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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >Quantitative Prediction of Peptide Binding to HLA-DP1 Protein
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Quantitative Prediction of Peptide Binding to HLA-DP1 Protein

机译：肽与HLA-DP1蛋白结合的定量预测

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The exogenous proteins are processed by the host antigen-processing cells. Peptidic fragments of them are presented on the cell surface bound to the major hystocompatibility complex (MHC) molecules class II and recognized by the CD4+ T lymphocytes. The MHC binding is considered as the crucial prerequisite for T-cell recognition. Only peptides able to form stable complexes with the MHC proteins are recognized by the T-cells. These peptides are known as T-cell epitopes. All T-cell epitopes are MHC binders, but not all MHC binders are T-cell epitopes. The T-cell epitope prediction is one of the main priorities of immunoinformatics. In the present study, three chemometric techniques are combined to derive a model for in silico prediction of peptide binding to the human MHC class II protein HLA-DP1. The structures of a set of known peptide binders are described by amino acid z-descriptors. Data are processed by an iterative self-consisted algorithm using the method of partial least squares, and a quantitative matrix (QM) for peptide binding prediction to HLA-DP1 is derived. The QM is validated by two sets of proteins and showed an average accuracy of 86 percent.
机译：外源蛋白质由宿主抗原加工细胞加工。它们的肽片段呈现在细胞表面，与主要的II类主要相容性复合物（MHC）分子结合，并被CD4 + T淋巴细胞识别。 MHC结合被认为是T细胞识别的关键前提。 T细胞只能识别能够与MHC蛋白形成稳定复合物的肽。这些肽被称为T细胞表位。所有的T细胞表位都是MHC结合物，但不是所有的MHC结合物都是T细胞表位。 T细胞表位的预测是免疫信息学的主要优先事项之一。在本研究中，将三种化学计量学技术结合在一起，得出了一种计算机模拟预测与人MHC II类蛋白HLA-DP1结合的肽的模型。一组已知的肽结合剂的结构由氨基酸z-描述符描述。使用偏最小二乘方法通过迭代自洽算法处理数据，并得出用于预测肽与HLA-DP1结合的定量矩阵（QM）。 QM已通过两组蛋白质验证，显示出86％的平均准确度。

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