Automated docking was performed on a series of thiazolo[5,4-f]quinazolin-9-one derivatives as GSK-3β inhibitors. The docking technique was employed to dock a set of representative compounds within the active site region of 1UV5 using AutoDock 3.05. For these compounds, the correlation between binding free energy (kcal/mol) and IC₅₀ (μM) values were examined. The docking simulation clearly predicted the binding mode that is nearly similar to the crystallographic binding mode within 1.0 Å RMSD. Based on the validations and interactions made by R1 and R2 substituents, inhibitor design was initiated by considering simple combinations. For the designed compounds where the interactions and dock scores are being considered for evaluation, compound 17 exhibited large binding energy (-13.14 kcal/mol) against GSK-3β than the remaining. The results help to understand the type of interactions that occur between designed ligands with GSK-3β binding site region and explain the importance of R1 and R2 substitutions on thiazolo[5,4-f]quinazolin-9-one derivatives.

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The non-uniformity of gene expression data is one of the factors that make gene expression analysis difficult. Gene expression data often do not follow a normal distribution but rather various distributions within each group. Thus, it is impossible to apply basic statistical techniques such as the t-test. In this study, we have developed an analysis method for gene expression data obtained by microarrays using a fuzzy logic algorithm with original membership functions. The method automatically evaluates the data from a histogram of gene expression information for a patient group. Using this method, we predicted the efficacy of an anti-TNF-α treatment for rheumatoid arthritis. We created a prediction model for the effects of 14 weeks of anti-TNF-α treatment based on the gene expression data from the peripheral blood of rheumatoid arthritis patients before the treatment. The model had a predictive success of 89% in the model-establishing data group, 94% in the training group, and 89% in the validation group. The results suggest that the method presented here could be an extremely effective tool for gene expression analysis.

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We introduced the dynamic update technique into the monomer self-consistent charge (SCC) process of the fragment molecular orbital (FMO) method to reduce its computational costs. This technique has already been used for solving linear equations in some quantum chemical calculations. After performing test calculations on three typical polyglycines (GLY₂₀, GLY₄₀, and GLY₆₀), we further performed the FMO calculations on the human immunodeficiency virus type 1 protease complexed with lopinavir using the dynamic update technique. These calculations demonstrate that the computational time of the monomer SCC process can be reduced by about one-third. Furthermore, we examined the dependence of the iteration number of the monomer SCC process on parallelization schemes.

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We compared binding affinity evaluations for 10 FKBP ligands with such state-of-the-art computational methods as FMO, QM/MM, MM-PB/SA, and MP-CAFEE. For the FKBP ligands, we confirmed that each method could provide good correlations between the experimental and computational binding affinities. From the calculated results, we discussed the importance of solvation effect and structural sampling for these methods in detail. In addition, we argued the issue of computational time and present arguments on the future perspective of the computational binding affinity evaluations.

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CDK2 (Cyclin Dependent Kinase 2) acts as a potential therapeutic target in cancer and several efforts have been made to find more specific, potent and selective ATP competitive CDK2 inhibitors. In this paper, we report a virtual screening approach that resulted in 54,558 Lipinski compliant hits from ZINC database based on the features exhibited by four compounds from our previous study. Docking and scoring of all compounds using GOLD (Genetic Optimisation for Ligand Docking) software, to evaluate the affinity of binding towards CDK2 enzyme 2UZO resulted in dock scores between 41.71 - 82.33 kcal/mol. The resultant dataset of 392 hits were filtered based on the specificity between CDK2 and GSK-3β (Glycogen Synthase Kinase-3β) to obtain 17 compounds that are more specific towards CDK2. Further, re-scoring of 17 best docked poses followed by a consensus scoring approach tested with five different scoring functions such as GOLD score, CHEM score implemented in GOLD 3.1, eHiTS_score (electronic High Throughput Screening), MolDock score of Molegro software and X-Score retrieved top hits. Finally, the top ten compounds were examined for anti-proliferative effects against human lung adenocarcinoma epithelial cell line, A549 using MTT assay.

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Single-spanning membrane proteins (MP1) occupy the largest component of membrane proteins in total open reading frames of organisms, having essential functions such as signal transduction, immunological reaction and cell adhesion. We developed a novel software system comprised of two filtering layers for predicting MP1 with or without a signal peptide region. In the first filtering layer, we selected membrane proteins with one or two transmembrane (TM) regions by the membrane protein prediction system SOSUI, which is accurate in predicting transmembrane regions but cannot identify signal peptide regions. The second filtering layer was comprised of several modules for distinguishing signal peptide regions. On the assumption that a signal peptide has two kinds of sequences at the N-terminus by which the signal peptide is embedded into membrane and cleaved at its C-terminal end, we calculated two discrimination scores by the canonical discriminant analysis, using averages of several physical properties around the first N-terminal hydrophobic cluster. This prediction system SOSUImp1 comprised of two filtering layers could discriminate very accurately among five types of proteins: cytoplasmic soluble proteins and secretory proteins, MP1 with and without a signal peptide, and multi spanning membrane proteins. The performance for MP1 with a signal peptide that is important in the cell-cell communication was particularly high compared with previous prediction systems.The prediction system SOSUImp1 and the dataset of 5932 proteins used for developing the system are available at http://bp.nuap.nagoya-u.ac.jp/sosui/mp1/

