Atoms in most organic molecules are often carbon,oxygen,nitrogen,sulfur,halogens,etc. Based on the three-dimensional structure of a molecule,a molecular structural characterization（MSC） method called improved molecu...Atoms in most organic molecules are often carbon,oxygen,nitrogen,sulfur,halogens,etc. Based on the three-dimensional structure of a molecule,a molecular structural characterization（MSC） method called improved molecular electronegativity-distance vector（I-MEDV） was developed. It was used to describe the structures of 37 compounds of styrax japonicus sieb flowers. Through multiple linear regression（MLR）,a QSRR model was built up. The correlation coefficient（R1） of the model was 0.980. Then,4 vectors were selected to build another model through the method of stepwise multiple regression（SMR） ,and the correlation coefficient（R2） of the model was 0.975. Moreover,all the two models were evaluated by performing the crossvalidation with the leave-one-out（LOO） procedure and the correlation coefficients（Rcv） were 0.948 and 0.968,respectively. The results show that the I-MEDV could successfully describe the structures of organic compounds. The stability and predictability of the models were good.展开更多
Polychlorinated dibenzothiophenes（PCDTs） are classified as persistent organic pollutants in the environment,so the analysis of PCDTs by their gas chromatographic behaviors is of great significance.Quantitative struc...Polychlorinated dibenzothiophenes（PCDTs） are classified as persistent organic pollutants in the environment,so the analysis of PCDTs by their gas chromatographic behaviors is of great significance.Quantitative structure-retention relationship（QSRR） analysis is a useful technique capable of relating chromatographic retention time to the molecular structure.In this paper,a QSRR study of 37 PCDTs was carried out by using molecular electronegativity distance vector（MEDV） descriptors and multiple linear regression（MLR） and partial least-squares regression（PLS） methods.The correlation coefficient R of established MLR,PLS models,leave-one-out（LOO） cross-validation（CV）,Q2ext were 0.9951,0.9942,0.9839（MLR） and 0.9925,0.9915,0.9833（PLS）,respectively.Results showed that the model exhibited excellent estimate capability for internal sample set and good predictive capability for external sample set.By using MEDV descriptors,the QSRR model can provide a simple and rapid way to predict the gas-chromatographic retention indices of polychlorinated dibenzothiophenes in conditions of lacking standard samples or poor experimental conditions.展开更多
A set of novel structural descriptors (molecular hybridization electronegativity-distance vector, VMEDh) was put forward, and the quantitative structure–activity relationship (QSAR) of a series of 17α-Acetoxyprogest...A set of novel structural descriptors (molecular hybridization electronegativity-distance vector, VMEDh) was put forward, and the quantitative structure–activity relationship (QSAR) of a series of 17α-Acetoxyprogesterones (APs) was investigated. Taking into account the effect of various hybridized orbits on atomic electronegativities, we developed the structure descriptors with amended electronegativities to build a QSAR model. The 10-parameter model based on VMEDh yields a correlation coefficient R=0.972 and standard deviation SD=0.262, which are more desirable than those of the previous molecular electonegativity-distance vector (MEDV-4) (R=0.969, SD=0.275). By stepwise multiple linear regression, several parameters are selected to construct optimal models. The 7-parameter model based on VMEDh has R=0.960 and SD=0.276; its correlation coefficient (RCV) and standard deviation (SDCV) for leave-one-out procedure crossvalidation are respectively RCV=0.890 and SDCV=0.445. The 6-parameter MEDV-4 model has R=0.946, SD=0.304, RCV=0.903 and SDCV=0.406. It is demonstrated that VMEDh has desirable estimation performance and good predictive capability for this series of chemical compounds.展开更多
Only from the primary structures of peptides, a new set of descriptors called the molecular electro-negativity edge-distance vector (VMED) was proposed and applied to describing and characterizing the molecular struct...Only from the primary structures of peptides, a new set of descriptors called the molecular electro-negativity edge-distance vector (VMED) was proposed and applied to describing and characterizing the molecular structures of oligopeptides and polypeptides, based on the electronegativity of each atom or electronic charge index (ECI) of atomic clusters and the bonding distance between atom-pairs. Here, the molecular structures of antigenic polypeptides were well expressed in order to propose the auto-mated technique for the computerized identification of helper T lymphocyte (Th) epitopes. Furthermore, a modified MED vector was