Association Between Serum 25-Hydroxyvitamin D Concentrations and Chronic Pain: Effects of Drinking Habits.

PURPOSE: Although the explanation for inconsistencies in the reported association between serum 25-hydroxyvitamin D [25(OH)D] levels and chronic pain (CP) has not yet been determined, understanding this discrepancy is necessary for the development of vitamin D supplementation as an effective treatment for CP. The aim of this cross-sectional study was to examine the relationship between 25(OH)D concentrations and CP according to drinking habits in Japanese subjects. PATIENTS AND METHODS: We distributed invitation letters to 2314 individuals older than 40 years in Shika town, a rural area in Japan, and 724 subjects (386 females; mean age: 63.9 ± 10.4 years) were recruited. CP was defined as persistent pain lasting at least 3 months in any part of the body. Serum concentrations of 25(OH)D, a biomarker of the vitamin D status, were measured using a radioimmunoassay. A serum 25(OH)D level <20 ng/mL was defined as serum 25(OH)D deficiency. Drinking habits were assessed using a self-administered questionnaire. There were three choices, "rarely drink", "sometimes" and "everyday". Respondents who answered "rarely drink" were labelled as non-drinkers and the others as drinkers. RESULTS: The prevalence of CP was 40.6%. A significant interaction between CP and drinking habits on 25(OH)D concentrations was observed (p = 0.098). A one-way analysis of covariance was performed to compare 25(OH)D concentrations between the subjects with and without CP in each drinking group, and the serum 25(OH)D levels of subjects with CP were significantly lower than those without CP among drinkers (p = 0.007). A logistic regression analysis revealed a correlation between serum 25(OH)D deficiency and CP in drinkers after adjustments for several confounding factors (odds ratio: 0.499; 95% confidence interval: 0.268 - 0.927; p = 0.028). CONCLUSION: The present results suggest that low serum 25(OH)D concentrations are associated with the development of CP in drinkers.

Relationship between the Intake of n-3 Polyunsaturated Fatty Acids and Depressive Symptoms in Elderly Japanese People: Differences According to Sex and Weight Status.

n-3 polyunsaturated fatty acids (PUFAs) have been shown to have preventive effects against depression. In this study, we aimed to investigate the associations between the intake of n-3 PUFAs and depression among people according to sex and weight status. We utilized cross-sectional data from the Shika study in Japan. The study was conducted between 2013 and 2016. Data were collected from adults older than 65 years. Invitation letters were distributed to 2677 individuals, 2470 of whom participated in the study (92.3%). We assessed depressive states using the Japanese short version of the Geriatric Depression Scale (GDS-15). We assessed the intake of n-3 PUFAs using the validated food frequency questionnaire. One thousand six hundred thirty-three participants provided data, among which 327 (20.0%) exhibited depressive symptoms. When we performed the stratified analysis by sex and weight status, there were significant inverse relationships between total n-3 PUFAs, individual n-3 PUFAs, and n-3/n-6 PUFAs ratio and depressive symptoms in overweight/obese females. No correlations were observed between n-3 PUFAs intake and depressive states in males. The results demonstrated a relationship between n-3 PUFAs deficiencies and depressive states, particularly in overweight/obese females. Dietary modifications may help to prevent depressive symptoms in overweight/obese females.

Relationship between Dietary n-6 Fatty Acid Intake and Hypertension: Effect of Glycated Hemoglobin Levels.

