Anand Balupuri

Development of 3D-pharmacophore model followed by successive virtual screening, molecular docking and ADME studies for the design of potent CCR2 antagonists for inflammation-driven diseases

In order to elucidate the essential structural features for CC chemokine receptor 2 (CCR2) antagonism, 3D-pharmacophore hypotheses were built based on a set of known compounds from the literature. The hypotheses were developed with the aid of HypoGen module within Discovery Studio 2.5 program. Multiple validation approaches provided the confidence in utilising the predictive pharmacophore models developed in this study. The most predictive pharmacophore model (Hypo1) was found to be statistically significant along with its ability to predict activities of the known CCR2 antagonists in the training and test set with high correlation coefficient. The best model was then used as a 3D search query in the virtual screening of chemical databases including ChemDiv and MiniMaybridge. Lipinskis rule of five and molecular docking studies were applied to the screened hits for retrieving potential lead compounds. Eight hits showed better in silico CCR2-binding affinities than the reported CCR2 antagonists, along with good absorption, distribution, metabolism and excretion profiles. The current 3D-quantitative structure–activity relationship (QSAR) pharmacophore modelling and molecular docking studies attempt to elucidate QSAR for CCR2 antagonism and identify novel potent CCR2 antagonist scaffolds.

In silico study on indole derivatives as anti HIV-1 agents: a combined docking, molecular dynamics and 3D-QSAR study

The HIV-1 envelope glycoprotein gp120 plays a vital role in the entry of virus into the host cells and is a potential antiviral drug target. Recently, indole derivatives have been reported to inhibit HIV-1 through binding to gp120, and this prevents gp120 and CD4 interaction to inhibit the infectivity of HIV-1. In this work, molecular docking, molecular dynamics (MD) and three-dimensional quantitative structure–activity relationship studies were carried out. Molecular docking studies of the most active and the least active compounds were performed to identify important residues in the binding pocket. We refined the docked poses by MD simulations which resulted in conformational changes. After equilibration, the structure of the ligand and receptor complex was stable. Therefore, we just took the last snapshot as the representative binding pose for this study. This pose for the most active inhibitor was used as a template for receptor-based alignment which was subsequently used for comparative molecular field analysis. Resultant 3D contour maps suggested smaller substituents are desirable at the 7-position of indole ring to avoid steric interactions with Ser375, Phe382 and Tyr384 residues in the active site. These results can be exploited to develop potential leads and for structure-based drug design of novel HIV-1 inhibitors.

Docking-based 3D-QSAR study of pyridyl aminothiazole derivatives as checkpoint kinase 1 inhibitors

Checkpoint kinase 1 (Chk1) is a promising target for the design of novel anticancer agents. In the present work, molecular docking simulations and three-dimensional quantitative structure–activity relationship (3D-QSAR) studies were performed on pyridyl aminothiazole derivatives as Chk1 inhibitors. AutoDock was used to determine the probable binding conformations of all the compounds inside the active site of Chk1. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models were developed based on the docking conformations and alignments. The CoMFA model produced statistically significant results with a cross-validated correlation coefficient (q2) of 0.608 and a coefficient of determination (r2) of 0.972. The reliable CoMSIA model with q2 of 0.662 and r2 of 0.970 was obtained from the combination of steric, electrostatic and hydrogen bond acceptor fields. The predictive power of the models were assessed using an external test set of 14 compounds and showed reasonable external predictabilities (r2pred) of 0.668 and 0.641 for CoMFA and CoMSIA models, respectively. The models were further evaluated by leave-ten-out cross-validation, bootstrapping and progressive scrambling analyses. The study provides valuable information about the key structural elements that are required in the rational design of potential drug candidates of this class of Chk1 inhibitors.

In silico study of 1-(4-Phenylpiperazin-1-yl)-2-(1H-pyrazol-1-yl) ethanones derivatives as CCR1 antagonist: Homology modeling, docking and 3D-QSAR approach

C-C chemokine receptor type 1 (CCR1) is a chemokine receptor with seven transmembrane helices and it belongs to the G-Protein Coupled receptor (GPCR) family. It plays an important role in rheumatoid arthritis, organ transplant rejection, Alzheimers disease and also causes inflammation. Because of its role in disease processes, CCR1 is considered to be an important drug target. In the present study, we have performed three dimensional Quantitative Structure activity relationship (3D-QSAR) studies on a series of 1-(4-Phenylpiperazin-1-yl)-2-(1H-pyrazol-1-yl) ethanone derivatives targeting CCR1. Homology modeling of CCR1 was performed based on a template structure (4EA3) which has a high sequence identity and resolution. The highest active molecule was docked into this model. Ligand-based and Receptor-based quantitative structure-activity relationship (QSAR) study was performed and CoMFA models with reasonable statistics was developed for both ligand-based (q(2)=0.606; r(2)=0.968) and receptor-guided (q(2)=0.640; r(2)=0.932) alignment methods. Contour map analyses identified favorable regions for high affinity binding. The docking results highlighted the important active site residues. Tyr113 was found to interact with the ligand through hydrogen bonding. This residue has been considered responsible for anchoring ligands inside the active site. Our results could also be helpful to understand the inhibitory mechanism of 1-(4-Phenylpiperazin-1-yl)-2-(1H-pyrazol-1-yl) ethanone derivatives thereby to design more effective ligands in the future.

