A new descriptor,namely scores vector of zero dimension,one dimension,two dimension and three dimension(SZOTT),was derived from principle components analysis of a matrix of 1 369 structural variables including 0D,1D,2...A new descriptor,namely scores vector of zero dimension,one dimension,two dimension and three dimension(SZOTT),was derived from principle components analysis of a matrix of 1 369 structural variables including 0D,1D,2D and 3D information for 20 coded amino acids.SZOTT scales were then employed to express structures of 20 thromboplastin inhibitors and 34 bactericidal peptides.The correlation coefficients of both whole calibration(%R%2=%R%2cu)and of cross validation(%Q%2=%R%2cv)for the multiple-variable models by classical partial least squares(PLS)and orthogonal signal correction-partial least squares(OSC-PLS)of 20 thromboplastin inhibitors were 0.989 and 0.748,0.994 and 0.936,respectively.%R%2 and %Q%2 for the models by PLS and OSC-PLS of 34 bactericidal peptides were 0.619 and 0.406,0.910 and 0.503,respectively.Satisfactory results obtained showed that structural information related to biological activity in both data sets could be described by SZOTT which included plentiful information related to biological activity,and which was conveniently operated and easy interpreted.,also predictive capability of models were relative robust.There is a high prospect for SZOTT wide applications on quantitative sequence-activity modeling(QSAM)of peptides.展开更多
A new descriptor, called vector of topological and structural information for coded and noncoded amino acids (VTSA), was derived by principal component analysis (PCA) from a matrix of 66 topological and structural var...A new descriptor, called vector of topological and structural information for coded and noncoded amino acids (VTSA), was derived by principal component analysis (PCA) from a matrix of 66 topological and structural variables of 134 amino acids. The VTSA vector was then applied into two sets of peptide quantitative structure-activity relationships or quantitative sequence-activity modelings (QSARs/QSAMs). Molded by genetic partial least squares (GPLS), support vector machine (SVM), and immune neural network (INN), good results were obtained. For the datasets of 58 angiotensin converting enzyme inhibitors (ACEI) and 89 elastase substrate catalyzed kinetics (ESCK), the R 2, cross-validation R 2, and root mean square error of estimation (RMSEE) were as follows: ACEI, R cu 2 ?0.82, Q cu 2 ?0.77, E rmse?0.44 (GPLS+SVM); ESCK, R cu 2 ?0.84, Q cu 2 ?0.82, E rmse?0.20 (GPLS+INN), respectively.展开更多
文摘A new descriptor,namely scores vector of zero dimension,one dimension,two dimension and three dimension(SZOTT),was derived from principle components analysis of a matrix of 1 369 structural variables including 0D,1D,2D and 3D information for 20 coded amino acids.SZOTT scales were then employed to express structures of 20 thromboplastin inhibitors and 34 bactericidal peptides.The correlation coefficients of both whole calibration(%R%2=%R%2cu)and of cross validation(%Q%2=%R%2cv)for the multiple-variable models by classical partial least squares(PLS)and orthogonal signal correction-partial least squares(OSC-PLS)of 20 thromboplastin inhibitors were 0.989 and 0.748,0.994 and 0.936,respectively.%R%2 and %Q%2 for the models by PLS and OSC-PLS of 34 bactericidal peptides were 0.619 and 0.406,0.910 and 0.503,respectively.Satisfactory results obtained showed that structural information related to biological activity in both data sets could be described by SZOTT which included plentiful information related to biological activity,and which was conveniently operated and easy interpreted.,also predictive capability of models were relative robust.There is a high prospect for SZOTT wide applications on quantitative sequence-activity modeling(QSAM)of peptides.
基金the Foundations of National High Technology (863) Programme (Grant No. 2006AA02Z312)State New Drug Project (Grant No. 1996ND1035A01)+4 种基金Fok- Yingtung Educational Foundation (Grant No. 980706)State Key Laboratory of Chemo/Biosensing and Chemometrics Foundation (Grant No. KLCB005-0012)Chongqing University Innovation Fund (Grant No. CUIF030506)Chongqing Mu-nicipality Applied Science Fund (Grant No. CASF01-3-6)Momentous Juche Innovation Fund for Tackle Key Problem Items (Grant No. MJIF 06-9-9)
文摘A new descriptor, called vector of topological and structural information for coded and noncoded amino acids (VTSA), was derived by principal component analysis (PCA) from a matrix of 66 topological and structural variables of 134 amino acids. The VTSA vector was then applied into two sets of peptide quantitative structure-activity relationships or quantitative sequence-activity modelings (QSARs/QSAMs). Molded by genetic partial least squares (GPLS), support vector machine (SVM), and immune neural network (INN), good results were obtained. For the datasets of 58 angiotensin converting enzyme inhibitors (ACEI) and 89 elastase substrate catalyzed kinetics (ESCK), the R 2, cross-validation R 2, and root mean square error of estimation (RMSEE) were as follows: ACEI, R cu 2 ?0.82, Q cu 2 ?0.77, E rmse?0.44 (GPLS+SVM); ESCK, R cu 2 ?0.84, Q cu 2 ?0.82, E rmse?0.20 (GPLS+INN), respectively.
