摘要
从20种天然氨基酸的41个分子轮廓指数（randic molecular profiles,R）、44个分子特征值指数（eigenvalue based indices,E）和47个分子运转路径数目（walk and path counts,W）分别进行主成分分析,得出1种新的氨基酸描述符（scores vector of R,E,W-SVREW）。将其应用于血管紧张素转化酶（ACE）抑制二肽、三肽、四肽、九肽结构表征,应用多元线性回归建立定量构效关系模型,同时采用内部与外部双重验证的方法验证模型的稳定性。所建ACE抑制二肽、三肽、四肽、九肽的模型复相关系数（Rcum^2）、留一法（LOO）交互校验复相关系数（Rcv^2）和外部样本校验相关系数（Qext^2）分别为：0.907、0.791、0.633;0.831、0.603、0.723;0.834、0.668、0.718;0.964、0.853、0.948。经研究表明：SVREW描述符应用于ACE抑制肽结构表征所建模型稳定性与预测能力均较好。
A new descriptor of amino acids-SVREW was derived from principal components analysis of the matrix of 41 randic molecular profiles descriptors,44 eigenvalue-based indices descriptors and 47 walk and path counts descriptors of amino acids. The structure of ACE inhibition peptides was characterized with SVREW,using multiple linear regression（ MLR） to establish a quantitative structure-activity relationship（ QSAR）,at the same time,adopt the method of internal and external verify the stability of the model. The relevant statistical parameters as follows： the correlation coefficient（ Rcum^2）,leave-one-out（ LOO） crossvalidation correlation coefficient（ Rcv^2） and external validation correlation coefficient（ Qext^2） were 0. 907,0. 791,0. 633 for di-peptides model; 0. 831,0. 603,0. 723 for tri-peptides model; 0. 834,0. 668,0. 718 for tetrapeptides model; 0. 964,0. 853,0. 948 for nona-peptides model. Studies showed that the MLR models constructed by SVREW descriptor had good fitting and predictive abilities,to become an effective structure characterization methods in peptide drugs QSAR study.
出处
《精细化工》
EI
CAS
CSCD
北大核心
2016年第5期536-540,共5页
Fine Chemicals
基金
国家自然科学基金(21475081)
陕西省自然科学基础研究计划(2015JM2057)
陕西科技大学研究生创新基金~~
关键词
定量构效关系
氨基酸描述子
血管紧张素转化酶抑制肽
多元线性回归
中药现代化技术
quantitative structure-activity relationship
amino acid descriptor
angiotensin converting enzyme inhibition
multiple linear regression
modernization technology of traditional Chinese medicines Foundation items
National Natural Science Funds of China（21475081）
Natural Science Foundation of Shaanxi Province of China（2015JM2057）
Graduate Innovation Fund of Shaanxi University of Science and Technology
