Thirumurthy Madhavan

Docking and 3D-QSAR (quantitative structure activity relationship) studies of flavones, the potent inhibitors of p-glycoprotein targeting the nucleotide binding domain

In order to explore the interactions between flavones and P-gp, in silico methodologies such as docking and 3D-QSAR were performed. CoMFA and CoMSIA analyses were done using ligand based and receptor guided alignment schemes. Validation statistics include leave-one-out cross-validated R(2) (q(2)), internal prediction parameter by progressive scrambling (Q(*2)), external prediction with test set. They show that models derived from this study are quite robust. Ligand based CoMFA (q(2) = 0.747, Q(*2) = 0.639, r(pred)(2)=0.802) and CoMSIA model (q(2) = 0.810, Q(*2) = 0.676, r(pred)(2)=0.785) were developed using atom by atom matching. Receptor guided CoMFA (q(2) = 0.712, Q(*2) = 0.497, r(pred)(2) = 0.841) and for CoMSIA (q(2) = 0.805, Q(*2) = 0.589, r(pred)(2) = 0.937) models were developed by docking of highly active flavone into the proposed NBD (nucleotide binding domain) of P-gp. The 3D-QSAR models generated here predicted that hydrophobic and steric parameters are important for activity toward P-gp. Our studies indicate the important amino acid in NBD crucial for binding in accordance with the previous results. This site forms a hydrophobic site. Since flavonoids have potential without toxicity, we propose to inspect this hydrophobic site including Asn1043 and Asp1049 should be considered for future inhibitor design.

Binding Site Analysis of CCR2 Through In Silico Methodologies: Docking, CoMFA, and CoMSIA

Chemokine receptor (CCR2) is a G protein‐coupled receptor that contains seven transmembrane domains. CCR2 is targeted for diseases like arthritis, multiple sclerosis, vascular disease, obesity and type 2 diabetes. Herein, we report on a binding site analysis of CCR2 through docking and three‐dimensional quantitative structure–activity relationship (3D‐QSAR). The docking study was performed with modeled receptor (CCR2) using β2‐andrenergic receptor structure as a template. Comparative molecular field analysis (CoMFA)‐ and comparative molecular similarity indices analysis (CoMSIA)‐based 3D‐QSAR models were developed using two different schemes: ligand‐based (CoMFA; q2 = 0.820, r2 = 0.966,  = 0.854 and CoMSIA; q2 = 0.762, r2 = 0.846,  = 0.684) and receptor‐guided (CoMFA; q2 = 0.753, r2 = 0.962,  = 0.786, CoMSIA; q2 = 0.750, r2 = 0.800,  = 0.797) methods. 3D‐QSAR analysis revealed the contribution of electrostatic and hydrogen bond donor parameters to the inhibitory activity. Contour maps suggested that bulky substitutions on the para position of R1 substituted phenyl ring, electronegative and donor substitutions on meta (5′) and ortho (2′) position of R2 substituted phenyl ring were favorable for activity. The results correlate well with previous results and newly report additional residues that may be crucial in CCR2 antagonism.

3D-QSAR studies of JNK1 inhibitors utilizing various alignment methods.

We report our three‐dimensional quantitative structure activity relationship (3D‐QSAR) studies of the series of anilinopyrimidine derivatives of JNK1 inhibitors. The comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were applied using different alignment methods. The ligand‐based atom‐by‐atom matching alignment has produced better values for CoMFA (q2 = 0.646 and r2 = 0.983), while in CoMSIA it has achieved only lower statistical values. The pharmacophore‐based model has produced (q2 = 0.568, r2 = 0.938) and (q2 = 0.670, r2 = 0.982) for CoMFA and CoMSIA models, respectively. As the model was based on the receptor‐guided alignment, all the compounds were optimized within the receptor, resulting in q2 = 0.605 and r2 = 0.944 for CoMFA, and q2 = 0.587 and r2 = 0.863 for CoMSIA. Molecular Dynamic simulation studies suggested that the generated models were consistent with the low‐energy protein ligand conformation. The CoMFA and CoMSIA contour maps indicated that the substitutions of the electropositive groups in the phenyl ring, and an addition of hydrophobic groups in the pyrimidine ring, are important to enhance the activity of this series. Moreover, the virtual screening analysis against NCI database yields potentials hits, and the results obtained would be useful to synthesize selective and highly potent c‐Jun N‐terminal kinase 1 analogs.

In silico quantitative structure-activity relationship studies on P-gp modulators of tetrahydroisoquinoline-ethyl-phenylamine series.

