Interleukin 6 and/or Interleukin 17A Modulate the OPG/RANKL System of MC3T3-E1 Murine Osteoblast Cell Line

Cytokines such as interleukin-6 (IL-6) and IL-17 which act as key regulators of the immune response have been identified to have a potential role in the bone remodeling mechanism. Receptor activator of NF-κB ligand (RANKL) has been shown to regulate osteoclast differentiation and function while the osteoprotegerin (OPG) blocks the binding of RANKL and inhibits the differentiation of osteoclasts, thus favoring osteogenesis. Alkaline phosphatase (ALP) on the other hand works as early mineralization indicator in bone regulation. The current study aims to determine the potential role of IL-6 and IL-17A in regulating the OPG/RANKL system of the murine osteoblast cell line (MC3T3-E1). Gene expression analysis showed significant up-regulation of OPG and ALP by all the treated groups (rIL-6, rIL-17A and rIL-6 + rIL-17A). In contrast, treatment of cells with rIL-6 and/or rIL-17A showed down-regulation of RANKL expression. Interestingly, the osteoblast cells treated with combinations of rIL-6 + rIL17A showed marked increased in OPG/RANKL ratio. Similar pattern of protein expression was observed in the osteoblasts treated with rIL-6 and/or rIL-17A as detected by western blotting and ELISA. These findings suggest a new mechanism of regulation by these cytokines on the expression of OPG and RANKL, which could promote osteogenesis and diminish osteoclastogenesis.     

COMPASS II: extended coverage for polymer and drug-like molecule databases

The COMPASS II force field has been developed by extending the coverage of the COMPASS force field (J Phys Chem B 102(38):7338–7364, 1998) to polymer and drug-like molecules found in popular databases. Using a fragmentation method to systematically construct small molecules that exhibit key functional groups found in these databases, parameters applicable to database compounds were efficiently obtained. Based on the same parameterization paradigm as used in the development of the COMPASS force field, new parameters were derived by a combination of fits to quantum mechanical data for valence parameters and experimental liquid and crystal data for nonbond parameters. To preserve the quality of the original COMPASS parameters, a quality assurance suite was used and updated to ensure that additional atom-types and parameters do not interfere with the existing ones. Validation against molecular properties, liquid and crystal densities, and enthalpies, demonstrates that the quality of COMPASS is preserved and the same quality of prediction is achieved for the additional coverage.     

Enhancing effect of metal coordination interaction on pnicogen bonding

The ternary complexes ML???PyZX₂???NH₃ (ML?=?CuCl, CuCN, AgCN, and AuCN; Z?=?P, As, and Sb; X?=?H and F) have been investigated with quantum chemical calculations. The results showed that the existence of coordination interaction has a prominent enhancing effect on the strength of pnicogen bonding. Even in ML???PySbH₂???NH₃, ML???PyAsF₂???NH₃, and ML???PySbF₂???NH₃, the pnicogen bond varies from a purely closed-shell interaction to a partially covalent interaction. The coordination interaction results in the enlargement of the σ-hole on the pnicogen atom and thus the enhancement of pnicogen bonding. In addition, the contribution of orbital interaction is also important.

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Graphical Abstract The pnicogen bond is strengthened by the coordinaiton bond

     

Improving the iterative Linear Interaction Energy approach using automated recognition of configurational transitions

