浙江科技学院学报
浙江科技學院學報
절강과기학원학보
JOURNAL OF ZHEJIANG UNIVERSITY OF SCIENCE AND TECHNOLOGY
2015年
3期
174-182
,共9页
血管紧张素转化酶%二肽%支持向量机%偏最小二乘%定量构效关系
血管緊張素轉化酶%二肽%支持嚮量機%偏最小二乘%定量構效關繫
혈관긴장소전화매%이태%지지향량궤%편최소이승%정량구효관계
angiotensin I-converting enzyme(ACE)%dipeptide%support vector machine(SVM)%partial least square regression(PLS)%quantitative structure-activity relationship(QSAR)
以自组建的食源性血管紧张素转化酶（angiotensin I‐converting enzyme ，ACE）抑制二肽为研究样本，采用氨基酸描述子VHSE（principal component score vector of hydrophilicity ，steric ，and electronic properties）对ACE抑制二肽进行表征后，比较偏最小二乘（partial least square regression ，PLS）、支持向量机（support vector machine ，SVM）及主成分分析（principal component analysis ，PCA）‐SVM相结合的3种建模方法对ACE抑制二肽的QSAR（quantitative structure‐activity relationship）建模。结果显示，对于食源性ACE抑制二肽，3个模型的拟合能力无明显差异，SVM模型的预测能力略强；对其进行权重投影分析发现，C末端氨基酸较N末端氨基酸对其活性的影响更为明显。
以自組建的食源性血管緊張素轉化酶（angiotensin I‐converting enzyme ，ACE）抑製二肽為研究樣本，採用氨基痠描述子VHSE（principal component score vector of hydrophilicity ，steric ，and electronic properties）對ACE抑製二肽進行錶徵後，比較偏最小二乘（partial least square regression ，PLS）、支持嚮量機（support vector machine ，SVM）及主成分分析（principal component analysis ，PCA）‐SVM相結閤的3種建模方法對ACE抑製二肽的QSAR（quantitative structure‐activity relationship）建模。結果顯示，對于食源性ACE抑製二肽，3箇模型的擬閤能力無明顯差異，SVM模型的預測能力略彊；對其進行權重投影分析髮現，C末耑氨基痠較N末耑氨基痠對其活性的影響更為明顯。
이자조건적식원성혈관긴장소전화매（angiotensin I‐converting enzyme ，ACE）억제이태위연구양본，채용안기산묘술자VHSE（principal component score vector of hydrophilicity ，steric ，and electronic properties）대ACE억제이태진행표정후，비교편최소이승（partial least square regression ，PLS）、지지향량궤（support vector machine ，SVM）급주성분분석（principal component analysis ，PCA）‐SVM상결합적3충건모방법대ACE억제이태적QSAR（quantitative structure‐activity relationship）건모。결과현시，대우식원성ACE억제이태，3개모형적의합능력무명현차이，SVM모형적예측능력략강；대기진행권중투영분석발현，C말단안기산교N말단안기산대기활성적영향경위명현。
A new ACE inhibitory dipeptides database was self‐established .After the structures of dipeptides were characterized by using amino acid descriptors VHSE (principal component score vector of hydrophilicity , steric , and electronic properties ) , three kinds of modeling methods ,namely partial least square regression(PLS),support vector machine(SVM),and principal component analysis(PCA ) combined with SVM were used to establish the models of the QSAR of ACE inhibitory dipeptides ,respectively . The results showed that there is no significant difference between the fitting abilities of the three models ;the predictive abilities of SVM model are stronger than other models .Moreover ,the key structure factors relevant with dipeptide activities were studied . The results showed that the effect of the amino acid at C‐terminal on ACE inhibitory activity of the dipeptides is more obvious than that at N‐terminal .
