Cellular Src (c-Src) kinases play a critical role in cell adhesion, proliferation, angiogenesis and cancer. Ligand-based pharmacophore models, used to identify the critical chemical features of c-Src inhibitors, were generated and validated by training, test and decoy sets, respectively. Best pharmacophore model, Hypo1, consists of four features such as HBA, HBD, Hy-Ar and RA. Hypo1 was used in virtual screening of the chemical databases such as Maybridge, Chembridge and NCI. The sorted compounds by Hypo1 were further reduced by applying drug-like properties and ADMET. Totally, 85 compounds which showed the good drug-like properties were selected from three databases and subjected to molecular docking for refinement of the retrieved hits by analysing the suitable orientation of the compounds in the active site of c-Src. Finally, 18 compounds were selected based on consensus scoring and hydrogen bond interactions with critical amino acids such as Met341, Thr338, Glu339 or Asp404. In addition, the Bayesian model was generated from the training set to find suitable fragments for inhibition of the c-Src function. Based on the above finding, we suggested that the Hypo1 and the good fragments from the Bayesian model will be helpful to select the compounds from various databases to identify the novel and potent c-Src inhibitor.

Stilbene urea derivatives as a novel and competitive class of non-glycosidic α-glucosidase inhibitors are effective for the treatment of type II diabetes and obesity. The main purposes of our molecular modeling study are to explore the most suitable binding poses of stilbene derivatives with analyzing the binding affinity differences and finally to develop a pharmacophore model which would represents critical features responsible for α-glucosidase inhibitory activity. Three-dimensional structure of S. cerevisiae α-glucosidase was built by homology modeling method and the structure was used for the molecular docking study to find out the initial binding mode of compound 12, which is the most highly active one. The initial structure was subjected to molecular dynamics (MD) simulations for protein structure adjustment at compound 12-bound state. Based on the adjusted conformation, the more reasonable binding modes of the stilbene urea derivatives were obtained from molecular docking and MD simulations. The binding mode of the derivatives was validated by correlation analysis between experimental Ki value and interaction energy. Our results revealed that the binding modes of the potent inhibitors were engaged with important hydrogen bond, hydrophobic, and π-interactions. With the validated compound 12-bound structure obtained from combining approach of docking and MD simulation, a proper four featured pharmacophore model was generated. It was also validated by comparison of fit values with the Ki values. Thus, these results will be helpful for understanding the relationship between binding mode and bioactivity and for designing better inhibitors from stilbene derivatives.

TWIK (tandem-pore domain weak inward rectifying K(+))-related spinal cord K(+) channel, TRESK, a member of the tandem-pore domain K(+) channel family, is the most recently cloned K(2P) channel. TRESK is highly expressed in dorsal root ganglion neuron, a pain sensing neuron, which is a target for analgesics. In this study, a reliable 3D structure for transmembrane (TM) region of mouse TRESK (mTRESK) was constructed, and then the reasonable blocker binding mode of the protein was investigated. The 3D structure of the mTRESK built by homology modeling method was validated with recommend value of stereochemical quality. Based on the validated structure, K(+) channel blocker-bound conformation was obtained by molecular docking and 5ns MD simulation with DPPC lipid bilayer. Our docking study provides the plausible binding mode of known blockers with key interacting residues, especially, F156 and F364. Finally, these modeling results were verified by experimental study with mutation from phenylalanine to alanine (F156A, F364A and F156A/F364A) at the TM2 and TM4. This is the first modeling study for TRESK that can provide structural information of the protein including ligand binding information. These results can be useful in structure based drug design for finding new blockers of the TRESK as potential therapeutic target of pain treatment.

