Predicting peptides bound to I-Ag7 class II histocompatibility molecules using a novel expectation-maximization alignment algorithm
Predicting peptides bound to I-Ag7 class II histocompatibility molecules using a novel expectation-maximization alignment algorithm
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DOI：
10.1002/pmic.200600584
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发表时间：
2007-02-01
期刊：
影响因子：
3.4
通讯作者：
Unanue, Emil R.
中科院分区：
文献类型：
--
作者：
Chang, Kuan Y.;Suri, Anish;Unanue, Emil R.
第二类MHC分子的有用结构特征很少被整合到T细胞表位预测中。我们提出了一种方法，应用一种新的期望最大化算法来比对由II类MHC I-A(G7)分子选择的自然处理的多肽-专注于五个MHC特定的锚点位置。根据比对结果，应用对数优势对数(LOD)计分和拉普拉斯加一伪计数法识别潜在的T细胞表位。此外，还提出了一种利用统计和结构信息阻碍残基的创新计算概念，以完善预测。通过接收器工作特性统计的性能分析和LOD分数的实验验证，证明了该预测模型的准确性。此外，我们的模型成功地预测了蛋清溶菌酶蛋白抗原的T细胞表位。我们的研究为预测第二类MHC分子中的T细胞表位提供了一个框架。
The useful structural features of class II MHC molecules are rarely integrated into T-cell epitope predictions. We propose an approach that applies a novel expectation-maximization algorithm to align the naturally processed peptides selected by the class II MHC I-A(g7) molecule - focusing on the five MHC-specific anchor positions. Based on the alignment profile, log of odds (LOD) scores supplemented with the Laplace plus-one pseudocounts method are applied to identify the potential T-cell epitopes. In addition, an innovative computational concept of hindering residues using statistical and structural information is developed to refine the prediction. Performance analysis by receiver operating characteristics statistics and the experimental validation of the LOD scores demonstrate the accuracy of our predictive model. Furthermore, our model successfully predicts T-cell epitopes of hen egg-white lysozyme protein antigen. Our study provides a framework for predicting T-cell epitopes in class II MHC molecules.