Patterning of ferroelectric nanodot arrays using a silicon nitride shadow mask

We grew well-ordered arrays of ferroelectric Pb (Zr0.2 Ti0.8) O3 (PZT) nanodots on a SrRu O3 SrTi O3 substrate by pulsed laser deposition. A silicon nitride shadow mask with ordered holes was used for patterning of the PZT arrays. Each dot has a height of ???15 nm and a diameter of ???120 nm with a similar dome shape over a large area. The ferroelectric properties of individual PZT dots were investigated by piezoresponse force microscopy. A single dot could be polarized individually and the polarized state remained unrelaxed to ???20 min.open232

SUMO-Specific Protease 2 (SENP2) Is an Important Regulator of Fatty Acid Metabolism in Skeletal Muscle

Small ubiquitin-like modifier (SUMO)-specific proteases (SENPs) that reverse protein modification by SUMO are involved in the control of numerous cellular processes, including transcription, cell division, and cancer development. However, the physiological function of SENPs in energy metabolism remains unclear. Here, we investigated the role of SENP2 in fatty acid metabolism in C2C12 myotubes and in vivo. In C2C12 myotubes, treatment with saturated fatty acids, like palmitate, led to nuclear factor-B-mediated increase in the expression of SENP2. This increase promoted the recruitment of peroxisome proliferator-activated receptor (PPAR) and PPAR, through desumoylation of PPARs, to the promoters of the genes involved in fatty acid oxidation (FAO), such as carnitine-palmitoyl transferase-1 (CPT1b) and long-chain acyl-CoA synthetase 1 (ACSL1). In addition, SENP2 overexpression substantially increased FAO in C2C12 myotubes. Consistent with the cell culture system, muscle-specific SENP2 overexpression led to a marked increase in the mRNA levels of CPT1b and ACSL1 and thereby in FAO in the skeletal muscle, which ultimately alleviated high-fat diet-induced obesity and insulin resistance. Collectively, these data identify SENP2 as an important regulator of fatty acid metabolism in skeletal muscle and further implicate that muscle SENP2 could be a novel therapeutic target for the treatment of obesity-linked metabolic disorders.11116Ysciescopu

Dissimilarity in the Folding of Human Cytosolic Creatine Kinase Isoenzymes

Creatine kinase (CK, EC 2.7.3.2) plays a key role in the energy homeostasis of excitable cells. The cytosolic human CK isoenzymes exist as homodimers (HMCK and HBCK) or a heterodimer (MBCK) formed by the muscle CK subunit (M) and/or brain CK subunit (B) with highly conserved three-dimensional structures composed of a small N-terminal domain (NTD) and a large C-terminal domain (CTD). The isoforms of CK provide a novel system to investigate the sequence/structural determinants of multimeric/multidomain protein folding. In this research, the role of NTD and CTD as well as the domain interactions in CK folding was investigated by comparing the equilibrium and kinetic folding parameters of HMCK, HBCK, MBCK and two domain-swapped chimeric forms (BnMc and MnBc). Spectroscopic results indicated that the five proteins had distinct structural features depending on the domain organizations. MBCK BnMc had the smallest CD signals and the lowest stability against guanidine chloride-induced denaturation. During the biphasic kinetic refolding, three proteins (HMCK, BnMc and MnBc), which contained either the NTD or CTD of the M subunit and similar microenvironments of the Trp fluorophores, refolded about 10-fold faster than HBCK for both the fast and slow phase. The fast folding of these three proteins led to an accumulation of the aggregation-prone intermediate and slowed down the reactivation rate thereby during the kinetic refolding. Our results suggested that the intra- and inter-subunit domain interactions modified the behavior of kinetic refolding. The alternation of domain interactions based on isoenzymes also provides a valuable strategy to improve the properties of multidomain enzymes in biotechnology

'Unite and conquer': enhanced prediction of protein subcellular localization by integrating multiple specialized tools

