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Abstract: β-Secretase is an important protease in the pathogenesis of Alzheimer’s disease. Some statine-based peptidomimetics show inhibitory activities to the β-secretase. To explore the inhibitory mechanism, molecular docking and three-dimensional quantitative structure–activity relationship (3D-QSAR) studies on these analogues were performed. The Lamarckian Genetic Algorithm (LGA) was applied to locate the binding orientations and conformations of the peptidomimetics with the β-secretase. A good correlation between the calculated binding free energies and the experimental inhibitory activities suggests that the identified binding conformations of these potential inhibitors are reliable. Based on the binding conformations, highly predictive 3D-QSAR models were developed with q2 values of 0.582 and 0.622 for CoMFA and CoMSIA, respectively. The predictive abilities of these models were validated by some compounds that were not included in the training set. Furthermore, the 3D-QSAR models were mapped back to the binding site of the β-secretase, to get a better understanding of vital interactions between the statine-based peptidomimetics and the protease. Both the CoMFA and the CoMSIA field distributions are in well agreement with the structural characteristics of the binding groove of the β-secretase. Therefore, the final 3D-QSAR models and the information of the inhibitor–enzyme interaction would be useful in developing new drug leads against Alzheimer’s disease. [Copyright &y& Elsevier]

The interaction of a series of indole-2-carboxamide compounds with human liver glycogen phosphorylase a (HLGPa) have been studied employing molecular docking and 3D-QSAR approaches. The Lamarckian Genetic Algorithm (LGA) of AutoDock 3.0 was employed to locate the binding orientations and conformations of the inhibitors interacting with HLGPa. The binding models were demonstrated in the aspects of inhibitor''s conformation, subsite interaction, and hydrogen bonding. The very similar binding conformations of these inhibitors show that they interact with HLGPa in a very similar way. Good correlations between the calculated interaction free energies and experimental inhibitory activities suggest that the binding conformations of these inhibitors are reasonable. The structural and energetic differences in inhibitory potencies of indole-2-carboxamide compounds were reasonably explored. Using the binding conformations of indole-2-carboxamides, consistent and highly predictive 3D-QSAR models were developed by CoMFA and CoMSIA analyses. The q2 values are 0.697 and 0.622 for CoMFA and CoMSIA models, respectively. The predictive ability of these models was validated by four compounds that were not included in the training set. Mapping these models back to the topology of the active site of HLGPa leads to a better understanding of the vital indole-2-carboxamide–HLGPa interactions. Structure-based investigations and the final 3D-QSAR results provide clear guidelines and accurate activity predictions for novel inhibitor design. [Copyright &y& Elsevier]

Abstract: It is recently proposed that compounds with equal capabilities of inhibiting COX and 5-LOX, both are key enzymes involved in the arachidonic acid (AA) cascade, are expected to be safer non-steroidal anti-inflammatory drugs (NSAIDs). To dig out helpful information in designing dual functional inhibitors against the two enzymes, homology modeling, molecular dynamics (MD) simulations, automated docking, and 3D-QSAR analyses were performed in this study on 21 COX-2/5-LOX dual inhibitors, namely, 7-tert-butyl-2,3-dihydro-3,3-dimethylbenzofuran (DHDMBF) analogues. A 3D-model of 5-LOX was built based on the high-resolution X-ray structure of rabbit reticulocyte 15-lipoxygenase. Molecular docking was then applied to locate the binding orientations and conformations of DHDMBF analogues with COX-2 and 5-LOX, respectively, leading to highly predictive CoMFA models constructed on the basis of the binding conformations with q 2 values of 0.782 and 0.634 for COX-2 and 5-LOX, respectively. In addition, CoMFA field distributions were found in good agreement with the structural characteristics of the corresponding binding sites. Both the docking simulations and QSAR analyses suggest that new potent dual inhibitors should share a structural feature with a moderately bulky group at R2 position and a rather negatively charged group around the position of the carbonyl group of DHDMBFs. Therefore, the final 3D-QSAR models and the information of the inhibitor–enzyme interaction should be useful in developing new NSAIDs as anti-inflammation drugs with favorable safety profile. [Copyright &y& Elsevier]