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991.
Young Ho Koh Yong Seek Park Motoko Takahashi Keiichiro Suzuki Naoyuki Taniguchi 《Free radical research》2013,47(6):739-746
Membrane lipid peroxidation results in the production of a variety of aldehydic compounds that play a significant role in aging, drug toxicity and the pathogenesis of a number of human diseases, such as atherosclerosis and cancer. Increased lipid peroxidation and reduced antioxidant status may also contribute to the development of diabetic complications. This study reports that lipid peroxidation end products such as malondialdehyde (MDA) and 4-hydroxynonenal (HNE) induce aldehyde reductase (ALR) gene expression. MDA and HNE induce an increase in intracellular peroxide levels; N-Acetyl-L-cysteine (NAC) suppressed MDA- and HNE-induced ALR gene expression. These results indicate that increased levels of intracellular peroxides by MDA and HNE might be involved in the upregulation of ALR. 相似文献
992.
Chiho Nishizawa Keizo Takeshita Jun-ichi Ueda Michiko Mizuno Kazuo T. Suzuki Toshihiko Ozawa 《Free radical research》2013,47(4):385-392
Photosensitizers newly developed for photodynamic therapy of cancer need to be assessed using accurate methods of measuring reactive oxygen species (ROS). Little is known about the characteristics of the reaction of singlet oxygen (1O2) with spin traps, although this knowledge is necessary in electron spin resonance (ESR)/spin trapping. In the present study, we examined the effect of various reductants usually present in biological samples on the reaction of 1O2 with 5,5-dimethyl-1-pyrroline-N-oxide (DMPO). The ESR signal of the hydroxyl radical (?OH) adduct of DMPO (DMPO-OH) resulting from 1O2-dependent generation of ?OH strengthened remarkably in the presence of reduced glutathione (GSH), 6-hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid (Trolox), ascorbic acid, NADPH, etc. A similar increase was observed in the photosensitization of uroporphyrin (UP), rose bengal (RB) or methylene blue (MB). Use of 5-(diethoxyphosphoryl)-5-methyl-1-pyrroline-N-oxide (DEPMPO) as a spin trap significantly lessened the production of its ?OH adduct (DEPMPO-OH) in the presence of the reductants. The addition of DMPO to the DEPMPO-spin trapping system remarkably increased the signal intensity of DEPMPO-OH. DMPO-mediated generation of ?OH was also confirmed utilizing the hydroxylation of salicylic acid (SA). These results suggest that biological reductants enhance the ESR signal of DMPO-OH produced by DMPO-mediated generation of ?OH from 1O2, and that spin trap-mediated ?OH generation hardly occurs with DEPMPO. 相似文献
993.
Background
Certain amino acids in proteins play a critical role in determining their structural stability and function. Examples include flexible regions such as hinges which allow domain motion, and highly conserved residues on functional interfaces which allow interactions with other proteins. Detecting these regions can aid in the analysis and simulation of protein rigidity and conformational changes, and helps characterizing protein binding and docking. We present an analysis of critical residues in proteins using a combination of two complementary techniques. One method performs in-silico mutations and analyzes the protein's rigidity to infer the role of a point substitution to Glycine or Alanine. The other method uses evolutionary conservation to find functional interfaces in proteins.Results
We applied the two methods to a dataset of proteins, including biomolecules with experimentally known critical residues as determined by the free energy of unfolding. Our results show that the combination of the two methods can detect the vast majority of critical residues in tested proteins.Conclusions
Our results show that the combination of the two methods has the potential to detect more information than each method separately. Future work will provide a confidence level for the criticalness of a residue to improve the accuracy of our method and eliminate false positives. Once the combined methods are integrated into one scoring function, it can be applied to other domains such as estimating functional interfaces.994.
Background
We introduce a protein docking refinement method that accepts complexes consisting of any number of monomeric units. The method uses a scoring function based on a tight coupling between evolutionary conservation, geometry and physico-chemical interactions. Understanding the role of protein complexes in the basic biology of organisms heavily relies on the detection of protein complexes and their structures. Different computational docking methods are developed for this purpose, however, these methods are often not accurate and their results need to be further refined to improve the geometry and the energy of the resulting complexes. Also, despite the fact that complexes in nature often have more than two monomers, most docking methods focus on dimers since the computational complexity increases exponentially due to the addition of monomeric units.Results
Our results show that the refinement scheme can efficiently handle complexes with more than two monomers by biasing the results towards complexes with native interactions, filtering out false positive results. Our refined complexes have better IRMSDs with respect to the known complexes and lower energies than those initial docked structures.Conclusions
Evolutionary conservation information allows us to bias our results towards possible functional interfaces, and the probabilistic selection scheme helps us to escape local energy minima. We aim to incorporate our refinement method in a larger framework which also enables docking of multimeric complexes given only monomeric structures.995.
