Interaction site prediction by structural similarity to neighboring clusters in protein-protein interaction networks

<p>Abstract</p> <p>Background</p> <p>Recently, revealing the function of proteins with protein-protein interaction (PPI) networks is regarded as one of important issues in bioinformatics. With the development of experimental methods such as the yeast two-hybrid method, the data of protein interaction have been increasing extremely. Many databases dealing with these data comprehensively have been constructed and applied to analyzing PPI networks. However, few research on prediction interaction sites using both PPI networks and the 3D protein structures complementarily has explored.</p> <p>Results</p> <p>We propose a method of predicting interaction sites in proteins with unknown function by using both of PPI networks and protein structures. For a protein with unknown function as a target, several clusters are extracted from the neighboring proteins based on their structural similarity. Then, interaction sites are predicted by extracting similar sites from the group of a protein cluster and the target protein. Moreover, the proposed method can improve the prediction accuracy by introducing repetitive prediction process.</p> <p>Conclusions</p> <p>The proposed method has been applied to small scale dataset, then the effectiveness of the method has been confirmed. The challenge will now be to apply the method to large-scale datasets.</p

A method for supporting retrieval of articles on protein structure analysis considering users’ intention

Abstract Background In recent years, information about protein structure and function is described in a large amount of articles. However, a naive full-text search by specific keywords often fails to find desired articles, because the articles involve the ambiguous and complicated concepts that cannot be described with uniform representation. For retrieving articles on protein structure and function, it is important to consider the relevance between structural and/or functional concepts by identifying the user’s intention. Results We introduce a scheme of evaluating relevance between articles based on various biological databases and ontologies on structures and functions of proteins. The relevance, which is defined as a path length between concepts on hierarchies, is modified adaptively based on additional articles as a query in order to reflect the user’s intention. Also we implemented the retrieval system, in which the user can input some articles as a query and the related articles are retrieved and displayed on the 2D map. Conclusions The effectiveness of the proposed system was confirmed experimentally by having shown that the users can obtain easily highly related articles which reflect their intention.</p

A Computational Model for Children's Language Acquisition using Inductive Logic Programming

This paper proposes a computational model for children's word acquisition based on inductive logic programming. There are three fundamental features in our approach. Firstly, we incorporate cognitive biases developed recently to explain the efficiency of children's language acquisition. Secondly, we design a co-evolution mechanism of acquiring concept definitions for words and developing concept hierarchy. Concept hierarchy plays an important role of defining contexts for later word learning processes. A context switching mechanism is used to select a relevant set of attributes for learning a word depending on the category which it belongs to. On the other hand, during acquiring definitions for words, concept hierarchy is developed. Thirdly, we pursue resemblance to human brain in functional level.We developed an experimental language acquisition system called WISDOM (Word Induction System for Deriving Object Model) and conducted virtual experiments or simulations on acquisition of words in two different categories. The experiments shows feasibility of our approach

A novel selective PPARα modulator, pemafibrate promotes ischemia-induced revascularization through the eNOS-dependent mechanisms.

ObjectiveCardiovascular disease is a leading cause of death worldwide. Obesity-related metabolic disorders including dyslipidemia cause impaired collateralization under ischemic conditions, thereby resulting in exacerbated cardiovascular dysfunction. Pemafibrate is a novel selective PPARα modulator, which has been reported to improve atherogenic dyslipidemia, in particular, hypertriglyceridemia and low HDL-cholesterol. Here, we investigated whether pemafibrate modulates the revascularization process in a mouse model of hindlimb ischemia.Methods and resultsMale wild-type (WT) mice were randomly assigned to two groups, normal diet or pemafibrate admixture diet from the ages of 6 weeks. After 4 weeks, mice were subjected to unilateral hindlimb surgery to remove the left femoral artery and vein. Pemafibrate treatment enhanced blood flow recovery and capillary formation in ischemic limbs of mice, which was accompanied by enhanced phosphorylation of endothelial nitric oxide synthase (eNOS). Treatment of cultured endothelial cells with pemafibrate resulted in increased network formation and migratory activity, which were blocked by pretreatment with the NOS inhibitor NG-nitro-L-arginine methyl ester (L-NAME). Pemafibrate treatment also increased plasma levels of the PPARα-regulated gene, fibroblast growth factor (FGF) 21 in WT mice. Systemic administration of adenoviral vectors expressing FGF21 (Ad-FGF21) to WT mice enhanced blood flow recovery, capillary density and eNOS phosphorylation in ischemic limbs. Treatment of cultured endothelial cells with FGF21 protein led to increases in endothelial cell network formation and migration, which were canceled by pretreatment with L-NAME. Furthermore, administration of pemafibrate or Ad-FGF21 had no effects on blood flow in ischemic limbs in eNOS-deficient mice.ConclusionThese data suggest that pemafibrate can promote revascularization in response to ischemia, at least in part, through direct and FGF21-mediated modulation of endothelial cell function. Thus, pemafibrate could be a potentially beneficial drug for ischemic vascular disease

Adipolin/C1q/Tnf-related protein 12 prevents adverse cardiac remodeling after myocardial infarction.

BackgroundMyocardial infarction (MI) is a leading cause of death worldwide. We previously identified adipolin, also known as C1q/Tnf-related protein 12, as an anti-inflammatory adipokine with protective features against metabolic and vascular disorders. Here, we investigated the effect of adipolin on myocardial remodeling in a mouse model of MI.MethodsMale adipolin-knockout (APL-KO) and wild-type (WT) mice were subjected to the permanent ligation of the left anterior descending coronary artery to create MI.ResultsAPL-KO mice exhibited increased ratios of heart weight/body weight and lung weight/body weight after MI compared with WT mice. APL-KO mice showed increased left ventricular diastolic diameter and decreased fractional shortening after MI compared with WT mice. APL-KO mice exhibited increased expression of pro-inflammatory mediators and enhanced cardiomyocyte apoptosis in the post-MI hearts compared with WT mice. Systemic administration of adenoviral vectors expressing adipolin to WT mice after MI surgery improved left ventricular contractile dysfunction and reduced cardiac expression of pro-inflammatory genes. Treatment of cultured cardiomyocytes with adipolin protein reduced lipopolysaccharide-induced expression of pro-inflammatory mediators and hypoxia-induced apoptosis. Treatment with adipolin protein increased Akt phosphorylation in cardiomyocytes. Inhibition of PI3 kinase/Akt signaling reversed the anti-inflammatory and anti-apoptotic effects of adipolin in cardiomyocytes.ConclusionOur data indicate that adipolin ameliorates pathological remodeling of myocardium after MI, at least in part, by its ability to reduce myocardial inflammatory response and apoptosis