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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分子肽结合预测新方法

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摘要

MHC molecule plays a key role in immunology, and the molecule binding reaction with peptide is an important prerequisite for T cell immunity induced. MHC II 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:MHC分子在免疫学中起关键作用,与肽的分子结合反应是诱导T细胞免疫的重要前提。 MHC II分子没有保守的残基,因此它们表现为开槽。结果,这将增加预测MHC II分子结合肽的难度。在本文中,我们旨在提出一种新的预测MHC II分子结合肽的方法。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,我们根据递减的相似性值对伪序列进行重新排序,并使用权重计算公式来计算新的口袋轮廓。最后,我们使用三个评分函数来预测绑定核心,并评估预测的准确性以判断每个评分函数的性能。在实验中,我们在权重公式中设置参数α。通过更改α值,我们可以观察到每个评分函数的不同性能。我们将功能最佳的方法与一些流行的预测方法进行了比较,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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