The relationship between dietary n-6 fatty acids and hypertension is not clear. The metabolic products of n-6 fatty acids include those that control blood pressure, such as prostaglandin and thromboxane, and that differ depending on the extent of glucose tolerance. This cross-sectional study investigated the association of dietary n-6 fatty acid intake on hypertension, and the effects of glycated hemoglobin (HbA1c) value in 633 Japanese subjects aged 40 years and older. Dietary intake was measured using a validated brief self-administered diet history questionnaire. We defined hypertension as the use of antihypertensive medication or a blood pressure of 140/90 mmHg. The prevalence of hypertension was 55.3%. A high n-6 fatty acids intake inversely correlated with hypertension in subjects with HbA1c values less than 6.5% (odds ratio, 0.857; 95% confidence interval, 0.744 to 0.987). On the contrary, in subjects with an HbA1c value of 6.5% or higher, the n-6 fatty acids intake was significantly associated with hypertension (odds ratio, 3.618; 95% confidence interval, 1.019 to 12.84). Regular dietary n-6 fatty acid intake may contribute to the prevention and treatment of hypertension in a healthy general population. By contrast, in subjects with diabetes, regular n-6 fatty acids intake may increase the risk of hypertension.

Selection of in silico drug screening results for G-protein-coupled receptors by using universal active probes.

We developed a new protocol for in silico drug screening for G-protein-coupled receptors (GPCRs) using a set of "universal active probes" (UAPs) with an ensemble docking procedure. UAPs are drug-like compounds, which are actual active compounds of a variety of known proteins. The current targets were nine human GPCRs whose three-dimensional (3D) structures are unknown, plus three GPCRs, namely ß(2)-adrenergic receptor (ADRB2), A(2A) adenosine receptor (A(2A)), and dopamine D3 receptor (D(3)), whose 3D structures are known. Homology-based models of the GPCRs were constructed based on the crystal structures with careful sequence inspection. After subsequent molecular dynamics (MD) simulation taking into account the explicit lipid membrane molecules with periodic boundary conditions, we obtained multiple model structures of the GPCRs. For each target structure, docking-screening calculations were carried out via the ensemble docking procedure, using both true active compounds of the target proteins and the UAPs with the multiple target screening (MTS) method. Consequently, the multiple model structures showed various screening results with both poor and high hit ratios, the latter of which could be identified as promising for use in in silico screening to find candidate compounds to interact with the proteins. We found that the hit ratio of true active compounds showed a positive correlation to that of the UAPs. Thus, we could retrieve appropriate target structures from the GPCR models by applying the UAPs, even if no active compound is known for the GPCRs. Namely, the screening result that showed a high hit ratio for the UAPs could be used to identify actual hit compounds for the target GPCRs.

Selection of in silico drug screening results by using universal active probes (UAPs).

We developed a new method that uses a set of drug-like compounds to select reliable in silico drug screening results. If some active compounds are known, the screening results that rank these active compounds at the top should be reliable. If no active compound is known, how to select the result is in question. We propose a concept of a set of "universal active probes" (UAPs), which is a set of small active compounds that bind to different kinds of proteins. We found that the hit ratio of the true active compounds in in silico screening shows positive correlation to that of the UAPs, probably because UAPs form a set of drug-like compounds. Thus, if the UAPs were added to the compound library, the screening result that shows a high hit ratio of the UAPs could give reliable actual hit compounds for the target protein. We examined this method for several targets and found this idea useful.

A similarity search using molecular topological graphs.

A molecular similarity measure has been developed using molecular topological graphs and atomic partial charges. Two kinds of topological graphs were used. One is the ordinary adjacency matrix and the other is a matrix which represents the minimum path length between two atoms of the molecule. The ordinary adjacency matrix is suitable to compare the local structures of molecules such as functional groups, and the other matrix is suitable to compare the global structures of molecules. The combination of these two matrices gave a similarity measure. This method was applied to in silico drug screening, and the results showed that it was effective as a similarity measure.

In silico fragment screening by replica generation (FSRG) method for fragment-based drug design.