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Molecular docking was performed on a series of bisaryl substituted thiazoles and oxazoles as PPARδ agonists. The docking technique was applied to dock a set of representative compounds within the active site region of 3D5F using Molegro Virtual Docker v 4.0.0. For these compounds, the correlation between binding free energy (kcal/mol) and log (1/EC₅₀) values produces a good correlation coefficient (r² = 0.719). The docking simulation clearly predicted the binding mode that is nearly similar to the crystallographic binding mode within 0.91Å RMSD. Based on the validations and interactions made by Ar₁ and Ar₂ substituents, ligand design was initiated considering simple combinations. For the designed compounds biopharmaceutical properties e.g. Lipophilicity (logP), Solubility (logS), Ionization constant (pKa), Distribution coefficient (logD) are predicted computationally using ACD/ChemSketch v 12.0. The hydrogen bond interactions are examined and bivariate statistical correlation between predicted biological activity (log (1/EC₅₀) and biopharmaceutical properties are considered for evaluation. Ligand 11 (cC) thus, showed high binding energy (-206.73 kcal/mol) against PPARδ. The results avail to understand the type of interactions that occur between designed ligands with PPARδ binding site region and explain the importance of Ar₁ and Ar₂ substitutions on derivatives of bisaryl substituted thiazoles and oxazoles.

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Structure-based virtual screening is gaining popularity in drug discovery. A number of molecular docking programs and scoring functions have been developed in the community, but they had not fulfilled the demands for the improved accuracy, yet. In order to improve the accuracy, the consensus scoring method has been developed. It combines docking scores from various scoring functions without considering characteristics of the docking scores. In this study, we adopted the concepts of the consensus scoring, and improved the docking score from each docking programs, DOCK, FRED or GOLD, for virtual screening. Instead using simple sum of score components in those docking scores, weight factors of the score components were introduced and adjusted for better predictions of active ligands during virtual screening. Several optimization processes were tested to find the best optimization methods of the docking scores using a wide variety of 113 target proteins with over 2000 diverse decoys. Finally, the optimizations improved the chance to discover the active ligands by up to 52.4% (e.g. from 36.8% to 56.1% using GOLD) for the test set. Additionally, the combination of the optimized scores using GOLD and FRED improved success rate in the test set by 77.2%, and approximately 70% of ligands for target proteins were predictable in the test set with 20 times enrichment.

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To elucidate the role of alterations to carbohydrate chains in cancer, we investigated changes in the expression of 107 glycogenes with the onset and progression of breast cancer by conducting an in silico analysis using GEO-registered data. A comparison between 43 breast tumors and paired normal controls (GSE15852) revealed significant changes in the expression of 24 glycogenes including the genes for 5 N-acetylgalactosaminyltransferases, 3 nucleotide sugar transporters, 3 asparagine-linked glycosylation-involved enzymes, 2 N-acetylglucosaminyl transferases, 2 fucosyltransferases, 2 sulfotransferases, 2 sulfatases, 1 glycosaminoglycan synthesis-involved enzyme, 1 glycosyltransferase, 1 glucosyltransferase, 1 sialyltransferase, and 1 N-acetylglucosaminidase. Furthermore, to clarify whether the expression of particular glycogenes changed dependent on the stage of cancer, we compared gene expression between preinvasive and invasive ductal carcinomas (GDS2045). The expression of 6 glycogenes exhibited significant changes in GDS2045. Notably, 3 of these genes were related to the sulfonation of carbohydrate chains. The up- and down-regulated glycogenes were related to the synthesis of carbohydrate chains and the effects of changes in the expression of glycogenes on oncogenesis were discussed.

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We present a new force field derived from a modification of the universal force field (UFF). Although the UFF is widely used in the field of molecular mechanics and dynamics, it is unsuitable for such applications as correctly estimating the hydrogen bond. We have derived a new variable-charge force field from the UFF by incorporating a modified charge equilibration (MQEq) method for the electrostatic term and a scale factor for the van der Waals term, which we refer to as the extended universal force field (XUFF). The MQEq parameters and the scale factor are optimized. We have implemented the XUFF in the ABINIT-MP program and show that the XUFF is very useful for analyzing the conformation of a molecular system such as hydrogen-bonding molecular pairs, ligands, and proteins.

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