Recently an iterative method was proposed to enhance the accuracy and efficiency of ligand-protein binding affinity prediction through linear interaction energy (LIE) theory. For ligand binding to flexible Cytochrome P450s (CYPs), this method was shown to decrease the root-mean-square error and standard deviation of error prediction by combining interaction energies of simulations starting from different conformations. Thereby, different parts of protein-ligand conformational space are sampled in parallel simulations. The iterative LIE framework relies on the assumption that separate simulations explore different local parts of phase space, and do not show transitions to other parts of configurational space that are already covered in parallel simulations. In this work, a method is proposed to (automatically) detect such transitions during the simulations that are performed to construct LIE models and to predict binding affinities. Using noise-canceling techniques and splines to fit time series of the raw data for the interaction energies, transitions during simulation between different parts of phase space are identified. Boolean selection criteria are then applied to determine which parts of the interaction energy trajectories are to be used as input for the LIE calculations. Here we show that this filtering approach benefits the predictive quality of our previous CYP 2D6-aryloxypropanolamine LIE model. In addition, an analysis is performed of the gain in computational efficiency that can be obtained from monitoring simulations using the proposed filtering method and by prematurely terminating simulations accordingly.     

How strong is the edge effect in the adsorption of anticancer drugs on a graphene cluster?

The adsorption of nucleobase-analog anticancer drugs (fluorouracil, thioguanine, and mercaptopurine) on a graphene flake (C₅₄H₁₈) was investigated by shifting the site at which adsorption occurs from one end of the sheet to the other end. The counterpoise-corrected M06-2X/cc-pVDZ binding energies revealed that the binding stability decreases in the sequence thioguanine?>?mercaptopurine?>?fluorouracil. We found that adsorption near the middle of the sheet is more favorable than adsorption near the edge due to the edge effect. This edge effect is stronger for the adsorption of thioguanine or mercaptopurine than for fluorouracil adsorption. However, the edge effect reduces the binding energy of the drug to the flake by only a small amount, <5 kcal/mol, depending on the adsorption site and the alignment of the drug at this site.     

Theoretical studies of the role of C-terminal cysteines in the process of <Emphasis Type="Italic">S</Emphasis>-nitrosylation of human Src kinases

Src tyrosine kinases are a family of non-receptor proteins that are responsible for the growth process, cellular proliferation, differentiation and survival. Lack of Src kinase control has been associated with the development of certain human cancers. This family of proteins is constituted of four domains, with SH1 being the kinase or catalytic domain. SH1 also presents three important regulatory sites. Two residues, Tyr416 and Tyr527, are responsible for important phosphorylation processes that lead to, respectively, activation and deactivation of these kinases. More recently, however, a set of four cysteine residues located near the C-terminus-Cys483, Cys487, Cys496 and Cys498-has been associated with the activation of the Src kinases through S-nitrosylation reactions. Particularly, the Cys498 has been specified as a fundamental residue when considering this regulatory mechanism. Aiming to understand the role of these four cysteines in S-nitrosylation, theoretical studies of electrostatic, steric and hydrophobic properties were performed with a sequence of 20 amino acids, enclosing the four cysteine residues under study, extracted from the PDB coordinates of the crystal obtained from the inactive state of Src kinase. Results indicate that Cys498 is buried deeply in the protein, in hydrophobic surroundings in which NO is more likely to suffer decomposition into the electrophilic intermediates known to be responsible for S-nitrosylation reactions. Electronic calculated properties, such as punctual atomic charges, electrostatic potentials and molecular orbital energy, also demonstrated the good nucleophilic potential of Cys498.

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Graphical Abstract Structure of Src kinase with zoomed area representing the 20 amino acids comprising the CC motif extracted from the whole protein structure. Right upper panel Electrostatic potential map, right lower panel hydrophilic map in anterior view

     

Molecular dynamics simulations of a lithium/sodium carbonate mixture

The diffusion and ionic conductivity of Li _x Na_1?x CO₃ salt mixtures were studied by means of Molecular Dynamics (MD) simulations, using the Janssen and Tissen model (Janssen and Tissen, Mol Simul 5:83–98; 1990). These salts have received particular attention due to their central role in fuel cells technology, and reliable numerical methods that could perform as important interpretative tool of experimental data are thus required but still lacking. The chosen computational model nicely reproduces the main structural behaviour of the pure Li₂CO₃, Na₂CO₃ and K₂CO₃ carbonates, but also of their Li/K and Li/Na mixtures. However, it fails to accurately describe dynamic properties such as activation energies of diffusion and conduction processes, outlining the need to develop more accurate models for the simulation of molten salt carbonates.     