We developed a new in silico screening method, which is a structure-based virtual fragment screening with protein-compound docking. The structure-based in silico screening of small fragments is known to be difficult due to poor surface complementarity between protein surfaces and small compound (fragment) surfaces. In our method, several side chains were attached to the fragment in question to generate a set of replica molecules of different sizes. This chemical modification enabled us to select potentially active fragments more easily than basing the selection on the original form of the fragment. In addition, the Coulombic and hydrogen bonding interactions were ignored in the docking simulation to reduce the variety of chemical modifications. Namely, we focused on the sizes and the shapes of the side chains and could ignore the atomic charges and types of elements. This procedure was validated in the screenings of inhibitors of six target proteins using known active compounds, and the results revealed that our procedure was effective.

A new method for in-silico drug screening and similarity search using molecular dynamics maximum volume overlap (MD-MVO) method.

We developed a new molecular dynamics simulation method for molecular overlapping (alignment) and ligand-based in-silico drug screening based on molecular similarity. The molecular system consists of the query compound and the other compound(s) selected from a compound library. The newly introduced intermolecular interaction between compounds is proportional to the molecular overlap instead of the van der Waals and Coulomb interactions between atoms of different molecules. This method was able to achieve both conformer generation of molecules and molecular overlapping (alignment) at the same time. After an energy minimization and following short-time MD simulation, the molecules in the system were overlapped with each other and the similarity between compounds was measured by the volume of the overlap. We applied this MD simulation method to ligand-based in-silico drug screening and found that it worked well for several targets.

Nature of protein family signatures: insights from singular value analysis of position-specific scoring matrices.

Position-specific scoring matrices (PSSMs) are useful for detecting weak homology in protein sequence analysis, and they are thought to contain some essential signatures of the protein families. In order to elucidate what kind of ingredients constitute such family-specific signatures, we apply singular value decomposition to a set of PSSMs and examine the properties of dominant right and left singular vectors. The first right singular vectors were correlated with various amino acid indices including relative mutability, amino acid composition in protein interior, hydropathy, or turn propensity, depending on proteins. A significant correlation between the first left singular vector and a measure of site conservation was observed. It is shown that the contribution of the first singular component to the PSSMs act to disfavor potentially but falsely functionally important residues at conserved sites. The second right singular vectors were highly correlated with hydrophobicity scales, and the corresponding left singular vectors with contact numbers of protein structures. It is suggested that sequence alignment with a PSSM is essentially equivalent to threading supplemented with functional information. In addition, singular vectors may be useful for analyzing and annotating the characteristics of conserved sites in protein families.

[New progress in crystallization technology of membrane protein and introduction of pharamaceutical innovation value chain].

We have recently established a Pharamaceutical Innovation Value Chain in collaboration with the SOSHO project (http://www.so-sho.jp) and BioGrid Project (http://www.biogrid.jp/) to accelerate new drug development. The SOSHO project provides novel crystallization technology with laser-irradiation and stirring growth methods, and the BioGrid Project is developing the software necessary for the in silico screening of promising drugs and the simulation of biological responses to proteins. In this paper, we report the recent research work on the crystallization of membrane proteins and the development of a method for in silico drug discovery.

In-silico drug screening method based on the protein-compound affinity matrix using the factor selection technique.

We have developed a new in-silico drug screening method, a modified version of a docking score index (DSI) method, based on a protein-compound docking affinity matrix. By using this method, the docking scores are converted to the docking score indexes by the principal component analysis (PCA) method and each compound is projected into a PCA space. In this study, we propose a method to select a set of suitable principal component axes and evaluate the database enrichment for 12 target proteins. This method selects the new active compounds or hits, which are close to the known active compounds, thereby enhancing the database enrichment.

Finding ligands for G protein-coupled receptors based on the protein-compound affinity matrix.

We developed a novel method of identifying new active ligands based on information related to known active compounds using protein-compound docking simulations, even when the tertiary structure of the actual target receptor protein is unknown. This method was used to find ligands of G protein-coupled receptors (GPCRs), i.e., agonists and antagonists of histamine, adrenaline, serotonin and dopamine receptors. The principal component analysis (PCA) method was applied to the protein-compound affinity matrix, which was given by thorough docking calculations between sets of many protein pockets and chemical compounds. The set of protein pockets did not necessary include the target protein. Each compound was depicted as a point in the PCA space. Compounds in a sphere, whose center was set to the known active compound in the multi-dimensional PCA space or to the average position of several known active compounds, were selected as candidate-hit compounds. Our method was found to be effective for finding the ligands of GPCRs based on known native ligands, even when only the soluble protein structures were used in the docking simulations.

