三维氨基酸结构描述子矢量SVRDF及其在肽QSAR中的应用

A new SVRDF 3D-descriptor of amino acids and its application to peptide quantitative structure activity relationship

为了建立一种新型氨基酸结构描述子并用于多肽定量构效关系研究,本研究从天然氨基酸150个radialdistributionfunction(RDF)指数经主成分分析得出一种新3D氨基酸描述子-SVRDF(principalcomponentscoresvectorofradialdistributionfunction),应用该描述子并通过偏最小二乘(PLS)对苦味二肽、催产素及促凝血酶原激酶抑制剂3个体系建立定量构效关系模型。对3个体系建模复相关系数(R2cum)与交互检验复相关系数(Qc2um)分别为0.766和0.724;0.941和0.811;0.996和0.919。研究结果表明,SVRDF描述子能够系统地表征肽与生物活性相关的结构信息,有望成为多肽定量构效关系研究中一种有效的结构表征方法。

To establish a new amino acid structure descriptor that can be applied to polypeptide quantitative structure activity relationship （QSAR） studies, a new descriptor, SVRDF, was derived from a principal components analysis of a matrix of 150 radial distribution function index of amino acids. The scale was then applied in three panels of peptide QSAR that were molded by partial least squares regression. The obtained models with the correlation coefficients （R^2 cum ）, cross-validation correlation coefficients （Q^2 cum） were 0. 766 and 0. 724 for 48 bitter tasting dipeptides; 0. 941 and 0. 811 for 21 oxytocin analogues; 0. 996 and 0. 919 for 20 thromboplastin inhibitors. Satisfactory results showed that information related to biological activity can be systemically expressed by SVRDF scales, which may be an useful structural expression methodology for the study of peptides QSAR.