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A combination of 3D-QSAR, molecular docking and molecular dynamics simulation studies of benzimidazole-quinolinone derivatives as iNOS inhibitors.

Zhang H, Zan J, Yu G, Jiang M, Liu P - Int J Mol Sci (2012)

Bottom Line: A QSAR model with R(2) of 0.9356, Q(2) of 0.8373 and Pearson-R value of 0.9406 was constructed, which presents a good predictive ability in both internal and external validation.Furthermore, a combined analysis incorporating the obtained model and the MD results indicates: (1) compounds with the proper-size hydrophobic substituents at position 3 in ring-C (R(3) substituent), hydrophilic substituents near the X(6) of ring-D and hydrophilic or H-bond acceptor groups at position 2 in ring-B show enhanced biological activities; (2) Met368, Trp366, Gly365, Tyr367, Phe363, Pro344, Gln257, Val346, Asn364, Met349, Thr370, Glu371 and Tyr485 are key amino acids in the active pocket, and activities of iNOS inhibitors are consistent with their capability to alter the position of these important residues, especially Glu371 and Thr370.The results provide a set of useful guidelines for the rational design of novel iNOS inhibitors.

View Article: PubMed Central - PubMed

Affiliation: Key Lab of Tianjin Molecular Nuclear Medicine, Institute of Radiation Medicine, Peking Union Medical College, Chinese Academy of Medical Sciences, Tianjin 300192, China; E-Mails: zhanghao27@126.com (H.Z.); sinokang123@yahoo.com.cn (J.Z.); yuguangyun123.good@163.com (G.Y.); jiangming_159@yahoo.com.cn (M.J.).

ABSTRACT
Inducible Nitric Oxide Synthase (iNOS) has been involved in a variety of diseases, and thus it is interesting to discover and optimize new iNOS inhibitors. In previous studies, a series of benzimidazole-quinolinone derivatives with high inhibitory activity against human iNOS were discovered. In this work, three-dimensional quantitative structure-activity relationships (3D-QSAR), molecular docking and molecular dynamics (MD) simulation approaches were applied to investigate the functionalities of active molecular interaction between these active ligands and iNOS. A QSAR model with R(2) of 0.9356, Q(2) of 0.8373 and Pearson-R value of 0.9406 was constructed, which presents a good predictive ability in both internal and external validation. Furthermore, a combined analysis incorporating the obtained model and the MD results indicates: (1) compounds with the proper-size hydrophobic substituents at position 3 in ring-C (R(3) substituent), hydrophilic substituents near the X(6) of ring-D and hydrophilic or H-bond acceptor groups at position 2 in ring-B show enhanced biological activities; (2) Met368, Trp366, Gly365, Tyr367, Phe363, Pro344, Gln257, Val346, Asn364, Met349, Thr370, Glu371 and Tyr485 are key amino acids in the active pocket, and activities of iNOS inhibitors are consistent with their capability to alter the position of these important residues, especially Glu371 and Thr370. The results provide a set of useful guidelines for the rational design of novel iNOS inhibitors.

Show MeSH
(a) Common pharmacophore for active ligands. Pharmacophore features are color-coded: dark blue H-donor, brown H-acceptor, filemot aromatic ring, green hydrophobic group. All distances between pharmacophore features are reported in Ångstroms; (b) Fitness graph between observed activity and Phase predicted activity for training and test set compounds.
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f1-ijms-13-11210: (a) Common pharmacophore for active ligands. Pharmacophore features are color-coded: dark blue H-donor, brown H-acceptor, filemot aromatic ring, green hydrophobic group. All distances between pharmacophore features are reported in Ångstroms; (b) Fitness graph between observed activity and Phase predicted activity for training and test set compounds.

Mentions: All pharmacophore hypotheses produced in the step of searching for common pharmacophores were used to build 3D-QSAR models. After analyzing the alignment between the active ligands and the generated hypothesis, a best hypothesis ADHHR.89 was selected for further research. The selected hypothesis contained one hydrogen bond donor, one hydrogen bond acceptor, one aromatic ring and two hydrophobic groups, as shown in Figure 1a. The QSAR model generated by hypothesis ADHHR.89 was constructed by an atom-based QSAR modeling approach, of which a molecule was treated as a set of overlapping van der Waals spheres. The atom-based QSAR modeling approach was applicable to the structures with a relatively small number of rotatable bonds and common structural framework [22,23]. Atom-Based QSAR models were created by applying partial least squares (PLS) regression to a large set of binary-valued variables that encode whether or not ligand atoms occupy various cube-shaped elements of space [24]. The accuracy of the models improved with the increasing number of PLS factors until overfitting starts to occur. In this study, the best QSAR model with 4 PLS factors was employed, and the QSAR visualization can be shown by cubic 3D grids mapped by ligand atoms. A report suggested that Phase performed well when Q2 > 0.7 or R2 > 0.4 [25]. The statistical parameters associated in the generated QSAR model were as follows: The training set achieved R2 (value of R2 for the regression) of 0.9356, with an SD (standard deviation of the regression) of 0.2863; the test set obtained Q2 (value of Q2 for the predicted activities) of 0.8373, with an RMSE (ROOT-mean-square error) of 0.2567; and Pearson-R (Pearson R value for the correlation between the predicted and observed activity for the test set) of 0.9406. The p value of 1.643 × 10−14 indicated a high degree of confidence. The regression line for the observed and Phase predicted activity was shown in Figure 1b. The predicted activities of the training and test set molecules were also listed in Table 1.