DFT investigation on the decarboxylation mechanism of <Emphasis Type="Italic">ortho</Emphasis> hydroxy benzoic acids with acid catalysis

A density functional theory (DFT) study was performed to explore the mechanisms of the acid-catalyzed decarboxylation reaction of salicylic acids using the B3LYP method with 6-31++G(d,p) basis set in both gas phase and aqueous environment. The α-protonated cation of carboxylate acid was formed during the decarboxylation process in acidic conditions, and the presence of hydrogen ions promotes decarboxylation greatly by significantly decreasing the overall reaction energy barriers to 20.98 kcal mol^?1 in gas phase and 20.93 kcal mol^?1 in water, respectively. The hydrogen in the α-carbon came directly from the acid rather than from the carboxyl group in neutral state. Compared with the reaction in gas phase, water in aqueous state causes the reaction to occur more easily. Substituents of methyl group, chlorine and fluorine at the ortho-position to the carboxyl of salicylic acid could further lower the decarboxylation energy barriers and facilitate the reaction.     

Generalized logical model based on network topology to capture the dynamical trends of cellular signaling pathways

Background

Cellular responses to extracellular perturbations require signaling pathways to capture and transmit the signals. However, the underlying molecular mechanisms of signal transduction are not yet fully understood, thus detailed and comprehensive models may not be available for all the signaling pathways. In particular, insufficient knowledge of parameters, which is a long-standing hindrance for quantitative kinetic modeling necessitates the use of parameter-free methods for modeling and simulation to capture dynamic properties of signaling pathways.

Results

We present a computational model that is able to simulate the graded responses to degradations, the sigmoidal biological relationships between signaling molecules and the effects of scheduled perturbations to the cells. The simulation results are validated using experimental data of protein phosphorylation, demonstrating that the proposed model is capable of capturing the main trend of protein activities during the process of signal transduction. Compared with existing simulators, our model has better performance on predicting the state transitions of signaling networks.

Conclusion

The proposed simulation tool provides a valuable resource for modeling cellular signaling pathways using a knowledge-based method.

     

Prediction of drugs having opposite effects on disease genes in a directed network

Background

Developing novel uses of approved drugs, called drug repositioning, can reduce costs and times in traditional drug development. Network-based approaches have presented promising results in this field. However, even though various types of interactions such as activation or inhibition exist in drug-target interactions and molecular pathways, most of previous network-based studies disregarded this information.

Methods

We developed a novel computational method, Prediction of Drugs having Opposite effects on Disease genes (PDOD), for identifying drugs having opposite effects on altered states of disease genes. PDOD utilized drug-drug target interactions with ‘effect type’, an integrated directed molecular network with ‘effect type’ and ‘effect direction’, and disease genes with regulated states in disease patients. With this information, we proposed a scoring function to discover drugs likely to restore altered states of disease genes using the path from a drug to a disease through the drug-drug target interactions, shortest paths from drug targets to disease genes in molecular pathways, and disease gene-disease associations.

Results

We collected drug-drug target interactions, molecular pathways, and disease genes with their regulated states in the diseases. PDOD is applied to 898 drugs with known drug-drug target interactions and nine diseases. We compared performance of PDOD for predicting known therapeutic drug-disease associations with the previous methods. PDOD outperformed other previous approaches which do not exploit directional information in molecular network. In addition, we provide a simple web service that researchers can submit genes of interest with their altered states and will obtain drugs seeming to have opposite effects on altered states of input genes at http://gto.kaist.ac.kr/pdod/index.php/main.

Conclusions

Our results showed that ‘effect type’ and ‘effect direction’ information in the network based approaches can be utilized to identify drugs having opposite effects on diseases. Our study can offer a novel insight into the field of network-based drug repositioning.