Current Computer - Aided Drug Design - Volume 8, Issue 4, 2012

The human immunodeficiency virus type 1 (HIV-1) integrase is an emerging target for novel antiviral drugs. Quantitative structure-activity relationship (QSAR) models for HIV-1 integrase inhibitors have been developed to understand the protein-ligand interactions to aid in the design of more effective analogs. This review paper presents a comprehensive overview of the computational modeling methods and results of QSAR models of HIV-1 integrase inhibitors published in 2005-2010. These QSAR models are classified according to the generation of molecular descriptors: 2D-QSAR, 3D-QSAR, and 4D-QSAR. Linear and non-linear modeling methods have been applied to derive these QSAR models, with the majority of the models derived from linear statistical methods such as multiple linear regression and partial least squares. While each of the published QSAR models have provided insight on the distinct chemical features of HIV-1 integrase inhibitors crucial for biological activity, only a few models have been used to propose and synthesize new HIV-1 integrase inhibitors. This study highlights the need for collaboration between computational and experimental chemists to utilize and improve these QSAR models to guide the design of the next generation of HIV-1 integrase inhibitors.

DHODH is a flavoenzyme that catalyzes the oxidation of dihydroorotate (DHO) to orotate (ORO) as part of the fourth and rate limiting step of the de novo pyrimidine biosynthetic pathway. Inhibitors of DHODHs have proven efficacy for the treatment of cancer, malaria and immunological disorders. 3D QSAR studies on some aryl carboxylic acid amide derivatives as hDHODH inhibitors were performed by comparative molecular field analysis (CoMFA) and comparative molecular similarity indices (CoMSIA) methods to rationalize the structural requirements responsible for the inhibitory activity of these compounds. The alignment strategy was used for these compounds by means of Distill function defined in SYBYL X 1.2. The best CoMFA and CoMSIA models obtained for the training set were statistically significant with cross-validated coefficients (q2) of 0.636 and 0.604 and conventional coefficients (r2) of 0.993 and 0.950, respectively. Both the models were validated by an external test set of five compounds giving satisfactory prediction (r2 pred) of 0.563 and 0.523 for CoMFA and CoMSIA models, respectively. Further the robustness of the model was verified by bootstrapping analysis. Generated CoMFA and CoMSIA models provide useful information for the design of novel inhibitors with good hDHODH inhibitory.

β3 adrenergic receptor (β3AR) is known to mediate various pharmacological and physiological effects such as thermogenesis in brown adipocytes, lipolysis in white adipocytes, glucose homeostasis and intestinal smooth muscle relaxation. Several efforts have been made in this field to understand their function and regulation in different human tissues and they have emerged as potential attractive targets in drug discovery for the treatment of diabetes, depression, obesity etc. Although the crystal structures of Bovine Rhodopsin and β2 adrenergic receptor have been resolved, to date there is no three dimensional structural information on β3AR. Our aim in this study was to model 3D structure of β3AR by various molecular modeling and simulation techniques. In this paper, we describe a refined predicted model of β3AR using different algorithms for structure prediction. The structural refinement and minimization of the generated 3D model of β3AR were done by Schrodinger suite 9.1. Docking studies of β3AR model with the known agonists enabled us to identify specific residues, viz, Asp 117, Ser 208, Ser 209, Ser 212, Arg 315, Asn 332, within the β3AR binding pocket, which might play an important role in ligand binding. Receptor ligand interaction studies clearly indicated that these five residues showed strong hydrogen bonding interactions with the ligands. The results have been correlated with the experimental data available. The predicted ligand binding interactions and the simulation studies validate the methods used to predict the 3D-structure.