BackgroundMultidrug resistance (MDR) is a major obstacle in cancer chemotherapy. The drug efflux by a transport protein is the main reason for MDR. In humans, MDR mainly occurs when the ATP-binding cassette (ABC) family of proteins is overexpressed simultaneously. P-glycoprotein (P-gp) is most commonly associated with human MDR; it utilizes energy from adenosine triphosphate (ATP) to transport a number of substrates out of cells against concentration gradients. By the active transport of substrates against concentration gradients, intracellular concentrations of substrates are decreased. This leads to the cause of failure in cancer chemotherapy.ResultsHerein, we report Topomer CoMFA (Comparative Molecular Field Analysis) and HQSAR (Hologram Quantitative Structure Activity Relationship) models for third generation MDR modulators. The Topomer CoMFA model showed good correlation between the actual and predicted values for training set molecules. The developed model showed cross validated correlation coefficient (q2) = 0.536 and non-cross validated correlation coefficient (r2) = 0.975 with eight components. The best HQSAR model (q2 = 0.777, r2 = 0.956) with 5-8 atom counts was used to predict the activity of test set compounds. Both models were validated using test set compounds, and gave a good predictive values of 0.604 and 0.730.ConclusionsThe contour map near R1 indicates that substitution of a bulkier and polar group to the ortho position of the benzene ring enhances the inhibitory effect. This explains why compounds with a nitro group have good inhibitory potency. Molecular fragment analyses shed light on some essential structural and topological features of third generation MDR modulators. Fragments analysis showed that the presence of tertiary nitrogen, a central phenyl ring and an aromatic dimethoxy group contributed to the inhibitory effect. Based on contour map information and fragment information, five new molecules with variable R1 substituents were designed. The activity of these designed molecules was predicted by the Topomer CoMFA and HQSAR models. The novel compounds showed higher potency than existing compounds.

Computational Analysis of CRTh2 receptor antagonist

CRTh2 receptor is an important mediator of inflammatory effects and has attracted much attention as a therapeutic target for the treatment of conditions such as asthma, COPD, allergic rhinitis and atopic dermatitis. In pursuit of better CRTh2 receptor antagonist agents, 3D-QSAR studies were performed on a series of 2-(2-(benzylthio)-1H-benzo[d]imidazol-1-yl) acetic acids. There is no crystal structure information available on this protein; hence in this work, ligand-based comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed by atom by atom matching alignment using systematic search and simulated annealing methods. The 3D-QSAR models were generated with 10 different combinations of test and training set molecules, since the robustness and predictive ability of the model is very important. We have generated 20 models for CoMFA and 100 models for CoMSIA based on two different alignments. Each model was validated with statistical cut off values such as q(2)>0.4, r(2)>0.5 and r(2)pred>0.5. Based on better q(2) and r(2)pred values, the best predictions were obtained for the CoMFA (model 5 q(2)=0.488, r(2)pred=0.732), and CoMSIA (model 45 q(2)=0.525, r(2)pred=0.883) from systematic search conformation alignment. The high correlation between the cross-validated/predicted and experimental activities of a test set revealed that the CoMFA and CoMSIA models were robust. Statistical parameters from the generated QSAR models indicated the data is well fitted and have high predictive ability. The generated models suggest that steric, electrostatic, hydrophobic, H-bond donor and acceptor parameters are important for activity. Our study serves as a guide for further experimental investigations on the synthesis of new CRTh2 antagonist.

Prognostic Significance of Cyclooxygenase-2 and Response to Chemotherapy in Invasive Ductal Breast Carcinoma Patients by Real Time Surface Plasmon Resonance Analysis

Cyclooxygenase-2 (COX-2), an inducible enzyme, has been implicated in the progression and angiogenesis of breast cancer. The aim of the study is to quantify the concentration of COX-2 and its association with clinico-pathological parameters and response to treatment in patients with invasive ductal carcinoma receiving both neo-adjuvant and adjuvant chemotherapy. The level of COX-2 was estimated using a novel biosensor-based surface plasmon resonance technique in serum of 84 patients with breast cancer (48 patients of neo-adjuvant chemotherapy and 36 patients of adjuvant chemotherapy) and 40 age- and gender-matched normal individuals. A significant increase in COX-2 level was observed in patients compared with normal individuals (p>0.0001). The COX-2 level in serum was found to be significantly higher in patients with lymph node involvement (p<0.0061). 68% (33/48) of the patients receiving neo-adjuvant chemotherapy showed significantly (p<0.0025) reduced COX-2 levels. This study shows significant decrease of COX-2 level in patients with breast cancer treated with both neo-adjuvant and adjuvant chemotherapy. Estimation of COX-2 level in serum may serve as a tumor biomarker in patients with breast cancer.

