物理化学学报 >> 2004, Vol. 20 >> Issue (08): 821-825.doi: 10.3866/PKU.WHXB20040808

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一种新的氨基酸描述子及其在肽QSAR中的应用

梅虎;周原;孙立力;李志良   

  1. 重庆大学化学化工学院;生物医学工程教育部与重庆市重点实验室;重庆大学生物工程学院,重庆　400044
  • 收稿日期:2003-12-25 修回日期:2004-03-29 发布日期:2004-08-15
  • 通讯作者: 李志良 E-mail:zlli2662@163.com

A New Descriptor of Amino Acids and Its Application in Peptide QSAR

Mei Hu;Zhou Yuan;Sun Li-Li;Li Zhi-Liang   

  1. College of Chemistry and Chemical Engineering, Chongqing University;Key Laboratory of Biomedical Engineering of Educational Ministry and Chongqing City;College of Bioengineering, Chongqing University, Chongqing　400044
  • Received:2003-12-25 Revised:2004-03-29 Published:2004-08-15
  • Contact: Li Zhi-Liang E-mail:zlli2662@163.com

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摘要： 从天然氨基酸的25个结构与拓扑变量中经主成分分析得到一种新的氨基酸描述子——VSTV （principal component scores vector of structural and topological variables）.应用该描述子对以下3个体系,即血管紧张素转化酶抑制剂（2肽）、抗菌18肽和促凝血酶原激酶抑制剂（6～12肽）进行分子结构参数化表达,并在此基础上通过偏最小二乘回归(PLSR)建立定量构效关系(QSAR)模型,取得了优于文献的结果.模型的复相关系数（R2）和交互检验复相关系数（Q2）分别为0.789, 0.767; 0.996, 0.879; 0.981, 0.480.

关键词: 氨基酸, 多肽, 拓扑, VSTV, 定量构效关系, 偏最小二乘回归

Abstract: Quantitative structure-activity relationships (QSARs) are essential to optimize the structure to give desired biological activities in drug development. In this paper, a new descriptor, principal component scores vector of structural and topological variables (VSTV), was derived from a principal components analysis of a matrix of 25 structural and topological variables of 20 natural amino acids. Using the method of partial least squares regression, the scales were then applied in QSARs of 58 angiotensin-converting enzyme inhibitors, 12 bactericidal peptides and 20 thromboplastin inhibitors. Good results were obtained and the multiple correlation coefficients (R2) and cross-validated R2 (Q2) of three models were 0.789, 0.767; 0.996, 0.879; 0.981, 0.480, respectively.

Key words: Amino acids, Peptide, Topology, Principal component scores vector of structural and topological variables (VSTV), Quantitative structure activity relationship, Partial least squares regression