胞表位的预测性能。基于该方法构建了一个公共使用的网络服务器。相应研究中使用的服务器和所有数据集均可在 http://sysbio.unl.edu/SVMTriP 获得。本章描述了 SVMTriP 的网络服务器。
