AIM: To understand pharmacophore properties of styrylquinoline derivatives and to design inhibitors of HIV-1 integrase. METHODS: Comparative molecular field analysis (CoMFA) was performed to analyze three-dimensional quantitative structure-activity relationship (3D-QSAR) of styrylquinoline derivatives. Thirty-eight compounds were randomly divided into a training set of 28 compounds and a test set of 10 compounds. The stability of 3D-QSAR models was proved by the analysis of cross-validated and non-cross-validated methods. Moreover, the binding mode of these compounds and integrase was constructed by AutoDock program. RESULTS: The CoMFA model of the training compounds was reasonably predicted with cross-validated coefficient (q2) and conventional (r2) values (up to 0.696 and 0.754). Then the model was validated by the test set. The resulting CoMFA maps visualized structural requirements for the biological activity of these inhibitors. Docking results showed that a carboxyl group at C-7 and a hydroxyl group at C-8 in the quinoline subunit, bound closely to the divalent metal cofactor (Mg2+) around the integrase catalytic site. Moreover, there is a linear correlation between the binding energy of the inhibitors with integrase and their inhibitory effect. CONCLUSIONS: The present study indicated that the CoMFA model together with docking results could give us helpful hints for drug design as well as interpretation of the binding affinity between these inhibitors and integrase.
AIM: To understand pharmacophore properties of styrylquinoline derivatives and to design inhibitors of HIV-1 integrase. METHODS: Comparative molecular field analysis (CoMFA) was performed to analyze three-dimensional quantitative structure-activity relationship (3D-QSAR) of styrylquinoline derivatives. Thirty-eight compounds were randomly divided into a training set of 28 compounds and a test set of 10 compounds. The stability of 3D-QSAR models was proved by the analysis of cross-validated and non-cross-validated methods. Moreover, the binding mode of these compounds and integrase was constructed by AutoDock program. RESULTS: The CoMFA model of the training compounds was reasonably predicted with cross-validated coefficient (q2) and conventional (r2) values (up to 0.696 and 0.754). Then the model was validated by the test set. The resulting CoMFA maps visualized structural requirements for the biological activity of these inhibitors. Docking results showed that a carboxyl group at C-7 and a hydroxyl group at C-8 in the quinoline subunit, bound closely to the divalent metal cofactor (Mg2+) around the integrase catalytic site. Moreover, there is a linear correlation between the binding energy of the inhibitors with integrase and their inhibitory effect. CONCLUSIONS: The present study indicated that the CoMFA model together with docking results could give us helpful hints for drug design as well as interpretation of the binding affinity between these inhibitors and integrase.