盛浩,卢 玉 峰,张屹.Computational prediction of MHC Ⅱ-peptide ligands binding specificities by AUC Optimized Gibbs[J].,2014,54(1):28-36 |
Computational prediction of MHC Ⅱ-peptide ligands binding specificities by AUC Optimized Gibbs |
基于AUC Optimized Gibbs方法的 MHC Ⅱ-短肽配体结合特异性预测 |
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DOI:10.7511/dllgxb201401005 |
中文关键词: Gibbs sampling method epitope MHC Ⅱ molecules reduced homology |
英文关键词: Gibbs采样方法 表位 MHC Ⅱ分子 约化同源性 |
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中文摘要: |
In the design of peptide-based or other defined antigen-based vaccines, it is important to know which fragments of pathogen-derived proteins would bind to the MHC Ⅱ molecules. Most studies of the MHC Ⅱ epitope prediction rarely gave the quantitative analyses of binding specificities. So the accuracy of these models still needs to be improved. AUC Optimized Gibbs (AOG) method uses the homology reduced AUC, rather than the relative entropy to guide the sampler. It makes both the positive and negative information of the samples be incorporated into the model. AOG achieves average AUC values of 0.771 and 0.713 on the ten original and homology reduced HLA-DR4 (B1*0401) epitope benchmarks, which are better than 0.744 and 0.673 by the Gibbs sampling method. In the quantitative IEDB MHC-Ⅱ benchmarks, AOG achieves an average AUC value of 0.766, compared to 0.718 by the TEPITOPE. A detailed inspection of information extracted from HLA-DR4 (B1*0401) data allows the identification of positions with obvious specificities, i. e., P1, P4, P6 and P9 positions, which have distinct influence on the MHC-peptide binding. |
英文摘要: |
在以短肽定义的或以抗原定义的疫苗设计中,识别哪个来自病原体的蛋白质片段会结合MHC Ⅱ分子是个重要问题 多数MHC Ⅱ表位预测的研究很少给出结合特异性的定量分析,所以这些模型的精确度仍然需要进一步提高 AUC Optimized Gibbs(AOG)使用约化同源性的AUC值而不是相对熵来引导采样,使得正样本和负样本的信息都被用于模型的训练 在10个HLA-DR4(B1*0401)原测试集和约化同源性测试集的测试中,AOG得到的平均AUC值分别是0.771和0.713,优于Gibbs的0.744和0.673 在定量IEDB的MHC Ⅱ测试集中,AOG得到的平均AUC值是0.766,而TEPITOPE得到的平均AUC值是0.718 从HLA-DR4(B1*0401)数据提取的信息可以识别某些有明显特异性的位置,即P1、P4、P6和P9位置,其对MHC-短肽结合有明显的影响 |
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