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3D-QSAR studies on a series of dihydroorotate dehydrogenase inhibitors: analogues of the active metabolite of leflunomide.

Li SL, He MY, Du HG - Int J Mol Sci (2011)

Bottom Line: Self-organizing molecular field analysis (SOMFA), a simple three-dimensional quantitative structure-activity relationship (3D-QSAR) method is used to study the correlation between the molecular properties and the biological activities of a series of analogues of the active metabolite.The statistical results, cross-validated r(CV) (2) (0.664) and non cross-validated r(2) (0.687), show a good predictive ability.The final SOMFA model provides a better understanding of DHODH inhibitor-enzyme interactions, and may be useful for further modification and improvement of inhibitors of this important enzyme.

View Article: PubMed Central - PubMed

Affiliation: College of Science, Beijing University of Chemical Technology, 15 Beisanhuan East Road, Chaoyang District, Beijing 100029, China; E-Mail: wulianhmy2008@163.com (M.-Y.H.).

ABSTRACT
The active metabolite of the novel immunosuppressive agent leflunomide has been shown to inhibit the enzyme dihydroorotate dehydrogenase (DHODH). This enzyme catalyzes the fourth step in de novo pyrimidine biosynthesis. Self-organizing molecular field analysis (SOMFA), a simple three-dimensional quantitative structure-activity relationship (3D-QSAR) method is used to study the correlation between the molecular properties and the biological activities of a series of analogues of the active metabolite. The statistical results, cross-validated r(CV) (2) (0.664) and non cross-validated r(2) (0.687), show a good predictive ability. The final SOMFA model provides a better understanding of DHODH inhibitor-enzyme interactions, and may be useful for further modification and improvement of inhibitors of this important enzyme.

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The electrostatic potential master grid with compound 43, red represents areas where postive potential is favorable, or negative charge is unfavorable, blue represents areas where negative potential is favorable, or postive charge is unfavorable. (a) Rat DHODH and (b) Mouse DHODH.
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f5-ijms-12-02982: The electrostatic potential master grid with compound 43, red represents areas where postive potential is favorable, or negative charge is unfavorable, blue represents areas where negative potential is favorable, or postive charge is unfavorable. (a) Rat DHODH and (b) Mouse DHODH.

Mentions: SOMFA calculation for both shape and electrostatic potentials are performed, then combined to get an optimal coefficient c1 = 0.766 according to Equation 1. The master grid maps derived from the best model is used to display the contribution of electrostatic potential and shape molecular field. The master grid maps give a direct visual indication of which parts of the compounds differentiate the activities of compounds in the training set under study. The master grid also offers an interpretation as to how to design and synthesize some novel compounds with much higher activities. The visualization of the potential master grid and shape master grid of the best SOMFA model is showed in Figure 5 and Figure 6 respectively, with compound 43 as the reference.


3D-QSAR studies on a series of dihydroorotate dehydrogenase inhibitors: analogues of the active metabolite of leflunomide.

Li SL, He MY, Du HG - Int J Mol Sci (2011)

The electrostatic potential master grid with compound 43, red represents areas where postive potential is favorable, or negative charge is unfavorable, blue represents areas where negative potential is favorable, or postive charge is unfavorable. (a) Rat DHODH and (b) Mouse DHODH.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC3116169&req=5

f5-ijms-12-02982: The electrostatic potential master grid with compound 43, red represents areas where postive potential is favorable, or negative charge is unfavorable, blue represents areas where negative potential is favorable, or postive charge is unfavorable. (a) Rat DHODH and (b) Mouse DHODH.
Mentions: SOMFA calculation for both shape and electrostatic potentials are performed, then combined to get an optimal coefficient c1 = 0.766 according to Equation 1. The master grid maps derived from the best model is used to display the contribution of electrostatic potential and shape molecular field. The master grid maps give a direct visual indication of which parts of the compounds differentiate the activities of compounds in the training set under study. The master grid also offers an interpretation as to how to design and synthesize some novel compounds with much higher activities. The visualization of the potential master grid and shape master grid of the best SOMFA model is showed in Figure 5 and Figure 6 respectively, with compound 43 as the reference.

Bottom Line: Self-organizing molecular field analysis (SOMFA), a simple three-dimensional quantitative structure-activity relationship (3D-QSAR) method is used to study the correlation between the molecular properties and the biological activities of a series of analogues of the active metabolite.The statistical results, cross-validated r(CV) (2) (0.664) and non cross-validated r(2) (0.687), show a good predictive ability.The final SOMFA model provides a better understanding of DHODH inhibitor-enzyme interactions, and may be useful for further modification and improvement of inhibitors of this important enzyme.

View Article: PubMed Central - PubMed

Affiliation: College of Science, Beijing University of Chemical Technology, 15 Beisanhuan East Road, Chaoyang District, Beijing 100029, China; E-Mail: wulianhmy2008@163.com (M.-Y.H.).

ABSTRACT
The active metabolite of the novel immunosuppressive agent leflunomide has been shown to inhibit the enzyme dihydroorotate dehydrogenase (DHODH). This enzyme catalyzes the fourth step in de novo pyrimidine biosynthesis. Self-organizing molecular field analysis (SOMFA), a simple three-dimensional quantitative structure-activity relationship (3D-QSAR) method is used to study the correlation between the molecular properties and the biological activities of a series of analogues of the active metabolite. The statistical results, cross-validated r(CV) (2) (0.664) and non cross-validated r(2) (0.687), show a good predictive ability. The final SOMFA model provides a better understanding of DHODH inhibitor-enzyme interactions, and may be useful for further modification and improvement of inhibitors of this important enzyme.

Show MeSH