Prediction of linear B-cell epitopes using AAT scale

摘要：

The prediction of B-cell epitopes is of great importance for computer acid vaccine design and immunodiagnostic test.Although it is said that a large majority of B-cell epitopes are conformational, experimental epitope identification has focused primarily on linear B-cell epitopes.A number of computational methods have been developed for the prediction of linear B-cell epitopes, but few of them can give us a convincible result.In this paper, a new method, call AAT-fs is proposed which focus on the amino acid triplet (AAT) antignenicity scale.After using AAT scale to create input vectors, we develop a Support Vector Machine (SVM) for the classification which is trained utilizing RBF kernel on homology reduced datasets with fivefold crossvalidation.The AAT-fs method gets the better performance than AAP scale, BCPred and other existing B-cell epitope prediction algorithms.It can be expect that with the rapid development of B-cell epitope identification experimental technology, the dataset will increase and AAT-fs can achieve better result.

关键词： B-cell epitope epitope predicion amino acid triplet SVM

作者: Lian Wang Juan Liu Shanfeng Zhu YangYang Gao

作者单位: School of Computer Science Wuhan university,Wuhan,China 430079 The School of Computer Science and Technology,Fudan University,Shanghai,China 200433

会议类型: 国际会议

会议名称: The 3rd International Conference on Bioinformatics and Biomedical Engineering(iCBBE 2009)(第三届生物信息与生物医学工程国际会议)

会议地点: 北京

会议语种:英文

页码: 1-4

在线出版日期: 2009-06-11（万方平台首次上网日期，不代表论文的发表时间）