3D-QSAR studies on indole and 7-azoindole derivatives as ROCK-2 inhibitors: An integrative computational approach

Rho Kinases (ROCK) has been found to regulate a wide range of fundamental cell functions such as contraction, motility, proliferation, and apoptosis. Recent experiments have defined new functions of ROCKs in cells, including centrosome positioning and cell-size regulation, which might contribute to various physiological and pathological states. In this study, we have performed pharmacophore modeling and 3D QSAR studies on a series of 36 indoles and 7-azoindoles derivatives as ROCK2 inhibitors to elucidate the structural variations with their inhibitory activities. Ligand based CoMFA and CoMSIA models were generated based on three different alignment methods such as systematic search, simulated annealing and pharmacophore. A total of 15 CoMFA models and 27 CoMSIA were generated using different alignments. One model from each alignment is selected based on the statistical values. Contour maps of the selected models were compared, analysed and reported. The 3D QSAR study revealed that electro positive group linked to the methoxy-benzene ring position of the structure will enhance the biological activity and bulkier substitutions are preferred in the methyl dihydroindole region. Also, it is found that the hydrogen bond donor substituted at the R1 position enhances the inhibitory activity. In future, this study would give proper guidelines to further enhance the activity of novel inhibitors for ROCK2.

Molecular Modeling Study on Diazine Indole Acetic Acid Derivatives for CRTH2 Inhibitory Activity

In the present work, molecular modeling studies have been reported on a series of diazine indole acetic acid derivatives to analyze the structure-activity relationship studies of CRTH2 using fragment (Topomer CoMFA and HQSAR) and field (CoMFA and CoMSIA) based QSAR methods. Twenty-six compounds were used as a training set to establish the model, and six compounds were used as a test set to validate the model. The generated models exhibited good statistical results such as correlation coefficient (r2) and the cross-validated correlation coefficient (q2). Topomer CoMFA analysis yielded the q2 of 0.610 and r2 of 0.981. HQSAR model generated using bond and connectivity as fragment distinction and 3-6 as fragment size has the q2 value of 0.707 and conventional r2 value of 0.892 with five components. CoMFA model was assessed by cross-validated q2 value of 0.543 and r2 value of 0.901 with steric and electrostatic fields. CoMSIA model generated using steric, hydrophobic and donor fields with q2 value of 0.550 and r2 value of 0.888 was found to be the optimal model among the various models generated. The contour maps were generated to analyze the important structural features that regulate their inhibitory potency. From the result of contour maps we have suggested the critical sites for chemical modification which will be useful in designing potent compounds with improved activity.

Computational identification of JAK2 inhibitors: a combined pharmacophore mapping and molecular docking approach

Abstract The Janus-associated kinase 2 (JAK2) V617F mutation is believed to play a critical role in the pathogenesis of polycythemia vera, essential thrombocythemia, and idiopathic myelofibrosis. The discovery of activating mutations associated with inhibition of cascade of events mediated by JAK2 target became an attractive approach for the treatment of myeloproliferative disorder. In this study, we performed a ligand-based pharmacophore modeling to explore the important chemical features of JAK2 inhibitors. The top ten hypotheses were generated based on 47 known inhibitors of JAK2 using PHASE module of Schrodinger software. The best pharmacophore hypothesis was found to be AADDR.212 which consists of two acceptors, two donors and one ring aromatic group. The selected model was validated by survival score, selectivity, and GH score. Two types of validation studies were done which includes potency validation by virtual screening against set of decoys, and selectivity validation by screening against set of inhibitors of JAK1, JAK2, JAK3, and Tyk2 (all tyrosine kinase family proteins). The selected model was utilized as a 3D query to screen against ZINC natural and chemical database, and subsequently the screened compounds were filtered by applying the Lipinski’s rule of five, ADME properties and molecular docking. Finally, fifteen compounds were obtained as novel virtual hits to inhibit the JAK2 enzyme.

Towards a better understanding of the interaction between Somatostatin receptor 2 (SSTR2) and its ligands: a structural characterization study using molecular dynamics and conceptual Density Functional Theory (DFT)

Abstract This study is a part of the extensive research intending to provide the structural insights on somatostatin and its receptor. Herein, we have studied the structural complexity involved in the binding of somatostatin receptor 2 (SSTR2) with its agonists and antagonist. A 3D QSAR study based on comparative molecular field analysis and comparative molecular similarity analysis (CoMSIA) discerned that a SSTR2 ligand with electronegative, less-bulkier, and hydrogen atom donating/accepting substitutions is important for their biological activity. A conceptual density functional theory (DFT) study was followed to study the chemical behavior of the ligands based on the molecular descriptors derived using the Fukui’s molecular orbital theory. We have performed molecular dynamics simulations of receptor–ligand complexes for 100 ns to analyze the dynamic stability of the backbone Cα atoms of the receptor and strength and approachability of the receptor–ligand complex. The findings of this study could be efficacious in the further studies understanding intricate structural features of the somatostatin receptors and in discovering novel subtype-specific ligands with higher affinity. Communicated by Ramaswamy H. Sarma