Vectors for N- or C-terminal positioning of the yeast Gal4p DNA binding or activator domains

EMTREE drug terms: fungal protein; heat shock protein 90; hybrid protein; transcription factor EMTREE medical terms: amino terminal sequence; article; carboxy terminal sequence; DNA binding; DNA binding domain; expression vector; Gal4p domain; gene activation; gene activation domain; gene expression; nonhuman; plasmid; plasmid ADC; plasmid BDC; protein domain; protein protein interaction; technique; two hybrid system; yeast MeSH: Binding Sites; DNA-Binding Proteins; Genetic Vectors; Protein Structure, Tertiary; Recombinant Fusion Proteins; Saccharomyces cerevisiae; Saccharomyces cerevisiae Proteins; Trans-Activation (Genetics); Transcription Factors; Two-Hybrid System Techniqu

Functional interaction of STAT3 transcription factor with the coactivator NcoA/SRC1a.

Signal transducer and activator of transcription 3 (STAT3) transcription factors are cytoplasmic proteins that induce gene activation in response to cytokine receptor stimulation. Following tyrosine phosphorylation, STAT3 proteins dimerize, translocate to the nucleus, and activate specific target genes. This transcriptional activation by STAT3 proteins has been shown to require the recruitment of coactivators such as CREB-binding protein (CBP)/p300. In the present study, we show that steroid receptor coactivator 1, NcoA/SRC1a, originally identified as a nuclear receptor coactivator, also functions as a coactivator of STAT3 proteins. In coimmunoprecipitations, NcoA/SRC1a was found to associate with STAT3 following IL-6 stimulation of HepG2 hepatoma cells. Pull-down experiments indicated that the N-terminal part of NcoA/SRC1a associates with the activation domain of STAT3. Overexpression of NcoA/SRC1a or its SRC1e isoform enhanced transcriptional activation by STAT3 proteins in transient transfection experiments. This ability of NcoA/SRC1a to enhance STAT3 activity is dependent upon the presence of the CBP-interacting domain, activation domain 1. Using chromatin immunoprecipitation assays, we found that STAT3, NcoA/SRC1a, and CBP/p300 are simultaneously recruited to the p21(waf1) promoter following interleukin-6 stimulation. Taken together, these data suggest that CBP/p300 and NcoA/SRC1a may function in a common pathway to regulate STAT3 transcriptional activity

Application of Social Network Analysis to Health Care Sectors

Objectives: This study aimed to examine the feasibility of social network analysis as a valuable research tool for indicating a change in research topics in health care and medicine. Methods: Papers used in the analysis were collected from the PubMed database at the National Library of Medicine. After limiting the search to papers affiliated with the National Institutes of Health, 27,125 papers were selected for the analysis. From these papers, the top 100 non-duplicate and most studied Medical Subject Heading terms were extracted. NetMiner V.3 was used for analysis. Weighted degree centrality was applied to the analysis to compare the trends in the change of research topics. Changes in the core keywords were observed for the entire group and in three-year intervals. Results: The core keyword with the highest centrality value was “Risk Factor, ” followed b

Maurocalcine and domain A of the II-III loop of the dihydropyridine receptor Cav 1.1 subunit share common binding sites on the skeletal ryanodine receptor.

International audienceMaurocalcine is a scorpion venom toxin of 33 residues that bears a striking resemblance to the domain A of the dihydropyridine voltage-dependent calcium channel type 1.1 (Cav1.1) subunit. This domain belongs to the II-III loop of Cav1.1, which is implicated in excitation-contraction coupling. Besides the structural homology, maurocalcine also modulates RyR1 channel activity in a manner akin to a synthetic peptide of domain A. Because of these similarities, we hypothesized that maurocalcine and domain A may bind onto an identical region(s) of RyR1. Using a set of RyR1 fragments, we demonstrate that peptide A and maurocalcine bind onto two discrete RyR1 regions: fragments 3 and 7 encompassing residues 1021-1631 and 3201-3661, respectively. The binding onto fragment 7 is of greater importance and was thus further investigated. We found that the amino acid region 3351-3507 of RyR1 (fragment 7.2) is sufficient for these interactions. Proof that peptide A and maurocalcine bind onto the same site is provided by competition experiments in which binding of fragment 7.2 to peptide A is inhibited by preincubation with maurocalcine. Moreover, when expressed in COS-7 cells, RyR1 carrying a deletion of fragment 7 shows a loss of interaction with both peptide A and maurocalcine. At the functional level, this deletion abolishes the maurocalcine induced stimulation of [3H]ryanodine binding onto microsomes of transfected COS-7 cells without affecting the caffeine and ATP responses

