The stability, dispersibility and oral bioavailability of coenzyme Q10 (CoQ10) are known to be improved upon complexing CoQ10 with g-cyclodextrin (γ-CD). However, the details of the three-dimensional structure of the γ-CD/CoQ10 complex are not well understood. Therefore, the molecular composition and three-dimensional structure of the complex were investigated using chemical analyses and molecular modeling. The molecular ratio of γ-CD and CoQ10 in the complex was investigated by NMR as well as by HPLC to determine the γ-CD/CoQ10 ratio of 2.5. DSC analysis of the γ-CD/CoQ10 complex indicated formation of the inclusion complex. Three different complex models (γ-CDx2+CoQ10; γ-CDx3+CoQ10; γ-CDx5+CoQ10x2) that correspond to the derived γ-CD/CoQ10 ratio were also constructed and then molecular mechanics and dynamics calculations were carried out to provide several possible complex structures. Based on the complex structures thus obtained, structural and energetic features of the complexes were examined

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A prediction system for identifying the region of flexible regions of the coiled coil was developed to determine the bending positions of the myosin rods using atomic force microscopy (AFM) and to analyze the molecular structures of proteins containing coiled coils. The prediction system comprises two modules: identification of heptad break points and prediction of fragile points in the coiled coil due to the hydrophilic core or hydrophobic outfield region. Here, we investigated the myosin rods using this prediction system. The results of AFM imaging showed four main flexible regions in a single myosin rod and of the 17 possible fragile points predicted, 16 were located in the four experimental bending regions. Next, we examined the enhanced fluctuation around these predicted fragile points using the B-factor for the three dimensional structure of coiled coil proteins from the SCOP database and found that the fluctuations in the hydrophilic core regions were significantly larger than those in the regions of the normal coiled coil. In contrast, the fluctuations in the hydrophobic outfield regions were reduced, suggesting a structural change of the coiled coils to balance these regions. Thus, the dynamic changes in the structure of the coiled coils around the fragile points may be related to the biological functions of the proteins. The prediction tool which developed in this work was incorporated in the SOSUIcoil system which predicts the coiled coil regions.

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We have improved a modified charge equilibration (MQEq) method for calculating the geometry-dependent distribution of atomic charges. In this paper, Ohno-Klopman, Ohno and DasGupta-Huzinaga equations are adopted to express the shielding effect, and the calculated atomic charges with these MQEq methods are in good agreement with those by the HF/6-31G(d,p) calculations for several organic molecules. These MQEq methods would be useful to estimate the charge distribution for large molecules.

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Recently, many concerns are paid for dual action drugs such as ACE/NEP dual inhibitors which have two different biological activities. To identify multiple active drugs by supervised learning approach, a multi-label classification technique is required. In the present work, we investigated the classification of antihypertensive drugs including ACE/NEP dual inhibitors using support vector machines (SVMs). Biological activity data of the drugs were taken from the MDDR database and they were employed for the computational trial for the training of the SVM classifiers. Structural feature representation of each drug molecule was based on topological fragment spectra (TFS) method. The obtained classifiers were tested for finding ACE/NEP dual inhibitors. The result suggests that the TFS-based SVM classifiers are useful for finding multiple active drugs such as ACE/NEP dual inhibitors.

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We conducted a docking efficiency validation of ArgusLab, a free docking software program. In this study, the calculated binding free energies of protein-ligand complexes by scoring functions were compared with experimental binding affinities. Correlations between the calculated and experimental data were evaluated for 11 ArgusLab settings and compared. Our results indicate that ArgusLab is useful for virtual screening and the weight of van der Waals interactions are unimportant for binding free energy calculations using this software.

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Recently, a variety of methods for protein structure prediction have been developed. However, there are only a limited number of studies where the results have been adequately validated. With this in mind, we have evaluated our previously predicted model of lactate oxidase by comparison with the recently determined crystal structure. In addition, we also analyzed our thermotolerant mutants that were designed on the basis of a predicted model. The validity of these procedures was assessed by comparing the results of rational design against the interpretation of the thermostabilization mechanism. Specifically, we analyzed lactate oxidase (LOX), a useful lactic acid sensor, as an example for validation. LOX was chosen because it has an (β/α)8 barrel (TIM barrel) fold, which constitutes the basic structural framework of numerous important enzymes. We also propose an effective modeling method and thermostabilization technique for the TIM barrel fold.

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Oral absorption of a drug is modeled by the differential equations for dissolution, permeation and gastrointestinal transit processes. The purpose of the present study was to compare simple approximate analytical solutions with full numerical solutions for the calculation of the fraction of a dose absorbed (Fa). The GI compartment model for numerical integration consisted of 1 stomach, 7 intestine and 1 colon compartments, whereas for analytical solutions a simple one well-stirred compartment was used. Full numerical solutions were obtained by numerically integrating the dissolution, permeation and gastrointestinal transit differential equations. In the numerical integration calculation, the concentration change in the GI tract, particle size reduction, transit of drugs, etc., was dynamically simulated. Precipitation in the GI tract and regional differences of solubility and permeability were not considered. In total, 7056 numerical integrations were performed, sweeping practical drug parameter ranges of solubility (0.001 to 1 mg/mL), diffusion coefficient (0.1 – 10 x 10¯6 cm²/sec), dose (1 to 1000 mg), particle diameter (1 to 300 μm) and effective permeability (0.03 – 10 x 10¯4 cm/sec). The analytical solutions investigated were (I) a sequential first order approximation (Fa =1–Pn/(Pn – Dn)exp(–Dn) + Dn/(Pn – Dn)exp(–Pn), Dn: dissolution number, Do: dose number and Pn: permeation number. Dn, Do and Pn are the dimensionless parameters which represent the dissolution time/GI transit time ratio, the solubility/dose ratio, and the permeation time/GI transit time ratio, respectively), (II) a limiting step approximation (the minimum value of Fa = 1–exp(–Pn), Fa = Pn/Do and Fa = 1–exp(–Dn)) and (III) a steady state approximation for the dissolved drug concentration (Fa =1–exp(–1/(1/Dn + Do/Pn)), if Do < 1, Do = 1). Fa values by (I) and (II) were higher than those by numerical integration for low solubility compounds (r² = 0.80 and 0.98, root mean square error (RMSE) = 0.28 and 0.079, respectively). By applying the steady state approximation, the correlation was improved (r² = 0.99, RMSE = 0.047). The steady state approximation for the dissolved drug concentration was appropriate for Fa calculation.

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Aflatoxin B₁ (AFB₁) is a harmful and cancer-causing mycotoxin generated by Aspergillus flavus. Its mechanism of toxicity has not been fully clarified and further research is required. In this study, we attempted to further clarify aflatoxin B₁ toxicity using the results of S. cerevisiae gene expression analysis. In a Ser/Thr phosphatase 2C disruptant (ptc1Δ) with weakened activity of anti-toxic components (cell wall and membrane), the addition of low concentrations of sodium dodecyl sulfate resulted in elevated susceptibility to AFB₁. From the microarray results, expression changes in DNA synthesis or repair, sphingolipid metabolism, glucose metabolism, and cell wall-related genes were well detected. Our results indicate that AFB₁ causes sphingolipid metabolism disorder, leading to dysfunction in signal secretion and inhibition of efficient glucose metabolism, which supplies the materials for cell wall proteins and cellular components, resulting in repression of the stress response to external toxicants.

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