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首页> 外文会议>2012 Second International Conference on Advances in Computing and Communications >Prediction Model of MHC Class-II Binding Peptide Motifs Using Sequence Weighting Method for Vaccine Design
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Prediction Model of MHC Class-II Binding Peptide Motifs Using Sequence Weighting Method for Vaccine Design

机译：序列加权法预测MHC II类结合肽基序的疫苗设计模型

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摘要

Identification of MHC class-II restricted epitope is an important goal in peptide based vaccine and diagnostic development. Currently, immuno informatics can circumvent conventional time-consuming and laborious experimental techniques of overlapping peptides from protein to epitope identification. However, prediction of MHC class-II epitope is difficult due to variable length of binding peptides (13-25 amino acids). In the present study, we applied the Gibbs motif sampler, Sturniolo pocket profile and NNAlign method for binding motif identification and further position specific scoring matrices (PSSM) using sequence weighting schemes for the prediction of peptide binding to seven human MHC class-II molecules. Here, we used a non-parametric performance measure, area under receiver operating characteristic curve (Aroc) which provides a global assessment of predictive power. The average prediction performances for motif identification based on NNAlign, Sturniolo pocket profile and Gibbs sampler in term of Aroc are 0.71, 0.68 and 0.64, respectively. Further improvements in the performance of MHC class-II binding peptide predictor largely depends on the size of training dataset, optimization of training parameters and the correct identification of the peptide binding motifs.
机译：MHC II类限制性表位的鉴定是基于肽的疫苗和诊断开发的重要目标。当前，免疫信息学可以绕开从蛋白质到表位鉴定的重叠肽的常规费时费力的实验技术。然而，由于结合肽的长度可变（13-25个氨基酸），难以预测II类MHC表位。在本研究中，我们应用Gibbs基序采样器，Sturniolo口袋轮廓和NNAlign方法来结合基序识别，并使用序列加权方案进一步定位特异性评分矩阵（PSSM），以预测与7种人类MHC II类分子结合的肽。在这里，我们使用了一种非参数性能指标，即接收机工作特性曲线（Aroc）下的面积，该指标可提供对预测能力的整体评估。基于NNAlign，Sturniolo口袋轮廓和Gibbs采样器的主题识别的平均预测性能（以Aroc表示）分别为0.71、0.68和0.64。 MHC II类结合肽预测因子性能的进一步提高在很大程度上取决于训练数据集的大小，训练参数的优化以及对肽结合基序的正确识别。

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