Henry Ginsberg 《Cardiovascular diabetology》2013,12(Z1):S1
The term cardiometabolic disease encompasses a range of lifestyle-related conditions, including Metabolic syndrome (MetS) and type 2 diabetes (T2D), that are characterized by different combinations of cardiovascular (CV) risk factors, including dyslipidemia, abdominal obesity, hypertension, hyperglycemia/insulin resistance, and vascular inflammation. These risk factors individually and interdependently increase the risk of CV and cerebrovascular events, and represent one of the biggest health challenges worldwide today. CV diseases account for almost 50% of all deaths in Europe and around 30% of all deaths worldwide. Furthermore, the risk of CV death is increased twofold to fourfold in people with T2D. Whilst the clinical management of CV disease has improved in Western Europe, the pandemic of obesity and T2D reduces the impact of these gains. This, together with the growing, aging population, means the number of CV deaths is predicted to increase from 17.1 million worldwide in 2004 to 23.6 million in 2030. The recommended treatment for MetS is lifestyle change followed by treatment for the individual risk factors. Numerous studies have shown that lowering low-density lipoprotein-cholesterol (LDL-C) levels using statins can significantly reduce CV risk in people with and without T2D or MetS. However, the risk of major vascular events in those attaining the maximum levels of LDL-C-reduction is only reduced by around one-third, which leaves substantial residual risk. Recent studies suggest that low high-density lipoprotein-cholesterol (HDL-C) (<1 .0 mmol/l; 40 mg/dl) and high triglyceride levels (≥1.7 mmol/l; 150 mg/dl) are independent risk factors for CV disease and that the relationship between HDL-C and CV risk persists even when on-treatment LDL-C levels are low (<1.7 mmol/l; 70 mg/dl). European guidelines highlight the importance of reducing residual risk by targeting these risk factors in addition to LDL-C. This is particularly important in patients with T2D and MetS because obesity and high levels of glycated hemoglobin are directly related to low levels of HDL-C and high triglyceride. Although most statins have a similar low-density lipoprotein-lowering efficacy, differences in chemical structure and pharmacokinetic profile can lead to variations in pleiotropic effects (for example, high-density lipoprotein-elevating efficacy), adverse event profiles, and drug-drug interactions. The choice of statin should therefore depend on the needs of the individual patient. The following reviews will discuss the potential benefits of pitavastatin versus other statins in the treatment of patients with dyslipidemia and MetS or T2D, focusing on its effects on HDL-C quantity and quality, its potential impact on atherosclerosis and CV risk, and its metabolic characteristics that reduce the risk of drug interactions. Recent controversies surrounding the potentially diabetogenic effects of statins will also be discussed. 相似文献
996.
Keitarou Suzuki Yoshikazu Terasaki Masaru Uyeda 《Journal of enzyme inhibition and medicinal chemistry》2013,28(3):183-186
The inhibitory effects of various fatty acids on three hyaluronidases (h-ST, h-SH and h-SD) and four chondroitinases (c-ABC, c-B, c-ACI and c-ACII) were examined, and their structure-activity relationships and mechanism of action were studied. The fatty acids used in this experiment showed various inhibitory activities against the enzymes. None of the fatty acids did not inhibit h-ST and h-SH. The saturated fatty acids (C 10:0 to C 22:0) showed very weak or no inhibition against h-SD, c-ABC, c-B, c-ACI and c-ACII but the unsaturated fatty acids (C 14:1 to C 24:1) with one double bond strongly inhibited the enzymes, and the inhibitory potency increased with increase in carbon chain length of the fatty acids. In contrast, the increase in number of double bonds caused a decrease in inhibitory potency against the enzymes. The position of the double bond and the stereochemistry of the cis - trans form of oleic acid (C 18:1) did not influence the inhibitory potency against the enzymes. Carboxyl and hydroxyl groups in the fatty acid molecule were concerned in the inhibition of c-ACI. Among the fatty acids, eicosatrienoic acid (C 20:3) generally inhibited h-SD, c-ABC, c-B and c-ACI, and nervonic acid (C 24:1) was a potent inhibitor of c-ACII, and the fatty acids inhibited the enzymes in a noncompetitive manner. 相似文献
997.