The sonic hedgehog (Shh) signaling pathway is necessary for a variety of development and differentiation during embryogenesis as well as maintenance and renascence of diverse adult tissues. However, an abnormal activation of the signaling pathway is related to various cancers. In this pathway, the Shh signaling transduction is facilitated by binding of Shh to its receptor protein, Ptch. In this study, we modeled the 3D structure of functionally important key loop peptides of Ptch based on homologous proteins. Using this loop model, the molecular interactions between the structural components present in the pseudo-active site of Shh and key residues of Ptch was investigated in atomic level through molecular dynamics (MD) simulations. For the purpose of developing inhibitor candidates of the Shh signaling pathway, the Shh pseudo-active site of this interface region was selected as a target to block the direct binding between Shh and Ptch. Two different structure-based pharmacophore models were generated considering the key loop of Ptch and known inhibitor-induced conformational changes of the Shh through MD simulations. Finally two hit compounds were retrieved through a series of virtual screening combined with molecular docking simulations and we propose two hit compounds as potential inhibitory lead candidates to block the Shh signaling pathway based on their strong interactions to receptor or inhibitor induced conformations of the Shh.

2-Cys peroxiredoxins (Prxs) play important roles in the protection of chloroplast proteins from oxidative damage. Arabidopsis NADPH-dependent thioredoxin reductase isotype C (AtNTRC) was identified as efficient electron donor for chloroplastic 2-Cys Prx-A. There are three isotypes (A, B, and C) of thioredoxin reductase (TrxR) in Arabidopsis. AtNTRA contains only TrxR domain, but AtNTRC consists of N-terminal TrxR and C-terminal thioredoxin (Trx) domains. AtNTRC has various oligomer structures, and Trx domain is important for chaperone activity. Our previous experimental study has reported that the hybrid protein (AtNTRA-(Trx-D)), which was a fusion of AtNTRA and Trx domain from AtNTRC, has formed variety of structures and shown strong chaperone activity. But, electron transfer mechanism was not detected at all. To find out the reason of this problem with structural basis, we performed two different molecular dynamics (MD) simulations on AtNTRC and AtNTRA-(Trx-D) proteins with same cofactors such as NADPH and flavin adenine dinucleotide (FAD) for 50 ns. Structural difference has found from superimposition of two structures that were taken relatively close to average structure. The main reason that AtNTRA-(Trx-D) cannot transfer the electron from TrxR domain to Trx domain is due to the difference of key catalytic residues in active site. The long distance between TrxR C153 and disulfide bond of Trx C387-C390 has been observed in AtNTRA-(Trx-D) because of following reasons: i) unstable and unfavorable interaction of the linker region, ii) shifted Trx domain, and iii) different or weak interface interaction of Trx domains. This study is one of the good examples for understanding the relationship between structure formation and reaction activity in hybrid protein. In addition, this study would be helpful for further study on the mechanism of electron transfer reaction in NADPH-dependent thioredoxin reductase proteins.

Grb2 is an adapter protein involved in the signal transduction and cell communication. The Grb2 is responsible for initiation of kinase signaling by Ras activation which leads to the modification in transcription. Ligand based pharmacophore approach was applied to built the suitable pharmacophore model for Grb2. The best pharmacophore model was selected based on the statistical values and then validated by Fischer's randomization method and test set. Hypo1 was selected as a best pharmacophore model based on its statistical values like high cost difference (182.22), lowest RMSD (1.273), and total cost (80.68). It contains four chemical features, one hydrogen bond acceptor (HBA), two hydrophobic (HY), and one ring aromatic (RA). Fischer's randomization results also shows that Hypo1 have a 95% significant level. The correlation coefficient of test set was 0.97 which was close to the training set value (0.94). Thus Hypo1 was used for virtual screening to find the potent inhibitors from various chemical databases. The screened compounds were filtered by Lipinski's rule of five, ADMET and subjected to molecular docking studies. Totally, 11 compounds were selected as a best potent leads from docking studies based on the consensus scoring function and critical interactions with the amino acids in Grb2 active site.