Identification of critical elements determining toxins and insecticide affinity, ligand binding domains and channel properties

Insect nicotinic acetylcholine receptors have been objects of attention since the discovery of neonicotinoid insecticides. Mutagenesis studies have revealed that, although the detailed subunit composition of insect nicotinic acetylcholine receptors subtypes eludes us, the framework provided by mutagenesis analysis makes a picture of the subunits involved in the ligand binding and channel properties. In fact, many residues that line the channel or bind to the ligand seemed to be strongly conserved in particular in the N-terminal extracellular region and the second transmembrane domain which constitutes the ion-conducting pathway supporting the flux of ions as well as their discrimination. In fact, the positions are carried by loops B and C, respectively, which contain amino acids directly contributing to the acetylcholine binding site. Mutation ofthese residues accounts for insect resistance to neonicotinoid insecticides such as imidacloprid or a loss ofspecific binding. The discovery of the same mutation at homologous residues in different insect species or its conservation raises the intriguing question of whether a single mutation is essential to generate a resistance phenotype or whether some subunit confer insensitivity to ligand. Consequently, recent finding using information from Torpedo marmorata al subunit and soluble Aplysia californica and Lymnae stagnalis acetylcholine bindingproteins from crystallization suggest that insect nAChR subunits had contributing amino acids in the agonist site structure which participate to affinity and pharmacological properties of these receptors. These new range of data greatly facilitate the understanding of toxin-nAChR interactions and the neonicotinoid binding and selectivity

Characterization of Cofactor-Induced Folding Mechanism of a Zinc Binding Peptide Using Computationally Designed Mutants

Metals are the most commonly encountered protein cofactors, and they play important structural and functional roles in biology. In many cases, metal binding provides a major driving force for a polypeptide chain to fold. While there are many studies on the structure, stability, and function of metal-binding proteins, there are few studies focusing on understanding the kinetic mechanism of metal-induced folding. Herein, the Zn(2+)-induced folding kinetics of a small zinc-binding protein are studied; the CH1(1) peptide is derived from the first cysteine/histidine-rich region (CH1 domain) of the protein interaction domains of the transcriptional coregulator CREB-binding protein. Computational design is used to introduce tryptophan and histidine mutations that are structurally consistent with CH1(1); these mutants are studied using stopped-flow tryptophan fluorescence experiments. The Zn(2+)-induced CH1(1) folding kinetics are consistent with two parallel pathways, where the initial binding of Zn(2+) occurs at two sites. However, the initially formed Zn(2+)-bound complexes can proceed either directly to the folded state where zinc adopts a tetrahedral coordination or to an off-pathway misligated intermediate. While elimination of those ligands responsible for misligation simplifies the folding kinetics, it also leads to a decrease in the zinc binding constant. Therefore, these results suggest why these nonnative zinc ligands in the CH1(1) motif are conserved in several distantly related organisms and why the requirement for function can lead to kinetic frustration in folding. In addition, the loop closure rate of the CH1(1) peptide is determined based on the proposed model and temperature-dependent kinetic measurements

Tertiary Alphabet for the Observable Protein Structural Universe

Here, we systematically decompose the known protein structural universe into its basic elements, which we dub tertiary structural motifs (TERMs). A TERM is a compact backbone fragment that captures the secondary, tertiary, and quaternary environments around a given residue, comprising one or more disjoint segments (three on average). We seek the set of universal TERMs that capture all structure in the Protein Data Bank (PDB), finding remarkable degeneracy. Only ∼600 TERMs are sufficient to describe 50% of the PDB at sub-Angstrom resolution. However, more rare geometries also exist, and the overall structural coverage grows logarithmically with the number of TERMs. We go on to show that universal TERMs provide an effective mapping between sequence and structure. We demonstrate that TERM-based statistics alone are sufficient to recapitulate close-to-native sequences given either NMR or X-ray backbones. Furthermore, sequence variability predicted from TERM data agrees closely with evolutionary variation. Finally, locations of TERMs in protein chains can be predicted from sequence alone based on sequence signatures emergent from TERM instances in the PDB. For multisegment motifs, this method identifies spatially adjacent fragments that are not contiguous in sequence—a major bottleneck in structure prediction. Although all TERMs recur in diverse proteins, some appear specialized for certain functions, such as interface formation, metal coordination, or even water binding. Structural biology has benefited greatly from previously observed degeneracies in structure. The decomposition of the known structural universe into a finite set of compact TERMs offers exciting opportunities toward better understanding, design, and prediction of protein structure

