Background: Immune reaction is the most important defense mechanism for destroying invading pathogens in our body, and the epitope is the position of the antigen–antibody interaction on pathogenic proteins.

Objective: The majority of epitopes are structural; however, the existing sequence-based predicting websites still have several methods to improve the predicting performance. Therefore, in this study, we used SVM as a machine learning tool to predict the epitope-based on protein sequences.

Methods: Firstly, we built five SVM models in the first layer according to five features, including binary composition, position-specific scoring matrix, secondary structure, accessible surface area, and association rule, and then chose the patterns that exhibited the best performance in each model. Secondly, using the confidence score of the first-layer models as the input value for the SVM model in the second layer, that SVM model was integrated into the first-layer SVM models for improving the predicting accuracy.

Results: The final prediction model was able to achieve up to 63% accuracy in predicting epitope results, and the predicting performance was better than that achieved by the existing predicting websites.

Conclusion: Finally, a case study using a two-subunit cytochrome c oxidase of Paracoccus denitrificans was tested, achieving an accuracy of up to 66%.

背景：免疫反应是消灭我们体内入侵病原体的最重要防御机制，表位是病原蛋白上抗原-抗体相互作用的位置。

目的：大多数表位是结构性的。但是，现有的基于序列的预测网站仍然有几种方法可以提高预测性能。因此，在这项研究中，我们使用SVM作为机器学习工具来预测基于蛋白质序列的表位。

方法：首先，我们根据二进制组成，位置特定的评分矩阵，二级结构，可访问的表面积和关联规则等五个特征在第一层中构建了五个SVM模型，然后选择表现出最佳性能的模式。每个模型。其次，使用第一层模型的置信度得分作为第二层SVM模型的输入值，将该SVM模型集成到第一层SVM模型中以提高预测精度。

结果：最终的预测模型在预测表位结果方面能够达到63％的准确性，并且预测性能要优于现有的预测网站。

结论：最后，对使用反硝化副球菌的两个亚基细胞色素C氧化酶的案例研究进行了测试，其准确性高达66％。