To provide a new idea for drug design, a computational investigation is performed on chymase and its novel 1,4-diazepane-2,5-diones inhibitors that explores the crucial molecular features contributing to binding specificity. Molecular docking studies of inhibitors within the active site of chymase were carried out to rationalize the inhibitory properties of these compounds and understand their inhibition mechanism. The density functional theory method was used to optimize molecular structures with the subsequent analysis of highest occupied molecular orbital, lowest unoccupied molecular orbital, and molecular electrostatic potential maps, which revealed that negative potentials near 1,4-diazepane-2,5-diones ring are essential for effective binding of inhibitors at active site of enzyme. The Bayesian model with receiver operating curve statistic of 0.82 also identified arylsulfonyl and aminocarbonyl as the molecular features favoring and not favoring inhibition of chymase, respectively. Moreover, genetic function approximation was applied to construct 3D quantitative structureactivity relationships models. Two models (genetic function approximation model 1 r2 = 0.812 and genetic function approximation model 2 r2 = 0.783) performed better in terms of correlation coefficients and cross-validation analysis. In general, this study is used as example to illustrate how combinational use of 2D/3D quantitative structureactivity relationships modeling techniques, molecular docking, frontier molecular orbital density fields (highest occupied molecular orbital and lowest unoccupied molecular orbital), and molecular electrostatic potential analysis may be useful to gain an insight into the binding mechanism between enzyme and its inhibitors.

Peroxisome proliferator-activated receptors (PPARs) are members of nuclear receptors and their activation induces regulation of fatty acid storage and glucose metabolism. Therefore, the PPARγ is a major target for the treatment of type 2 diabetes mellitus. In order to generate pharmacophore model, 1080 known agonists database was constructed and a training set was selected. The Hypo7, selected from 10 hypotheses, contains four features: three hydrogen-bond acceptors (HBA) and one general hydrophobic (HY). This pharmacophore model was validated by using 862 test set compounds with a correlation coefficient of 0.903 between actual and estimated activity. Secondly, CatScramble method was used to verify the model. Hence, the validated Hypo7 was utilized for searching new lead compounds over 238,819 and 54,620 chemical structures in NCI and Maybridge database, respectively. Then the leads were selected by screening based on the pharmacophore model, predictive activity, and Lipinski’s rules. Candidates were obtained and subsequently the binding affinities to PPARγ were investigated by the molecular docking simulations. Finally the best two compounds were presented and would be useful to treat type 2 diabetes.

Human chymase is a very important target for the treatment of cardiovascular diseases. Using a series of theoretical methods like pharmacophore modeling, database screening, molecular docking and Density Functional Theory (DFT) calculations, an investigation for identification of novel chymase inhibitors, and to specify the key factors crucial for the binding and interaction between chymase and inhibitors is performed. A highly correlating (r = 0.942) pharmacophore model (Hypo1) with two hydrogen bond acceptors, and three hydrophobic aromatic features is generated. After successfully validating "Hypo1", it is further applied in database screening. Hit compounds are subjected to various drug-like filtrations and molecular docking studies. Finally, three structurally diverse compounds with high GOLD fitness scores and interactions with key active site amino acids are identified as potent chymase hits. Moreover, DFT study is performed which confirms very clear trends between electronic properties and inhibitory activity (IC(50)) data thus successfully validating "Hypo1" by DFT method. Therefore, this research exertion can be helpful in the development of new potent hits for chymase. In addition, the combinational use of docking, orbital energies and molecular electrostatic potential analysis is also demonstrated as a good endeavor to gain an insight into the interaction between chymase and inhibitors.

Sonic hedgehog (Shh) plays an important role in the activation of Shh signaling pathway that regulates preservation and rebirth of adult tissues. An abnormal activation of this pathway has been identified in hyperplasia and various tumorigenesis. Hence the inhibition of this pathway using a Shh inhibitor might be an efficient way to treat a wide range of malignancies. This study was done in order to develop a lead chemical candidate that has an inhibitory function in the Shh signaling pathway. We have generated common feature pharmacophore models using three-dimensional (3D) structural information of robotnikinin, an inhibitor of the Shh signaling pathway, and its analogs. These models have been validated with fit values of robotnikinin and its analogs, and the best model was used as a 3D structural query to screen chemical databases. The hit compounds resulted from the screening docked into a proposed binding site of the Shh named pseudo-active site. Molecular dynamics (MD) simulations were performed to investigate detailed binding modes and molecular interactions between the hit compounds and functional residues of the pseudo-active site. The results of the MD simulation analyses revealed that the hit compounds can bind the pseudo-active site with high affinity than robotnikinin. As a result of this study, a candidate inhibitor (GK 03795) was selected as a potential lead to be employed in future Shh inhibitor design.