Different Evolutionary Modifications as a Guide to Rewire Two-Component Systems

Two-component systems (TCS) are short signalling pathways generally occurring in prokaryotes. They frequently regulate prokaryotic stimulus responses and thus are also of interest for engineering in biotechnology and synthetic biology. The aim of this study is to better understand and describe rewiring of TCS while investigating different evolutionary scenarios. Based on large-scale screens of TCS in different organisms, this study gives detailed data, concrete alignments, and structure analysis on three general modification scenarios, where TCS were rewired for new responses and functions: (i) exchanges in the sequence within single TCS domains, (ii) exchange of whole TCS domains; (iii) addition of new components modulating TCS function. As a result, the replacement of stimulus and promotor cassettes to rewire TCS is well defined exploiting the alignments given here. The diverged TCS examples are non-trivial and the design is challenging. Designed connector proteins may also be useful to modify TCS in selected cases

Finding Communities in Typed Citation Networks

As the Web has become more and more important to our daily lives, algorithms that can effectively utilize the link structure have become more and more important. One such task has been to find communities in social network data. Recently, however, there has been increased interest in augmenting links with additional semantic information. We examine link classification from the point of view of scientometrics, with an eye towards applying what has been learned about scientific citation to Web linking. Some community detection algorithms are reviewed, and one that has been developed for topical community finding on the Web is adapted to typed scientific citations

Data Enrichment for Data Mining Applied to Bioinformatics and Cheminformatics Domains

Problemas cada vez mais complexos estão a ser tratados na àrea das ciências da vida. A aquisição de todos os dados que possam estar relacionados com o problema em questão é primordial. Igualmente importante é saber como os dados estão relacionados uns com os outros e com o próprio problema. Por outro lado, existem grandes quantidades de dados e informações disponíveis na Web. Os investigadores já estão a utilizar Data Mining e Machine Learning como ferramentas valiosas nas suas investigações, embora o procedimento habitual seja procurar a informação baseada nos modelos indutivos. Até agora, apesar dos grandes sucessos já alcançados com a utilização de Data Mining e Machine Learning, não é fácil integrar esta vasta quantidade de informação disponível no processo indutivo, com algoritmos proposicionais. A nossa principal motivação é abordar o problema da integração de informação de domínio no processo indutivo de técnicas proposicionais de Data Mining e Machine Learning, enriquecendo os dados de treino a serem utilizados em sistemas de programação de lógica indutiva. Os algoritmos proposicionais de Machine Learning são muito dependentes dos atributos dos dados. Ainda é difícil identificar quais os atributos mais adequados para uma determinada tarefa na investigação. É também difícil extrair informação relevante da enorme quantidade de dados disponíveis. Vamos concentrar os dados disponíveis, derivar características que os algoritmos de ILP podem utilizar para induzir descrições, resolvendo os problemas. Estamos a criar uma plataforma web para obter informação relevante para problemas de Bioinformática (particularmente Genómica) e Quimioinformática. Esta vai buscar os dados a repositórios públicos de dados genómicos, proteicos e químicos. Após o enriquecimento dos dados, sistemas Prolog utilizam programação lógica indutiva para induzir regras e resolver casos específicos de Bioinformática e Cheminformática. Para avaliar o impacto do enriquecimento dos dados com ILP, comparamos com os resultados obtidos na resolução dos mesmos casos utilizando algoritmos proposicionais.Increasingly more complex problems are being addressed in life sciences. Acquiring all the data that may be related to the problem in question is paramount. Equally important is to know how the data is related to each other and to the problem itself. On the other hand, there are large amounts of data and information available on the Web. Researchers are already using Data Mining and Machine Learning as a valuable tool in their researches, albeit the usual procedure is to look for the information based on induction models. So far, despite the great successes already achieved using Data Mining and Machine Learning, it is not easy to integrate this vast amount of available information in the inductive process with propositional algorithms. Our main motivation is to address the problem of integrating domain information into the inductive process of propositional Data Mining and Machine Learning techniques by enriching the training data to be used in inductive logic programming systems. The algorithms of propositional machine learning are very dependent on data attributes. It still is hard to identify which attributes are more suitable for a particular task in the research. It is also hard to extract relevant information from the enormous quantity of data available. We will concentrate the available data, derive features that ILP algorithms can use to induce descriptions, solving the problems. We are creating a web platform to obtain relevant bioinformatics (particularly Genomics) and Cheminformatics problems. It fetches the data from public repositories with genomics, protein and chemical data. After the data enrichment, Prolog systems use inductive logic programming to induce rules and solve specific Bioinformatics and Cheminformatics case studies. To assess the impact of the data enrichment with ILP, we compare with the results obtained solving the same cases using propositional algorithms