In silico characterization of binding mode of CCR8 inhibitor: homology modeling, docking and membrane based MD simulation study

Human CC-chemokine receptor 8 (CCR8) is a crucial drug target in asthma that belongs to G-protein-coupled receptor superfamily, which is characterized by seven transmembrane helices. To date, there is no X-ray crystal structure available for CCR8; this hampers active research on the target. Molecular basis of interaction mechanism of antagonist with CCR8 remains unclear. In order to provide binding site information and stable binding mode, we performed modeling, docking and molecular dynamics (MD) simulation of CCR8. Docking study of biaryl-ether-piperidine derivative (13C) was performed inside predefined CCR8 binding site to get the representative conformation of 13C. Further, MD simulations of receptor and complex (13C-CCR8) inside dipalmitoylphosphatidylcholine lipid bilayers were performed to explore the effect of lipids. Results analyses showed that the Gln91, Tyr94, Cys106, Val109, Tyr113, Cys183, Tyr184, Ser185, Lys195, Thr198, Asn199, Met202, Phe254, and Glu286 were conserved in both docking and MD simulations. This indicated possible role of these residues in CCR8 antagonism. However, experimental mutational studies on these identified residues could be effective to confirm their importance in CCR8 antagonism. Furthermore, calculated Coulombic interactions represented the crucial roles of Glu286, Lys195, and Tyr113 in CCR8 antagonism. Important residues identified in this study overlap with the previous non-peptide agonist (LMD-009) binding site. Though, the non-peptide agonist and currently studied inhibitor (13C) share common substructure, but they differ in their effects on CCR8. So, to get more insight into their agonist and antagonist effects, further side-by-side experimental studies on both agonist (LMD-009) and antagonist (13C) are suggested.

Receptor-guided 3D-QSAR studies, molecular dynamics simulation and free energy calculations of Btk kinase inhibitors

BackgroundBruton tyrosine kinase (Btk) plays an important role in B-cell development, differentiation, and signaling. It is also found be in involved in male immunodeficiency disease such as X-linked agammaglobulinemia (XLA). Btk is considered as a potential therapeutic target for treating autoimmune diseases and hematological malignancies.ResultsIn this work, a combined molecular modeling study was performed on a series of thieno [3,2-c] pyridine-4-amine derivatives as Btk inhibitors. Receptor-guided COMFA (q2 = 0.574, NOC = 3, r2 = 0.924) and COMSIA (q2 = 0.646, NOC = 6, r2 = 0.971) models were generated based on the docked conformation of the most active compound 26. All the developed models were tested for robustness using various validation techniques. Furthermore, a 5-ns molecular dynamics (MD) simulation and binding free energy calculations were carried out to determine the binding modes of the inhibitors and to identify crucial interacting residues. The rationality and stability of molecular docking and 3D-QSAR results were validated by MD simulation. The binding free energies calculated by the MM/PBSA method showed the importance of the van der Waals interaction.ConclusionsA good correlation between the MD results, docking studies, and the contour map analysis were observed. The study has identified the key amino acid residues in Btk binding pocket. The results from this study can provide some insights into the development of potent, novel Btk inhibitors.

3D-QSAR study of tetrahydro-3H-imidazo[4,5-c]pyridine derivatives as VEGFR-2 kinase inhibitors using various charge schemes

Vascular endothelial growth factor-2 receptor (VEGFR-2) kinase is a promising target for the development of novel anticancer drugs. Three-dimensional quantitative structure–activity relationship (3D-QSAR) study was performed on a series of tetrahydro-3H-imidazo[4,5-c]pyridine derivatives to understand the structural basis for VEGFR-2 inhibitory activity. Several 3D-QSAR models were developed using various partial atomic charge schemes. Comparative molecular field analysis (CoMFA) and Comparative molecular similarity indices analysis (CoMSIA) methods were employed to derive these models. The CoMFA models performed better than the CoMSIA models. The reliable CoMFA model was obtained with the Gasteiger–Marsili charge scheme. The model produced statistically significant results with a cross-validated correlation coefficient (q2) of 0.635 and a coefficient of determination (r2) of 0.930. The model showed reasonable predictive power with predictive correlation coefficient (

Determination of structural requirements of Mer kinase inhibitors and binding interaction analysis using in silico approaches

{\text{r}}_{\text{pred}}^{2}