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A Novel Peptide Binding Prediction Approach for HLA-DR Molecule Based on Sequence and Structural Information

机译:基于序列和结构信息的HLA-DR分子的一种新型肽结合预测方法

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摘要

MHO molecule plays a key role in immunology, and the molecule binding reaction with peptide is an important prerequisite for T cell immunity induced. MHCII molecules do not have conserved residues, so they appear as open grooves. As a consequence, this will increase the difficulty in predicting MHC II molecules binding peptides. In this paper, we aim to propose a novel prediction method for MHC II molecules binding peptides. First, we calculate sequence similarity and structural similarity between different MHC II molecules. Then, we reorder pseudosequences according to descending similarity values and use a weight calculation formula to calculate new pocket profiles. Finally, we use three scoring functions to predict binding cores and evaluate the accuracy of prediction to judge performance of each scoring function. In the experiment, we set a parameter α in the weight formula. By changing α value, we can observe different performances of each scoring function. We compare our method with the best function to some popular prediction methods and ultimately find that our method outperforms them in identifying binding cores of HLA-DR molecules.
机译:MHO分子在免疫学中起着关键作用,与肽的分子结合反应是诱导的T细胞免疫的重要前提。 MHCII分子没有保守的残留物,因此它们出现为开放槽。因此,这将增加预测MHC II分子结合肽的困难。在本文中,我们的目标是提出一种新的MHC II分子结合肽的预测方法。首先,我们计算不同MHC II分子之间的序列相似性和结构相似性。然后,我们根据下行相似性值重新排序伪鼠标,并使用权重计算公式来计算新的掌上配置文件。最后,我们使用三个评分功能来预测绑定核心,并评估预测的准确性,以判断每个评分功能的性能。在实验中,我们在重量公式中设置了一个参数α。通过改变α值,我们可以观察每个评分函数的不同性能。我们将我们的方法与一些流行的预测方法的最佳功能进行比较,并最终发现我们的方法在识别HLA-DR分子的结合核心方面优于它们。

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