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Liansheng Wang – One of the best experts on this subject based on the ideXlab platform.

  • Predicting octanol/water partition coefficient using solvation free energy and solvent-Accessible Surface Area.
    Journal of environmental sciences (China), 2001
    Co-Authors: Xinhui Liu, Shuo-kui Han, Liansheng Wang
    Abstract:

    The regression model for octanol/water partition coefficients (Kow), is founded with only two molecular descriptors available through quantum chemical calculations: solvation free energy (delta Gs), and solvent-Accessible Surface Area (SASA). For the properties of 47 organic compounds from 17 types, the model gives a correction coefficient (adjusted for degrees of freedom) of 0.959 and a standard error of 0.277 log unit. It is a suitable way to predict the partition properties that are related to solute-solvent interactions in the water phase.

  • predicting octanol water partition coefficient using solvation free energy and solvent Accessible Surface Area
    Journal of Environmental Sciences-china, 2001
    Co-Authors: Xinhui Liu, Shuo-kui Han, Liansheng Wang
    Abstract:

    The regression model for octanol/water partition coefficients (Kow), is founded with only two molecular descriptors available through quantum chemical calculations: solvation free energy (delta Gs), and solvent-Accessible Surface Area (SASA). For the properties of 47 organic compounds from 17 types, the model gives a correction coefficient (adjusted for degrees of freedom) of 0.959 and a standard error of 0.277 log unit. It is a suitable way to predict the partition properties that are related to solute-solvent interactions in the water phase.

Xinhui Liu – One of the best experts on this subject based on the ideXlab platform.

  • Predicting octanol/water partition coefficient using solvation free energy and solvent-Accessible Surface Area.
    Journal of environmental sciences (China), 2001
    Co-Authors: Xinhui Liu, Shuo-kui Han, Liansheng Wang
    Abstract:

    The regression model for octanol/water partition coefficients (Kow), is founded with only two molecular descriptors available through quantum chemical calculations: solvation free energy (delta Gs), and solvent-Accessible Surface Area (SASA). For the properties of 47 organic compounds from 17 types, the model gives a correction coefficient (adjusted for degrees of freedom) of 0.959 and a standard error of 0.277 log unit. It is a suitable way to predict the partition properties that are related to solute-solvent interactions in the water phase.

  • predicting octanol water partition coefficient using solvation free energy and solvent Accessible Surface Area
    Journal of Environmental Sciences-china, 2001
    Co-Authors: Xinhui Liu, Shuo-kui Han, Liansheng Wang
    Abstract:

    The regression model for octanol/water partition coefficients (Kow), is founded with only two molecular descriptors available through quantum chemical calculations: solvation free energy (delta Gs), and solvent-Accessible Surface Area (SASA). For the properties of 47 organic compounds from 17 types, the model gives a correction coefficient (adjusted for degrees of freedom) of 0.959 and a standard error of 0.277 log unit. It is a suitable way to predict the partition properties that are related to solute-solvent interactions in the water phase.

Yanhong Zhou – One of the best experts on this subject based on the ideXlab platform.

  • identifying protein protein interaction sites in transient complexes with temperature factor sequence profile and Accessible Surface Area
    Amino Acids, 2010
    Co-Authors: Rong Liu, Wenchao Jiang, Yanhong Zhou
    Abstract:

    Transient protein–protein interactions play a vital role in many biological processes, such as cell regulation and signal transduction. A nonredundant dataset of 130 protein chains extracted from transient complexes was used to analyze the features of transient interfaces. It was found that besides the two well-known features, sequence profile and Accessible Surface Area (ASA), the temperature factor (B-factor) can also reflect the differences between interface and the rest of protein Surface. These features were utilized to construct support vector machine (SVM) classifiers to identify interaction sites. The results of threefold cross-validation on the nonredundant dataset show that when B-factor was used as an additional feature, the prediction performance can be improved significantly. The sensitivity, specificity and correlation coefficient were raised from 54 to 62%, 41 to 45% and 0.20 to 0.29, respectively. To further illustrate the effectiveness of our method, the classifiers were tested with an independent set of 53 nonhomologous protein chains derived from benchmark 2.0. The sensitivity, specificity and correlation coefficient of the classifier based on the three features were 63%, 45% and 0.33, respectively. It is indicated that our classifiers are robust and can be applied to complement experimental techniques in studying transient protein–protein interactions.

  • Identifying protein–protein interaction sites in transient complexes with temperature factor, sequence profile and Accessible Surface Area
    Amino acids, 2009
    Co-Authors: Rong Liu, Wenchao Jiang, Yanhong Zhou
    Abstract:

    Transient protein–protein interactions play a vital role in many biological processes, such as cell regulation and signal transduction. A nonredundant dataset of 130 protein chains extracted from transient complexes was used to analyze the features of transient interfaces. It was found that besides the two well-known features, sequence profile and Accessible Surface Area (ASA), the temperature factor (B-factor) can also reflect the differences between interface and the rest of protein Surface. These features were utilized to construct support vector machine (SVM) classifiers to identify interaction sites. The results of threefold cross-validation on the nonredundant dataset show that when B-factor was used as an additional feature, the prediction performance can be improved significantly. The sensitivity, specificity and correlation coefficient were raised from 54 to 62%, 41 to 45% and 0.20 to 0.29, respectively. To further illustrate the effectiveness of our method, the classifiers were tested with an independent set of 53 nonhomologous protein chains derived from benchmark 2.0. The sensitivity, specificity and correlation coefficient of the classifier based on the three features were 63%, 45% and 0.33, respectively. It is indicated that our classifiers are robust and can be applied to complement experimental techniques in studying transient protein–protein interactions.