高等学校化学学报

高等学校化学学报 ›› 2012, Vol. 33 ›› Issue (11): 2556.doi: 10.7503/cjcu20120056

• 物理化学 • 上一篇    下一篇

基于VHSE结构表征的TAP亲和活性预测及选择特异性分析

潘显超1, 梅虎1,2, 谢江安2, 吕娟2, 王青2, 张亚兰2, 谭文2   

  1. 1. 重庆大学生物流变科学与技术教育部重点实验室, 重庆 400044;
    2. 重庆大学生物工程学院, 重庆 400044
  • 收稿日期:2012-01-12 出版日期:2012-11-10 发布日期:2012-10-15
  • 通讯作者: 梅虎,男,博士,副教授,主要从事生物信息学研究.E-mail:meihu@cqu.edu.cn E-mail:meihu@cqu.edu.cn
  • 基金资助:

    国家自然科学基金(批准号: 61073135); 重庆市自然科学基金重点项目(批准号: 2009BA5068)和中央高校基本科研业务费专项基金(批准号: CDJXS11230013)资助.

Prediction of TAP Binding Affinity of Peptide and Selection Specificity Using VHSE Descriptors

PAN Xian-Chao1, MEI Hu1,2, XIE Jiang-An2, LÜ Juan2, WANG Qing2, ZHANG Ya-Lan2, TAN Wen2   

  1. 1. Key Laboratory of Biorheological Science and Technology, Ministry of Education, Chongqing University, Chongqing 400044, China;
    2. College of Bioengineering, Chongqing University, Chongqing 400044, China
  • Received:2012-01-12 Online:2012-11-10 Published:2012-10-15

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被引次数

2

摘要：

应用氨基酸描述子VHSE(Principal component score vector of hydrophobic, steric, and electronic properties)对613个抗原9肽进行结构表征, 在此基础上, 采用支持向量机结合逐步回归变量筛选方法, 成功建立了抗原肽抗原处理相关转运蛋白(Transporter associated with antigen processing, TAP)亲和活性预测模型, 最优线性支持向量机模型的R2, Q2和R2ext分别为0.7386, 0.7270和0.6057. 模型结果分析表明, 影响TAP亲和活性的首要因素是电性, 其次是立体和疏水性质; 底物9肽的P1(N端)及P2, P7和P9(C端)位氨基酸物化性质对TAP亲和活性有重要影响, 而P3, P4, P5和P6位对模型贡献相对较小, P8位则与活性无关. 依据最优模型对模拟点突变9肽的TAP亲和活性的预测结果, 并结合变量载荷分析, 对TAP底物选择特异性进行了分析和总结.

关键词: 抗原处理相关转运蛋白, 抗原肽, 氨基酸描述子VHSE, 支持向量机, 活性, 选择特异性

Abstract:

The transporter associated with antigen processing (TAP) plays a crucial role in the antigen-processing pathway mediated by human leukocyte antigen(HLA) class Ⅰ molecules. TAP binding affinity of peptide can affect T-cell epitope selection significantly, so prediction of TAP binding affinity as well as selection specificity research has become a hot spot in computational immunology recently. Compared to the existing methods with little physicochemical meanings, in this paper, VHSE(principal component score vector of hydrophobic, steric, and electronic properties), a novel set of descriptors based on the physical and chemical characteristics of 20 nature amino acids, was used to characterize 613 nonamer peptides of known affinity to TAP. Then, support vector machine(SVM) combined with multiple stepwise regression(MSR) was adopted to establish prediction models of TAP binding affinity of peptide. An optimal SVM model with linear kernel was obtained, of which R2, Q2 and Rext2 were 0.7386, 0.7270 and 0.6057, respectively. The results show that electronic, steric, and hydrophobic properties of the amino acid sites are closely related to TAP binding affinity and substrate specificity, especially for electrical property. The amino acids at P1, P2, P7 and P9 sites of antigenic peptides have the most impact on TAP binding affinity, while those at P3, P4, P5 and P6 sites have less impact and amino acid at P8 site has no impact. According to the prediction results of single point mutated antigenic peptides by the optimal linear SVM model together with the contribution weights of selected variables, substrate specificity of TAP was summarized.

Key words: Transporter associated with antigen processing, Antigenic peptide, VHSE(principal component score vector of hydrophobic, steric, and electronic properties), Support vector machine, Activity, Selection specificity

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