A combination of 3D-QSAR, molecular docking and molecular dynamics simulation studies of benzimidazole-quinolinone derivatives as iNOS inhibitors.

Zhang H, Zan J, Yu G, Jiang M, Liu P - Int J Mol Sci (2012)

(a) Common pharmacophore for active ligands. Pharmacophore features are color-coded: dark blue H-donor, brown H-acceptor, filemot aromatic ring, green hydrophobic group. All distances between pharmacophore features are reported in Ångstroms; (b) Fitness graph between observed activity and Phase predicted activity for training and test set compounds.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC3472740&req=5

f1-ijms-13-11210: (a) Common pharmacophore for active ligands. Pharmacophore features are color-coded: dark blue H-donor, brown H-acceptor, filemot aromatic ring, green hydrophobic group. All distances between pharmacophore features are reported in Ångstroms; (b) Fitness graph between observed activity and Phase predicted activity for training and test set compounds.
Mentions: All pharmacophore hypotheses produced in the step of searching for common pharmacophores were used to build 3D-QSAR models. After analyzing the alignment between the active ligands and the generated hypothesis, a best hypothesis ADHHR.89 was selected for further research. The selected hypothesis contained one hydrogen bond donor, one hydrogen bond acceptor, one aromatic ring and two hydrophobic groups, as shown in Figure 1a. The QSAR model generated by hypothesis ADHHR.89 was constructed by an atom-based QSAR modeling approach, of which a molecule was treated as a set of overlapping van der Waals spheres. The atom-based QSAR modeling approach was applicable to the structures with a relatively small number of rotatable bonds and common structural framework [22,23]. Atom-Based QSAR models were created by applying partial least squares (PLS) regression to a large set of binary-valued variables that encode whether or not ligand atoms occupy various cube-shaped elements of space [24]. The accuracy of the models improved with the increasing number of PLS factors until overfitting starts to occur. In this study, the best QSAR model with 4 PLS factors was employed, and the QSAR visualization can be shown by cubic 3D grids mapped by ligand atoms. A report suggested that Phase performed well when Q2 > 0.7 or R2 > 0.4 [25]. The statistical parameters associated in the generated QSAR model were as follows: The training set achieved R2 (value of R2 for the regression) of 0.9356, with an SD (standard deviation of the regression) of 0.2863; the test set obtained Q2 (value of Q2 for the predicted activities) of 0.8373, with an RMSE (ROOT-mean-square error) of 0.2567; and Pearson-R (Pearson R value for the correlation between the predicted and observed activity for the test set) of 0.9406. The p value of 1.643 × 10−14 indicated a high degree of confidence. The regression line for the observed and Phase predicted activity was shown in Figure 1b. The predicted activities of the training and test set molecules were also listed in Table 1.

Bottom Line: A QSAR model with R(2) of 0.9356, Q(2) of 0.8373 and Pearson-R value of 0.9406 was constructed, which presents a good predictive ability in both internal and external validation.Furthermore, a combined analysis incorporating the obtained model and the MD results indicates: (1) compounds with the proper-size hydrophobic substituents at position 3 in ring-C (R(3) substituent), hydrophilic substituents near the X(6) of ring-D and hydrophilic or H-bond acceptor groups at position 2 in ring-B show enhanced biological activities; (2) Met368, Trp366, Gly365, Tyr367, Phe363, Pro344, Gln257, Val346, Asn364, Met349, Thr370, Glu371 and Tyr485 are key amino acids in the active pocket, and activities of iNOS inhibitors are consistent with their capability to alter the position of these important residues, especially Glu371 and Thr370.The results provide a set of useful guidelines for the rational design of novel iNOS inhibitors.

View Article: PubMed Central - PubMed

Affiliation: Key Lab of Tianjin Molecular Nuclear Medicine, Institute of Radiation Medicine, Peking Union Medical College, Chinese Academy of Medical Sciences, Tianjin 300192, China; E-Mails: zhanghao27@126.com (H.Z.); sinokang123@yahoo.com.cn (J.Z.); yuguangyun123.good@163.com (G.Y.); jiangming_159@yahoo.com.cn (M.J.).

ABSTRACT
Inducible Nitric Oxide Synthase (iNOS) has been involved in a variety of diseases, and thus it is interesting to discover and optimize new iNOS inhibitors. In previous studies, a series of benzimidazole-quinolinone derivatives with high inhibitory activity against human iNOS were discovered. In this work, three-dimensional quantitative structure-activity relationships (3D-QSAR), molecular docking and molecular dynamics (MD) simulation approaches were applied to investigate the functionalities of active molecular interaction between these active ligands and iNOS. A QSAR model with R(2) of 0.9356, Q(2) of 0.8373 and Pearson-R value of 0.9406 was constructed, which presents a good predictive ability in both internal and external validation. Furthermore, a combined analysis incorporating the obtained model and the MD results indicates: (1) compounds with the proper-size hydrophobic substituents at position 3 in ring-C (R(3) substituent), hydrophilic substituents near the X(6) of ring-D and hydrophilic or H-bond acceptor groups at position 2 in ring-B show enhanced biological activities; (2) Met368, Trp366, Gly365, Tyr367, Phe363, Pro344, Gln257, Val346, Asn364, Met349, Thr370, Glu371 and Tyr485 are key amino acids in the active pocket, and activities of iNOS inhibitors are consistent with their capability to alter the position of these important residues, especially Glu371 and Thr370. The results provide a set of useful guidelines for the rational design of novel iNOS inhibitors.

Show MeSH