<p>Abstract</p> <p>Background</p> <p>Knowing the subcellular location of proteins provides clues to their function as well as the interconnectivity of biological processes. Dozens of tools are available for predicting protein location in the eukaryotic cell. Each tool performs well on certain data sets, but their predictions often disagree for a given protein. Since the individual tools each have particular strengths, we set out to integrate them in a way that optimally exploits their potential. The method we present here is applicable to various subcellular locations, but tailored for predicting whether or not a protein is localized in mitochondria. Knowledge of the mitochondrial proteome is relevant to understanding the role of this organelle in global cellular processes.</p> <p>Results</p> <p>In order to develop a method for enhanced prediction of subcellular localization, we integrated the outputs of available localization prediction tools by several strategies, and tested the performance of each strategy with known mitochondrial proteins. The accuracy obtained (up to 92%) surpasses by far the individual tools. The method of integration proved crucial to the performance. For the prediction of mitochondrion-located proteins, integration via a two-layer decision tree clearly outperforms simpler methods, as it allows emphasis of biologically relevant features such as the mitochondrial targeting peptide and transmembrane domains.</p> <p>Conclusion</p> <p>We developed an approach that enhances the prediction accuracy of mitochondrial proteins by uniting the strength of specialized tools. The combination of machine-learning based integration with biological expert knowledge leads to improved performance. This approach also alleviates the conundrum of how to choose between conflicting predictions. Our approach is easy to implement, and applicable to predicting subcellular locations other than mitochondria, as well as other biological features. For a trial of our approach, we provide a webservice for mitochondrial protein prediction (named YimLOC), which can be accessed through the AnaBench suite at http://anabench.bcm.umontreal.ca/anabench/. The source code is provided in the Additional File <supplr sid="S2">2</supplr>.</p> <suppl id="S2"> <title> <p>Additional file 2</p> </title> <text> <p>This file contains scripts for the online server YimLOC. Please note that there scripts only codes for the ready-to-use STACK-mem-DT described in the main text. The scripts do not provide the training process.</p> </text> <file name="1471-2105-8-420-S2.pdf"> <p>Click here for file</p> </file> </suppl

Subcellular location prediction of proteins using support vector machines with alignment of block sequences utilizing amino acid composition

Background: Subcellular location prediction of proteins is an important and well-studied problem in bioinformatics. This is a problem of predicting which part in a cell a given protein is transported to, where an amino acid sequence of the protein is given as an input. This problem is becoming more important since information on subcellular location is helpful for annotation of proteins and genes and the number of complete genomes is rapidly increasing. Since existing predictors are based on various heuristics, it is important to develop a simple method with high prediction accuracies. Results: In this paper, we propose a novel and general predicting method by combining techniques for sequence alignment and feature vectors based on amino acid composition. We implemented this method with support vector machines on plant data sets extracted from the TargetP database. Through fivefold cross validation tests, the obtained overall accuracies and average MCC were 0.9096 and 0.8655 respectively. We also applied our method to other datasets including that of WoLF PSORT. Conclusion: Although there is a predictor which uses the information of gene ontology and yields higher accuracy than ours, our accuracies are higher than existing predictors which use only sequence information. Since such information as gene ontology can be obtained only for known proteins, our predictor is considered to be useful for subcellular location prediction of newly-discovered proteins. Furthermore, the idea of combination of alignment and amino acid frequency is novel and general so that it may be applied to other problems in bioinformatics. Our method for plant is also implemented as a web-system and available on http://sunflower.kuicr.kyoto-u.ac.jp/~tamura/slpfa.html webcite

A method to improve protein subcellular localization prediction by integrating various biological data sources

<p>Abstract</p> <p>Background</p> <p>Protein subcellular localization is crucial information to elucidate protein functions. Owing to the need for large-scale genome analysis, computational method for efficiently predicting protein subcellular localization is highly required. Although many previous works have been done for this task, the problem is still challenging due to several reasons: the number of subcellular locations in practice is large; distribution of protein in locations is imbalanced, that is the number of protein in each location remarkably different; and there are many proteins located in multiple locations. Thus it is necessary to explore new features and appropriate classification methods to improve the prediction performance.</p> <p>Results</p> <p>In this paper we propose a new predicting method which combines two key ideas: 1) Information of neighbour proteins in a probabilistic gene network is integrated to enrich the prediction features. 2) Fuzzy k-NN, a classification method based on fuzzy set theory is applied to predict protein locating in multiple sites. Experiment was conducted on a dataset consisting of 22 locations from Budding yeast proteins and significant improvement was observed.</p> <p>Conclusion</p> <p>Our results suggest that the neighbourhood information from functional gene networks is predictive to subcellular localization. The proposed method thus can be integrated and complementary to other available prediction methods.</p

Search for a narrow charmed baryonic state decaying to D^*+/- p^-/+ in ep collisions at HERA

A resonance search has been made in the D^*+/- p^-/+ invariant-mass spectrum with the ZEUS detector at HERA using an integrated luminosity of 126 pb^-1. The decay channels D^*+ -> D^0 pi^+_s -> (K^- pi^+) pi^+_s and D^*+ -> D^0 pi^+_s -> (K^- pi^+ pi^+ pi^-) pi^+_s (and the corresponding antiparticle decays) were used to identify D^*+/- mesons. No resonance structure was observed in the D^*+/- p^-/+ mass spectrum from more than 60000 reconstructed D^*+/- mesons. The results are not compatible with a report of the H1 Collaboration of a charmed pentaquark, Theta^0_c.Comment: 22 pages, 7 figures, 1 table; minor text revisions; 2 references adde