The QSAR and docking studies were performed on fifty seven steroids with binding affinities for corticosteroid-binding globulin (CBG) and eighty four steroids with binding affinities for sex hormone-binding globulin (SHBG). Since the steroidal compounds have binding affinity for both CBG and SHBG, multi-target QSAR approach was employed to establish a unique QSAR method for simultaneous evaluation of the CBG and SHBG binding affinities. The constitutional, geometrical, physico-chemical and electronic descriptors were computed for the examined structures by use of the Chem3D Ultra 7.0.0, the Dragon 6.0, the MOPAC2009, and the Chemical Descriptors Library (CDL) program. Partial least squares regression (PLSR) has been applied for selection of the most relevant molecular descriptors and QSAR models building. The QSAR (SHGB) model, QSAR model (CBG), and multi-target QSAR model (CBG, SHBG) were created. The multi-target QSAR model (CBG and SHBG) was found to be more effective in describing the CBG and SHBG affinity of steroids in comparison to the one target models (QSAR (SHGB) model, QSAR model (CBG)). The multi-target QSAR study indicated the importance of the electronic descriptor (Mor16v), steric/symmetry descriptors (Eig06_EA(ed)), 2D autocorrelation descriptor (GATS4m), distance distribution descriptor (RDF045m), and atom type fingerprint descriptor (CDL-ATFP 253) in describing the CBG and SHBG affinity of steroidal compounds. Results of the created multi-target QSAR model were in accordance with the performed docking studies. The theoretical study defined physicochemical, electronic and structural requirements for selective and effective binding of steroids to the CBG and SHBG active sites.

Aldolase has emerged as a promising molecular target for the treatment of human African trypanosomiasis. Over the last years, due to the increasing number of patients infected with Trypanosoma brucei, there is an urgent need for new drugs to treat this neglected disease. In the present study, two-dimensional fragment-based quantitative-structure activity relationship (QSAR) models were generated for a series of inhibitors of aldolase. Through the application of leave-one-out and leave-many-out cross-validation procedures, significant correlation coefficients were obtained (r2 = 0.98 and q2 = 0.77) as an indication of the statistical internal and external consistency of the models. The best model was employed to predict pKi values for a series of test set compounds, and the predicted values were in good agreement with the experimental results, showing the power of the model for untested compounds. Moreover, structure-based molecular modeling studies were performed to investigate the binding mode of the inhibitors in the active site of the parasitic target enzyme. The structural and QSAR results provided useful molecular information for the design of new aldolase inhibitors within this structural class.

DNA methyltransferases (DNMTs) are emerging targets for the treatment of cancer and other diseases. The quinolone-based compound, SGI-1027, is a promising inhibitor of DNMT1 with a distinct mode of action and it is an attractive starting point for further research. Several experimental and computational approaches can be used to further develop novel DNMT1 inhibitors based on SGI-1027. In this work, we used a chemoinformatic-based approach to explore the potential to identify novel inhibitors in large screening collections of natural products and synthetic commercial libraries. Using the principles of similarity searching, the similarity profile to the active reference compound SGI-1027 was computed for four different screening libraries using a total of 22 two- and three- dimensional representations and two similarity metrics. The compound library with the overall highest similarity profile to the probe molecule was identified as the most promising collection for experimental testing. Individual compounds with high similarity to the reference were also selected as suitable candidates for experimental validation. During the course of this work, the 22 twoand three- dimensional representations were compared to each other and classified based on the similarity values computed with the reference compound. This classification is valuable to select structure representations for similarity searching of any other screening library. This work represents a step forward to further advance epigenetic therapies using computational approaches.

3D pharmacophore modeling is an important computational methodology for ligand-enzyme binding interactions in drug discovery. More specifically, a consensus pharmacophore model derived from diverse ligands is a key determinant upon which the prediction power of computational models is based for designing novel ligands. In this work, by merging the important pharmacophore features based on four classes of covalent FAAH ligands, and then integrating the exclusion volume spheres derived from the crystal structure, we created for the first time an integrated FAAH pharmacophore model to describe the ligand-enzyme binding interactions. This new integrated FAAH pharmacophore model can correctly predict the covalent ligand binding mode, which correlates with the SAR data. The study is expected to provide insights into novel covalent ligand-FAAH binding interactions, and facilitate the design of covalent ligands against FAAH.