proposed from the primary structures of polypeptides, based on the ECI and the relative bonding distance of the fundamental skeleton groups. The side-chains of each amino acid were here treated as a pseudo-atom. The developed VMED was easy to calculate and able to work. Some quantitative model was established for 28 immunogenic or antigenic polypeptides (AGPP) with 14 (1― 14) Ad and 14 other restricted activities assigned as "1"(+) and "0"(-), respectively. The latter comprised 6 Ab(15-20), 3 Ak(21-23), 2 Ek(24-26), 2 H-2k(27 and 28) restricted sequences. Good results were obtained with 90% correct classification (only 2 wrong ones for 20 training samples) and 100% correct prediction(none wrong for 8 testing samples); while con-trastively 100% correct classification (none wrong for 20 training samples) and 88% correct classification (1 wrong for 8 testing samples). Both stochastic samplings and cross valida-tions were performed to demonstrate good performance. The described method may also be suitable for estimation and prediction of classes I and II for major histocompatibility an-tigen (MHC) epitope of human. It will be useful in immune identification and recognition of pro-teins and genes and in the design and devel-opment of subunit vaccines. Several quantitative structure activity relationship (QSAR) models were developed for various oligopeptides and polypeptides including 58 dipeptides and 31 pentapeptides with angiotensin converting enzyme (ACE) inhibition by multiple linear regression (MLR) method. In order to explain the ability to characterize molecular structure of polypeptides, a molecular modeling investigation on QSAR was performed for functional prediction of polypeptide sequences with anti-genic activity and heptapeptide sequences with tachykinin activity through quantitative se-quence-activity models (QSAMs) by the molecular electronegativity edge-distance vector (VMED). The results showed that VMED exhibited both excellent structural selectivity and good activity prediction. Moreover, the results showed that VMED behaved quite well for both QSAR and QSAM of poly-and oli-gopeptides, which exhibited both good estimation ability and prediction power, equal to or better than those reported in the previous references. Finally, a preliminary conclusion was drwan: both classical and modified MED vectors were very useful structural descriptors. Some suggestions were proposed for further studies on QSAR/QSAM of proteins in various fields.展开更多
基金supported by the Youth Foundation of Education Bureau,Sichuan Province (09ZB036)Technology Bureau,Sichuan Province (2006j13-141)
文摘Atoms in most organic molecules are often carbon,oxygen,nitrogen,sulfur,halogens,etc. Based on the three-dimensional structure of a molecule,a molecular structural characterization（MSC） method called improved molecular electronegativity-distance vector（I-MEDV） was developed. It was used to describe the structures of 37 compounds of styrax japonicus sieb flowers. Through multiple linear regression（MLR）,a QSRR model was built up. The correlation coefficient（R1） of the model was 0.980. Then,4 vectors were selected to build another model through the method of stepwise multiple regression（SMR） ,and the correlation coefficient（R2） of the model was 0.975. Moreover,all the two models were evaluated by performing the crossvalidation with the leave-one-out（LOO） procedure and the correlation coefficients（Rcv） were 0.948 and 0.968,respectively. The results show that the I-MEDV could successfully describe the structures of organic compounds. The stability and predictability of the models were good.
基金supported by the Foundation of Returned Scholars (Main Program) of Shanxi Province (200902)
文摘Polychlorinated dibenzothiophenes（PCDTs） are classified as persistent organic pollutants in the environment,so the analysis of PCDTs by their gas chromatographic behaviors is of great significance.Quantitative structure-retention relationship（QSRR） analysis is a useful technique capable of relating chromatographic retention time to the molecular structure.In this paper,a QSRR study of 37 PCDTs was carried out by using molecular electronegativity distance vector（MEDV） descriptors and multiple linear regression（MLR） and partial least-squares regression（PLS） methods.The correlation coefficient R of established MLR,PLS models,leave-one-out（LOO） cross-validation（CV）,Q2ext were 0.9951,0.9942,0.9839（MLR） and 0.9925,0.9915,0.9833（PLS）,respectively.Results showed that the model exhibited excellent estimate capability for internal sample set and good predictive capability for external sample set.By using MEDV descriptors,the QSRR model can provide a simple and rapid way to predict the gas-chromatographic retention indices of polychlorinated dibenzothiophenes in conditions of lacking standard samples or poor experimental conditions.
