摘要
从天然氨基酸74个Geometrical指数经主成分分析得出一种新三维氨基酸描述子——SVG(principalcomponent scores vector of geometrical descriptor),应用该描述子通过偏最小二乘对58个血管紧张素转化酶抑制剂与48个苦味活性二肽建立定量构效关系(QSAR)模型。建模复相关系数R2cum与交互检验复相关系数Qc2um分别为0.823,0.770;0.844,0.704。进一步采用外部样本对模型稳定性能进行了深入分析和检验,所得外部样本校验复相关系数(Qe2xt)分别为0.755和0.703。研究结果表明,SVG描述子操作简便、结构表达能力强。
To establish a new amino acid structure descriptor that can be applied to peptide quantitative structure activity relationship (QSAR) studies, a new descriptor, SVG, was derived from principal components analysis of the matrix of 74 geometrical indexes of amino acids. The scale was then applied in two panels of peptide QSAR that was molded by partial least square regression. The correlation coefficient( R^2cum ) and cross-validation correlation coefficient (Q^2cum) of the obtained models were respectively 0. 823 and 0. 770 for 58 angiotensin-converting enzyme inhibitors'; and 0. 844 and 0. 704 for 48 bitter tasting dipeptides. In addition, the estimation capability and generalization ability of the models were analyzed by external validation. The correlation coefficients of predicted values versus experimental ones of external samples ( Q^2ext ) were 0. 755 and 0. 703. Satisfactory results showed that information related to biological activity can be systemically expressed by SVG scales.
出处
《精细化工》
EI
CAS
CSCD
北大核心
2008年第7期655-659,共5页
Fine Chemicals
基金
陕西科技大学博士科研启动基金(BJ07-03
BJ07-04)
国家863计划基金(2006AA02Z312)~~
关键词
氨基酸
肽
SVG描述子
定量构效关系
amino acid
peptide
SVG descriptor
quantitative structure activity relationship