Murtada Khalafallah Elbashir Jianxin Wang Fang-Xiang Wu Lusheng Wang 《Proteome science》2013,11(Z1):S5
Background
β-turns are secondary structure type that have essential role in molecular recognition, protein folding, and stability. They are found to be the most common type of non-repetitive structures since 25% of amino acids in protein structures are situated on them. Their prediction is considered to be one of the crucial problems in bioinformatics and molecular biology, which can provide valuable insights and inputs for the fold recognition and drug design.Results
We propose an approach that combines support vector machines (SVMs) and logistic regression (LR) in a hybrid prediction method, which we call (H-SVM-LR) to predict β-turns in proteins. Fractional polynomials are used for LR modeling. We utilize position specific scoring matrices (PSSMs) and predicted secondary structure (PSS) as features. Our simulation studies show that H-SVM-LR achieves Qtotal of 82.87%, 82.84%, and 82.32% on the BT426, BT547, and BT823 datasets respectively. These values are the highest among other β-turns prediction methods that are based on PSSMs and secondary structure information. H-SVM-LR also achieves favorable performance in predicting β-turns as measured by the Matthew's correlation coefficient (MCC) on these datasets. Furthermore, H-SVM-LR shows good performance when considering shape strings as additional features.Conclusions
In this paper, we present a comprehensive approach for β-turns prediction. Experiments show that our proposed approach achieves better performance compared to other competing prediction methods.998.
Background
Detecting protein complexes in protein-protein interaction (PPI) networks plays an important role in improving our understanding of the dynamic of cellular organisation. However, protein interaction data generated by high-throughput experiments such as yeast-two-hybrid (Y2H) and tandem affinity-purification/mass-spectrometry (TAP-MS) are characterised by the presence of a significant number of false positives and false negatives. In recent years there has been a growing trend to incorporate diverse domain knowledge to support large-scale analysis of PPI networks.Methods
This paper presents a new algorithm, by incorporating Gene Ontology (GO) based semantic similarities, to detect protein complexes from PPI networks generated by TAP-MS. By taking co-complex relations in TAP-MS data into account, TAP-MS PPI networks are modelled as bipartite graph, where bait proteins consist of one set of nodes and prey proteins are on the other. Similarities between pairs of bait proteins are computed by considering both the topological features and GO-driven semantic similarities. Bait proteins are then grouped in to sets of clusters based on their pair-wise similarities to produce a set of 'seed' clusters. An expansion process is applied to each 'seed' cluster to recruit prey proteins which are significantly associated with the same set of bait proteins. Thus, completely identified protein complexes are then obtained.Results
The proposed algorithm has been applied to real TAP-MS PPI networks. Fifteen quality measures have been employed to evaluate the quality of generated protein complexes. Experimental results show that the proposed algorithm has greatly improved the accuracy of identifying complexes and outperformed several state-of-the-art clustering algorithms. Moreover, by incorporating semantic similarity, the proposed algorithm is more robust to noises in the networks.999.
Background
Protein-protein interactions (PPIs) play a key role in understanding the mechanisms of cellular processes. The availability of interactome data has catalyzed the development of computational approaches to elucidate functional behaviors of proteins on a system level. Gene Ontology (GO) and its annotations are a significant resource for functional characterization of proteins. Because of wide coverage, GO data have often been adopted as a benchmark for protein function prediction on the genomic scale.Results
We propose a computational approach, called M-Finder, for functional association pattern mining. This method employs semantic analytics to integrate the genome-wide PPIs with GO data. We also introduce an interactive web application tool that visualizes a functional association network linked to a protein specified by a user. The proposed approach comprises two major components. First, the PPIs that have been generated by high-throughput methods are weighted in terms of their functional consistency using GO and its annotations. We assess two advanced semantic similarity metrics which quantify the functional association level of each interacting protein pair. We demonstrate that these measures outperform the other existing methods by evaluating their agreement to other biological features, such as sequence similarity, the presence of common Pfam domains, and core PPIs. Second, the information flow-based algorithm is employed to discover a set of proteins functionally associated with the protein in a query and their links efficiently. This algorithm reconstructs a functional association network of the query protein. The output network size can be flexibly determined by parameters.Conclusions
M-Finder provides a useful framework to investigate functional association patterns with any protein. This software will also allow users to perform further systematic analysis of a set of proteins for any specific function. It is available online at http://bionet.ecs.baylor.edu/mfinder1000.