基金Funded by Chongqing Medical University Scientific Research Foundation
文摘A set of novel structural descriptors (molecular hybridization electronegativity-distance vector, VMEDh) was put forward, and the quantitative structure–activity relationship (QSAR) of a series of 17α-Acetoxyprogesterones (APs) was investigated. Taking into account the effect of various hybridized orbits on atomic electronegativities, we developed the structure descriptors with amended electronegativities to build a QSAR model. The 10-parameter model based on VMEDh yields a correlation coefficient R=0.972 and standard deviation SD=0.262, which are more desirable than those of the previous molecular electonegativity-distance vector (MEDV-4) (R=0.969, SD=0.275). By stepwise multiple linear regression, several parameters are selected to construct optimal models. The 7-parameter model based on VMEDh has R=0.960 and SD=0.276; its correlation coefficient (RCV) and standard deviation (SDCV) for leave-one-out procedure crossvalidation are respectively RCV=0.890 and SDCV=0.445. The 6-parameter MEDV-4 model has R=0.946, SD=0.304, RCV=0.903 and SDCV=0.406. It is demonstrated that VMEDh has desirable estimation performance and good predictive capability for this series of chemical compounds.
基金Supported by National High-Tech R&D Programme of China (863) (Grant No. 2006AA02Z312)National 111 Programme Introducing Talents of Discipline to Universities (Grant No. 0507111106)+6 种基金National Chunhui Project (Grant No. 990404+00307)State New Drug Project (Grant No. 1996ND1035A01)Fok YingTung Educational Foundation (Grant No. 980706)State Key Laboratory of Chemo/Biosensing and Chemometrics Foundation (KCBCF0501201)Chongqing University Innovation Fund (CUIF030506)Chongqing Municipality Applied Science Fund (Grant No. CASF01-3-6)Momentous Juche Innovation Fundfor Tackle Key Problem Items (MJIF 03-5-6+04-10-10)
文摘Only from the primary structures of peptides, a new set of descriptors called the molecular electro-negativity edge-distance vector (VMED) was proposed and applied to describing and characterizing the molecular structures of oligopeptides and polypeptides, based on the electronegativity of each atom or electronic charge index (ECI) of atomic clusters and the bonding distance between atom-pairs. Here, the molecular structures of antigenic polypeptides were well expressed in order to propose the auto-mated technique for the computerized identification of helper T lymphocyte (Th) epitopes. Furthermore, a modified MED vector was proposed from the primary structures of polypeptides, based on the ECI and the relative bonding distance of the fundamental skeleton groups. The side-chains of each amino acid were here treated as a pseudo-atom. The developed VMED was easy to calculate and able to work. Some quantitative model was established for 28 immunogenic or antigenic polypeptides (AGPP) with 14 (1― 14) Ad and 14 other restricted activities assigned as "1"(+) and "0"(-), respectively. The latter comprised 6 Ab(15-20), 3 Ak(21-23), 2 Ek(24-26), 2 H-2k(27 and 28) restricted sequences. Good results were obtained with 90% correct classification (only 2 wrong ones for 20 training samples) and 100% correct prediction(none wrong for 8 testing samples); while con-trastively 100% correct classification (none wrong for 20 training samples) and 88% correct classification (1 wrong for 8 testing samples). Both stochastic samplings and cross valida-tions were performed to demonstrate good performance. The described method may also be suitable for estimation and prediction of classes I and II for major histocompatibility an-tigen (MHC) epitope of human. It will be useful in immune identification and recognition of pro-teins and genes and in the design and devel-opment of subunit vaccines. Several quantitative structure activity relationship (QSAR) models were developed for various oligopeptides and polypeptides including 58 dipeptides and 31 pentapeptides with angiotensin converting enzyme (ACE) inhibition by multiple linear regression (MLR) method. In order to explain the ability to characterize molecular structure of polypeptides, a molecular modeling investigation on QSAR was performed for functional prediction of polypeptide sequences with anti-genic activity and heptapeptide sequences with tachykinin activity through quantitative se-quence-activity models (QSAMs) by the molecular electronegativity edge-distance vector (VMED). The results showed that VMED exhibited both excellent structural selectivity and good activity prediction. Moreover, the results showed that VMED behaved quite well for both QSAR and QSAM of poly-and oli-gopeptides, which exhibited both good estimation ability and prediction power, equal to or better than those reported in the previous references. Finally, a preliminary conclusion was drwan: both classical and modified MED vectors were very useful structural descriptors. Some suggestions were proposed for further studies on QSAR/QSAM of proteins in various fields.
