Toward prediction of binding affinities between the MHC protein and its peptide ligands using quantitative structure-affinity relationship approach.
Toward prediction of binding affinities between the MHC protein and its peptide ligands using quantitative structure-affinity relationship approach.
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DOI：
10.2174/092986608786071120
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发表时间：
2008-09
影响因子：
1.6
通讯作者：
F. Tian;F. Lv;P. Zhou;Q. Yang;A. Jalbout
中科院分区：
文献类型：
--
作者：
F. Tian;F. Lv;P. Zhou;Q. Yang;A. Jalbout
准确测定MHC-肽相互作用的结合特异性并定量预测其结合亲和力是重要而具有挑战性的。在本文中，我们讨论了一个有效的氨基酸描述符的应用模型和预测的MHC蛋白质和它的肽配体之间的结合亲和力。该氨基酸描述符是使用主成分分析（PCA）从编码氨基酸的23个电子性质、37个空间性质、54个疏水性质和5个氢键性质中获得的，称为划分的物理化学性质得分（DPPS）。利用DPPS描述子对一组小鼠MHC（H-2K（K））结合肽进行表征，构建遗传算法-偏最小二乘（GA-PLS）模型。在分析中，这些模型与以前的报告和分子图形显示在统计上是一致的。疏水相互作用和氢键对抗原的识别和呈递有重要作用，尤其是对多肽的锚残基有重要影响。
It is important but challenging to determine the binding specificity of MHC-peptide interactions accurately and to predict their binding affinity quantitatively. In this paper, we discuss the application of an effective amino acid descriptor to model and predict the binding affinities between the MHC protein and its peptide ligands. This amino acid descriptor was derived from 23 electronic properties, 37 steric properties, 54 hydrophobic properties and 5 hydrogen bond properties of coded amino acids using principal component analysis (PCA), called the divided physicochemical property scores (DPPS). The DPPS descriptor was used to characterize a set of mouse MHC (H-2K(K)) binding peptides, and genetic algorithm-partial least square (GA-PLS) models were then constructed. In analyses, these models were statistically consistent with previous reports and molecular graphics exhibition. Hydrophobic interactions and hydrogen bonds were important to antigen recognition and presentation, especially exerting effects on anchor residues of peptides.