College of Chemistry & Chemical Engineering, Shaanxi University of Science & Technology, Xi'an, PR China.
SAR QSAR Environ Res. 2011 Jul-Sep;22(5-6):611-20. doi: 10.1080/1062936X.2011.604099. Epub 2011 Aug 10.
In this work, a descriptor, SVRG (principal component scores vector of radial distribution function descriptors and geometrical descriptors), was derived from principal component analysis (PCA) of a matrix of two structural variables of coded amino acids, including radial distribution function index (RDF) and geometrical index. SVRG scales were then applied in three panels of peptide quantitative structure-activity relationships (QSARs) which were modelled by partial least squares regression (PLS). The obtained models with the correlation coefficient (R²(cum)), cross-validation correlation coefficient (Q²(LOO)) were 0.910 and 0.863 for 48 bitter-tasting dipeptides; 0.968 and 0.931 for 21 oxytocin analogues; and 0.992 and 0.954 for 20 thromboplastin inhibitors. Satisfactory results showed that SVRG contained much chemical information relating to bioactivities. The approach may be a useful structural expression methodology for studies on peptide QSAR.
在这项工作中，我们从编码氨基酸的两个结构变量（径向分布函数指数（RDF）和几何指数）的矩阵的主成分分析（PCA）中推导出了一个描述符 SVRG（主成分得分向量的径向分布函数描述符和几何描述符）。然后，SVRG 标度应用于三个肽定量构效关系（QSAR）面板中，这些面板由偏最小二乘回归（PLS）建模。对于 48 种苦味二肽，获得的模型的相关系数（R²（cum））和交叉验证相关系数（Q²（LOO））分别为 0.910 和 0.863；对于 21 种催产素类似物，为 0.968 和 0.931；对于 20 种凝血酶抑制剂，为 0.992 和 0.954。令人满意的结果表明，SVRG 包含了许多与生物活性相关的化学信息。该方法可能是研究肽 QSAR 的一种有用的结构表达方法。
