A hybrid approach for predicting promiscuous MHC class I restricted T cell epitopes
A hybrid approach for predicting promiscuous MHC class I restricted T cell epitopes
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DOI：
10.1007/s12038-007-0004-5
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发表时间：
2007-01-01
影响因子：
2.9
通讯作者：
Raghava, G. P. S.
中科院分区：
文献类型：
--
作者：
Bhasin, Manoi;Raghava, G. P. S.
在本研究中，系统地尝试开发一种准确的方法来预测大量 MHC I 类等位基因的 MHC I 类限制性 T 细胞表位。最初，针对具有至少 15 个结合物的 47 个 MHC I 类等位基因开发了基于定量矩阵 (QM) 的方法。针对具有至少 40 个结合物的 47 个 MHC 等位基因中的 30 个，开发了基于辅助人工神经网络 (ANN) 的方法。与每种单独的方法相比，这些基于 ANN 和 QM 的预测方法的组合对 30 个等位基因的预测准确性提高了 6%。混合方法对 30 个 MHC 等位基因的平均准确度为 92.8%。该方法还允许使用文献中报道的 QM 预测 20 个其他等位基因的结合物，从而允许预测 67 个 MHC I 类等位基因。使用折刀验证测试评估该方法的性能。还根据盲数据或独立数据评估了这些方法的性能。我们的方法与现有的 MHC 结合物预测方法对两种方法研究的等位基因的比较表明，我们的方法优于其他现有方法。该方法还通过实施前面描述的矩阵来识别抗原序列中的蛋白酶体切割位点。因此，我们发现的方法可以鉴定在 C-末端具有蛋白酶体切割位点的 MHC I 类结合物（与许多 MHC 等位基因结合的肽）。用户友好的结果显示格式 (HTML-II) 可以帮助从抗原序列中定位混杂的 MHC 结合区域。该方法可在 www.imtech.res.inlraghavalnhlapred 网站上获取，其镜像站点可在 http://bioinformatics.uams.edu/mirror/nhlapred/ 上获取。
In the present study, a systematic attempt has been made to develop an accurate method for predicting MHC class I restricted T cell epitopes for a large number of MHC class I alleles. Initially, a quantitative matrix (QM)-based method was developed for 47 MHC class I alleles having at least 15 binders. A secondary artificial neural network (ANN)-based method was developed for 30 out of 47 MHC alleles having a minimum of 40 binders. Combination of these ANN- and QM-based prediction methods for 30 alleles improved the accuracy of prediction by 6% compared to each individual method. Average accuracy of hybrid method for 30 MHC alleles is 92.8%. This method also allows prediction of binders for 20 additional alleles using QM that has been reported in the literature, thus allowing prediction for 67 MHC class I alleles. The performance of the method was evaluated using jack-knife validation test. The performance of the methods was also evaluated on blind or independent data. Comparison of our method with existing MHC binder prediction methods for alleles studied by both methods shows that our method is superior to other existing methods. This method also identifies proteasomal cleavage sites in antigen sequences by implementing the matrices described earlier. Thus, the method that we discover allows the identification of MHC class I binders (peptides binding with many MHC alleles) having proteasomal cleavage site at C-tenninus. The user-friendly result display format (HTML-II) can assist in locating the promiscuous MHC binding regions from antigen sequence. The method is available on the web at www.imtech.res.inlraghavalnhlapred and its mirror site is available at http://bioinformatics.uams.edu/mirror/nhlapred/.