An efficient in silico screening method based on the protein-compound affinity matrix and its application to the design of a focused library for cytochrome P450 (CYP) ligands.

A new method has been developed to design a focused library based on available active compounds using protein-compound docking simulations. This method was applied to the design of a focused library for cytochrome P450 (CYP) ligands, not only to distinguish CYP ligands from other compounds but also to identify the putative ligands for a particular CYP. Principal component analysis (PCA) was applied to the protein-compound affinity matrix, which was obtained by thorough docking calculations between a large set of protein pockets and chemical compounds. Each compound was depicted as a point in the PCA space. Compounds that were close to the known active compounds were selected as candidate hit compounds. A machine-learning technique optimized the docking scores of the protein-compound affinity matrix to maximize the database enrichment of the known active compounds, providing an optimized focused library.

A virtual active compound produced from the negative image of a ligand-binding pocket, and its application to in-silico drug screening.

We developed a new structure-based in-silico screening method using a negative image of a ligand-binding pocket and a multi-protein-compound interaction matrix. Based on the structure of the ligand pocket of the target protein, we designed a negative image, which consists of virtual atoms whose radii are close to those of carbon atoms. The virtual atoms fit the pocket ideally and achieve an optimal Coulomb interaction. A protein-compound docking program calculates the protein-compound interaction matrix for many proteins and many compounds including the negative image, which can be treated as a virtual compound. With specific attention to a vector of docking scores for a single compound with many proteins, we selected a compound whose score vector was similar to that of the negative image as a candidate hit compound. This method was applied to representative target proteins and showed high database enrichment with a relatively quick procedure.

Disordered domains and high surface charge confer hubs with the ability to interact with multiple proteins in interaction networks.

We investigate the structural properties of hubs that enable them to interact with several partners in protein-protein interaction networks. We find that hubs have more observed and predicted disordered residues with fewer loops/coils, and more charged residues on the surface as compared to non-hubs. Smaller hubs have fewer disordered residues and more charged residues on the surface than larger hubs. We conclude that the global flexibility provided by disordered domains, and high surface charge are complementary factors that play a significant role in the binding ability of hubs.

Multiple target screening method for robust and accurate in silico ligand screening.

We developed a new in silico multiple target screening (MTS) method, based on a multi-receptor versus multi-ligand docking affinity matrixes, and examined its robustness against changes in the scoring system. According to this method, compounds in a database are docked to multiple proteins. The compounds among these proteins that are likely bind to the target protein are selected as the members of the candidate-hit compound group. Then, the compounds in the group are sorted into descending order using the docking score: the first (n-th) compound is expected to be the most (n-th) probable hit compound. This method was applied to the analysis of a set of 142 receptors and 142 compounds using a receptor-ligand docking program, Sievgene [Y. Fukunishi, Y. Mikami, H. Nakamura, Similarities among receptor pockets and among compounds: analysis and application to in silico ligand screening, J. Mol. Graphics Modelling, 24 (2005) 34-45], and the results demonstrated that this method achieves a high hit ratio compared to uniform sampling. We prepared two new scores: the DeltaG score, designed to reproduce the protein-ligand binding free energy, and the hit-optimized score, designed to maximize the hit ratio of in silico screening. Using the Sievgene docking score, DeltaG score and hit-optimized score, the MTS method is more robust than the multiple active-site correction scoring method [G.P.A. Vigers, J.P. Rizzi, Multiple active site corrections for docking and virtual screening, J. Med. Chem., 47 (2004) 80-89].