Three-dimensional quantitative structure-activity relationship studies on c-Src inhibitors based on different docking methods.

c-Src kinase play an important role in cell growth and differentiation and its inhibitors can be useful for the treatment of various diseases, including cancer, osteoporosis, and metastatic bone disease. Three dimensional quantitative structure-activity relationship (3D-QSAR) studies were carried out on quinazolin derivatives inhibiting c-Src kinase. Molecular field analysis (MFA) models with four different alignment techniques, namely, GLIDE, GOLD, LIGANDFIT and Least squares based methods were developed. glide based MFA model showed better results (Leave one out cross validation correlation coefficient r(2)(cv) = 0.923 and non-cross validation correlation coefficient r(2)= 0.958) when compared with other models. These results help us to understand the nature of descriptors required for activity of these compounds and thereby provide guidelines to design novel and potent c-Src kinase inhibitors.

Structural analysis of carboline derivatives as inhibitors of MAPKAP K2 using 3D QSAR and docking studies.

MAPKAPK2, a substrate of p38 MAPKs, plays central role in p38-mediated signal transduction, and its inhibitors are promisingly useful in the treatment of inflammatory diseases. The computational approaches comprising both ligand-based drug design and structure-based drug design were used as virtual screening strategies for the discovery of novel MK2 inhibitors. Two quantitative pharmacophore models were generated with a training set of 27 MK2 inhibitors using HypoGen module of CATALYST. The two models suggested that two hydrogen bond acceptors, one hydrogen bond donor, and one hydrophobic feature are essential for ligand binding. Further, 3D QSAR model with comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) was constructed using a training set of 42 inhibitors. The predictive CoMFA and CoMSIA models have cross-validated coefficients (q2) of 0.804 and 0.765 and regression coefficients (r2) of 0.984 and 0.986, respectively. The structure-based studies were carried out by docking 77 known MAPKAPK2 inhibitors into the active site of receptor using Glide and analyzing the "hotspots" of the active site. Docking studies revealed that Met138, Leu141, Asp207, Lys93, Cys140, Leu70, Thr206, and Gly143 are showing interactions with highly active compounds. The structure activity relationships elucidated here for carboline derivatives combined with their binding information will provide an integrated approach to explore the chemical space further for improving the potency of MAPKAPK2 inhibitors.

Virtual screening of cathepsin k inhibitors using docking and pharmacophore models.

Cathepsin K is a lysosomal cysteine protease that is highly and selectively expressed in osteoclasts, the cells which degrade bone during the continuous cycle of bone degradation and formation. Inhibition of cathepsin K represents a potential therapeutic approach for diseases characterized by excessive bone resorption such as osteoporosis. In order to elucidate the essential structural features for cathepsin K, a three-dimensional pharmacophore hypotheses were built on the basis of a set of known cathepsin K inhibitors selected from the literature using catalyst program. Several methods are used in validation of pharmacophore hypothesis were presented, and the fourth hypothesis (Hypo4) was considered to be the best pharmacophore hypothesis which has a correlation coefficient of 0.944 with training set and has high prediction of activity for a set of 30 test molecules with correlation of 0.909. The model (Hypo4) was then employed as 3D search query to screen the Maybridge database containing 59,000 compounds, to discover novel and highly potent ligands. For analyzing intermolecular interactions between protein and ligand, all the molecules were docked using Glide software. The result showed that the type and spatial location of chemical features encoded in the pharmacophore are in full agreement with the enzyme inhibitor interaction pattern identified from molecular docking.