Homology modeling, molecular docking and molecular dynamics (MD) simulation methods were used to build a reliable model for A1AR (as one of the G protein-coupled receptors—GPCRs) and to explore the structural features and binding mechanism of ligands to this receptor. A model of A1AR was built and inserted in a hydrated lipid bilayer, and 20-ns MD simulation was performed to examine the stability of the best model. In this study, RG-14718 as the best A1AR agonist and bamifylline as a selective antagonist of A1AR have been docked into the active site of the A1AR. After docking, two 20-ns MD simulation was performed on the A1AR–ligand complex to explore effects of the presence of lipid membrane in the vicinity of the A1AR–ligand complex. At the end of the MD simulation, a change in the position and orientation of the ligand in the binding site was observed. This important observation indicated that the application of MD simulation after docking of ligands is useful. Thr270, His278 and Asn70 were crucial residues for hydrogen bonds with these ligands. Phe171, Glu172, Tyr271 and Ile274 were determined to be involved in ligand–receptor binding. The results obtained are in good agreement with most of the site-directed mutagenesis data reported by others. Our results show that molecular modeling and rational drug design for adenosine targets is a possible approach.

Since the introduction of the first protein side-chain rotamer library (RL) almost half a century ago, RLs have been components of many programs and algorithms in structural bioinformatics. Based on the dependence of side-chain dihedral angles on the local backbone, three types of RLs have been identified: backbone-independent, secondary-structure-dependent and backbone-dependent. In all previous studies, the effect of sequence specificity on side-chain conformational preferences was neglected. In the effort to develop a new class of RLs, we considered that the side-chain conformation of the central residue in each triplet on a protein backbone depends on the sequence of the triplet; therefore, we developed a sequence-dependent rotamer library (SDRL). To accomplish this, 400 possible triplet sequences for 18 natural amino acids as the central residue, which corresponds to 7200 triplet sequences in total, were considered. Searching the set of 11 546 selected PDB entries for the 7200 triplet sequences resulted in 2 364 541 instances occurring for 18 amino acids. Our results show that Leu and Val experience minimal impact from the adjacent residues in adopting side-chain conformations. Cys, Ile, Trp, His, Asp, Met, Glu, Gln, Arg and Lys, on the other hand, adopt their side-chain conformations mostly based on the adjacent residues on the backbone. The remaining residue types were moderately dependent on the adjacent residues. Using the new library, side-chain repacking algorithms can find preferred conformations of each residue more easily than with other backbone-independent RLs.

Partial least squares (PLS) method as ligand-based method was applied for building quantitative structure–activity relationships (QSAR) regression model to predict the inhibitory activity of some A2B antagonists. The accuracy and predictability of the developed model were evaluated by several validation methods using external and internal test sets and also the criteria recommended by Tropsha and Roy were met. The result of the PLS model had a high statistical quality (R 2 = 0.936 and Q 2 = 0.867) for predicting the activity of the compounds. Evaluation of a test set of seven compounds with the developed PLS model revealed that this model is reliable and has a good predictability. Because of high correlation between the predicted and experimental values of activity, PLS model proved to be a highly predictive QSAR approach. Also, the reliability of the model was assessed through docking for the selected antagonists as structure-based method. A potential binding site of A2BAR was verified according to the previous studies of site-directed mutagenesis. Phe173, Glu174, His251, Asn254, Lys269, Ile276, and His 280 were determined to be involved in ligand–receptor interactions.

Anaphase promoting complex (APC) controls cell cycle and chromosome segregation. The APC activation occurs after binding of co-activators, cdh1 and cdc20. Cdh1 plays a role in cancer pathogenesis and is known as a potential drug target. The main aim of this study was prediction of 3D structure of cdh1 and designing the inhibitory compounds based on the structural model. First, 3D structure of cdh1 was predicted by means of homology modelling and molecular dynamics tools, MODELLER and Gromacs package, respectively. Then, inhibitory compounds were designed using virtual screening and molecular docking by means AutoDock package. The overall structure of cdh1 is propeller like and each DW40 repeat contains four antiparallel beta-sheets. Moreover, binding pocket of the inhibitory compounds was determined. The results might be helpful in finding a suitable cdh1 inhibitor for the treatment of cancer.

Accurate protein function prediction is an important subject in bioinformatics, especially where sequentially and structurally similar proteins have different functions. Malate dehydrogenase and L-lactate dehydrogenase are two evolutionary related enzymes, which exist in a wide variety of organisms. These enzymes are sequentially and structurally similar and share common active site residues, spatial patterns and molecular mechanisms. Here, we study various features of the active site cavity of 229 PDB chain entries and try to classify them automatically by various classifiers including the support vector machine, k nearest neighbour and random forest methods. The results show that the support vector machine yields the highest predictive performance among mentioned classifiers. Despite very close and conserved patterns among Malate dehydrogenases and L-lactate dehydrogenases, the SVM predicts the function efficiently and achieves 0.973 Matthew’s correlation coefficient and 0.987 F-score. The same approach can be used in other enzyme families for automatic discrimination between homologous enzymes with common active site elements, however, acting on different substrates.

PELE, Protein Energy Landscape Exploration, our novel technology based on protein structure prediction algorithms and a Monte Carlo sampling, is capable of modelling the all-atom protein–ligand dynamical interactions in an efficient and fast manner, with two orders of magnitude reduced computational cost when compared with traditional molecular dynamics techniques. PELE’s heuristic approach generates trial moves based on protein and ligand perturbations followed by side chain sampling and global/local minimization. The collection of accepted steps forms a stochastic trajectory. Furthermore, several processors may be run in parallel towards a collective goal or defining several independent trajectories; the whole procedure has been parallelized using the Message Passing Interface. Here, we introduce the PELE web server, designed to make the whole process of running simulations easier and more practical by minimizing input file demand, providing user-friendly interface and producing abstract outputs (e.g. interactive graphs and tables). The web server has been implemented in C++ using Wt (http://www.webtoolkit.eu) and MySQL (http://www.mysql.com). The PELE web server, accessible at http://pele.bsc.es, is free and open to all users with no login requirement.

A series of 16 novel 1,2,4-triazine derivatives bearing hydrazone moiety (7a–7p) have been designed, synthesized and evaluated for their activity to inhibit IL-1β and TNF-α production. All compounds are reported for the first time. The chemical structures of all compounds were confirmed by spectroscopic methods and elemental analyzes. Most of the synthesized compounds were proved to have potent anti-cytokine activity and low toxicity on PBMC and MCF-7 cell lines. Compounds 7f, 7k, 7l and 7j presented simultaneously good levels of inhibition of both cytokines. Moreover, compound 7l exhibited good anti-inflammatory effect in carrageenan-induced rat paw edema. The results of Western blotting demonstrated that the anti-cytokine potential of compound 7l is mainly mediated through the inhibition of p38 MAPK signaling pathway. Molecular docking was performed to position compound 7l into p38α binding site in order to explore the potential target. The information of this work might be helpful for the design and synthesis of novel scaffold toward the development of new therapeutic agent to fight against inflammatory diseases.

Due to the increasing number of protein structures with unknown function originated from structural genomics projects, protein function prediction has become an important subject in bioinformatics. Among diverse function prediction methods, exploring known 3D-motifs, which are associated with functional elements in unknown protein structures is one of the most biologically meaningful methods. Homologous enzymes inherit such motifs in their active sites from common ancestors. However, slight differences in the properties of these motifs, results in variation in the reactions and substrates of the enzymes. In this study, we examined the possibility of discriminating highly related active site patterns according to their EC-numbers by 3D-motifs. For each EC-number, the spatial arrangement of an active site, which has minimum average distance to other active sites with the same function, was selected as a representative 3D-motif. In order to characterize the motifs, various points in active site elements were tested. The results demonstrated the possibility of predicting full EC-number of enzymes by 3D-motifs. However, the discriminating power of 3D-motifs varies among different enzyme families and depends on selecting the appropriate points and features.

The inhibition of β secretase (BACE1) is potentially important approach to treatment of Alzheimer disease (AD). A novel series of 4-bromophenyl piperazine derivatives coupled to the phenylimino-2H-chromen-3-carboxamide scaffold were investigated as BACE1 inhibitors in this study. Docking study suggested that the phenyl-imino group of the scaffold establishes favorable π–π stacking interaction with side chain of Phe108 of flap pocket. Some of the docking proposed derivatives were synthesized and evaluated for BACE1 inhibitory activity using a FRET-based assay. High BACE1 inhibitory activities were observed from derivatives containing fused heteroaromtic groups attached through the aliphatic linkage to the N4-piperazine moiety, which may be attributed to the engagement of effective interactions with S1–S′1 sub-pocket residues. Of the most potent compounds, 9e displayed an IC50 value for BACE1 of 98 nM. Some of these derivatives demonstrated good inhibitory activity on Aβ production in N2a-APPswe cells at 5 and 10 μM. These compounds might be considered as promising BACE1 inhibitory agents that could lower Aβ production in AD.

A QSAR study of 74 derivatives of 1,3,8-substituted-9-deazaxanthines as potent and selective A2B adenosine receptor (A2BAR) antagonists is described. pKi of all the studied compounds were acquired by three linear and nonlinear methods namely stepwise multiple linear regression, partial least squares (PLS), and general regression neural networks (GRNN). The performances of developed models were tested by several external and internal validation methods and also the criteria recommended by Tropsha and Roy. Predictability and possible overfitting in the resulting models were examined by cross-validation. Results revealed the significant role of topological and geometrical descriptors in binding of the studied compounds to A2BAR. PLS and GRNN models had good statistical qualities with PLS showing better performance (R 2 = 0.863 and Q 2 = 0.817). Applicability domain of the models was also defined. The prediction results were in good agreement with the experimental data.

A computational procedure was performed on some indenopyrazole derivatives. Two important procedures in computational drug discovery, namely docking for modeling ligand-receptor interactions and quantitative structure activity relationships were employed. MIA-QSAR analysis of the studied derivatives produced a model with high predictability. The developed model was then used to evaluate the bioactivity of 54 proposed indenopyrazole derivatives. In order to confirm the obtained results through this ligand-based method, docking was performed on the selected compounds. An ADME–Tox evaluation was also carried out to search for more suitable compounds. Satisfactory bioactivities and ADME–Tox profiles for two of the compounds, namely 62 and S13, propose that further studies should be performed on such devoted chemical structures.