214,460 research outputs found

    Purification and characterization of UDP-glucose: hydroxycoumarin 7-O-glucosyltransferase, with broad substrate specificity from tobacco cultured cells

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    The enzyme UDP-glucose: hydroxycoumarin 7-O-glucosyltransferase (CGTase), which catalyzes the formation of scopolin from scopoletin, was purified approximately 1200-fold from a culture of 2,4-D-treated tobacco cells (Nicotiana tabacum L. cv. Bright Yellow T-13) with a yield of 7%. Purification to apparent homogeneity, as judged by SDS-PAGE, was achieved by sequential anion-exchange chromatography, hydroxyapatite chromatography, gel filtration, a second round of anion-exchange chromatography, and affinity chromatography on UDP-glucuronic acid agarose. The purified enzyme had a pH optimum of 7.5, an isoelectric point (pI) of 5.0, and a molecular mass of 49 kDa. The enzyme did not require metal cofactors for activity. Its activity was inhibited by Zn2+, Co2+ and Cu2+ ions, as well as by SH-blocking reagents. The K-m values for UDP-glucose, scopoletin and esculetin were 43, 150 and 25 mu M. respectively. A study of the initial rate of the reaction suggested that the reaction proceeded via a sequential mechanism. The purified enzyme preferred hydroxycoumarins as substrates but also exhibited significant activity with flavonoids. A database search using the amino terminus amino acid sequence of CGTase revealed strong homology to the amino acid sequences of other glucosyltransferases in plants.ArticlePlant Science. 157(1):105-112 (2000)journal articl

    The FH mutation database: an online database of fumarate hydratase mutations involved in the MCUL (HLRCC) tumor syndrome and congenital fumarase deficiency

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    <p>Abstract</p> <p>Background</p> <p>Fumarate hydratase (HGNC approved gene symbol – <it>FH</it>), also known as fumarase, is an enzyme of the tricarboxylic acid (TCA) cycle, involved in fundamental cellular energy production. First described by Zinn <it>et al </it>in 1986, deficiency of FH results in early onset, severe encephalopathy. In 2002, the Multiple Leiomyoma Consortium identified heterozygous germline mutations of <it>FH </it>in patients with multiple cutaneous and uterine leiomyomas, (MCUL: OMIM 150800). In some families renal cell cancer also forms a component of the complex and as such has been described as hereditary leiomyomatosis and renal cell cancer (HLRCC: OMIM 605839). The identification of FH as a tumor suppressor was an unexpected finding and following the identification of subunits of succinate dehydrogenase in 2000 and 2001, was only the second description of the involvement of an enzyme of intermediary metabolism in tumorigenesis.</p> <p>Description</p> <p>The <it>FH </it>mutation database is a part of the TCA cycle gene mutation database (formerly the succinate dehydrogenase gene mutation database) and is based on the Leiden Open (source) Variation Database (LOVD) system. The variants included in the database were derived from the published literature and annotated to conform to current mutation nomenclature. The <it>FH </it>database applies HGVS nomenclature guidelines, and will assist researchers in applying these guidelines when directly submitting new sequence variants online. Since the first molecular characterization of an <it>FH </it>mutation by Bourgeron <it>et al </it>in 1994, a series of reports of both FH deficiency patients and patients with MCUL/HLRRC have described 107 variants, of which 93 are thought to be pathogenic. The most common type of mutation is missense (57%), followed by frameshifts & nonsense (27%), and diverse deletions, insertions and duplications. Here we introduce an online database detailing all reported <it>FH </it>sequence variants.</p> <p>Conclusion</p> <p>The <it>FH </it>mutation database strives to systematically unify all current genetic knowledge of <it>FH </it>variants. We believe that this knowledge will assist clinical geneticists and treating physicians when advising patients and their families, will provide a rapid and convenient resource for research scientists, and may eventually assist in gaining novel insights into FH and its related clinical syndromes.</p

    MACiE: a database of enzyme reaction mechanisms.

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    SUMMARY: MACiE (mechanism, annotation and classification in enzymes) is a publicly available web-based database, held in CMLReact (an XML application), that aims to help our understanding of the evolution of enzyme catalytic mechanisms and also to create a classification system which reflects the actual chemical mechanism (catalytic steps) of an enzyme reaction, not only the overall reaction. AVAILABILITY: http://www-mitchell.ch.cam.ac.uk/macie/.EPSRC (G.L.H. and J.B.O.M.), the BBSRC (G.J.B. and J.M.T.—CASE studentship in association with Roche Products Ltd; N.M.O.B. and J.B.O.M.—grant BB/C51320X/1), the Chilean Government’s Ministerio de Planificacio´n y Cooperacio´n and Cambridge Overseas Trust (D.E.A.) for funding and Unilever for supporting the Centre for Molecular Science Informatics.application note restricted to 2 printed pages web site: http://www-mitchell.ch.cam.ac.uk/macie

    Complex networks theory for analyzing metabolic networks

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    One of the main tasks of post-genomic informatics is to systematically investigate all molecules and their interactions within a living cell so as to understand how these molecules and the interactions between them relate to the function of the organism, while networks are appropriate abstract description of all kinds of interactions. In the past few years, great achievement has been made in developing theory of complex networks for revealing the organizing principles that govern the formation and evolution of various complex biological, technological and social networks. This paper reviews the accomplishments in constructing genome-based metabolic networks and describes how the theory of complex networks is applied to analyze metabolic networks.Comment: 13 pages, 2 figure

    Identification of functionally related enzymes by learning-to-rank methods

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    Enzyme sequences and structures are routinely used in the biological sciences as queries to search for functionally related enzymes in online databases. To this end, one usually departs from some notion of similarity, comparing two enzymes by looking for correspondences in their sequences, structures or surfaces. For a given query, the search operation results in a ranking of the enzymes in the database, from very similar to dissimilar enzymes, while information about the biological function of annotated database enzymes is ignored. In this work we show that rankings of that kind can be substantially improved by applying kernel-based learning algorithms. This approach enables the detection of statistical dependencies between similarities of the active cleft and the biological function of annotated enzymes. This is in contrast to search-based approaches, which do not take annotated training data into account. Similarity measures based on the active cleft are known to outperform sequence-based or structure-based measures under certain conditions. We consider the Enzyme Commission (EC) classification hierarchy for obtaining annotated enzymes during the training phase. The results of a set of sizeable experiments indicate a consistent and significant improvement for a set of similarity measures that exploit information about small cavities in the surface of enzymes

    Mining protein database using machine learning techniques

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    With a large amount of information relating to proteins accumulating in databases widely available online, it is of interest to apply machine learning techniques that, by extracting underlying statistical regularities in the data, make predictions about the functional and evolutionary characteristics of unseen proteins. Such predictions can help in achieving a reduction in the space over which experiment designers need to search in order to improve our understanding of the biochemical properties. Previously it has been suggested that an integration of features computable by comparing a pair of proteins can be achieved by an artificial neural network, hence predicting the degree to which they may be evolutionary related and homologous. We compiled two datasets of pairs of proteins, each pair being characterised by seven distinct features. We performed an exhaustive search through all possible combinations of features, for the problem of separating remote homologous from analogous pairs, we note that significant performance gain was obtained by the inclusion of sequence and structure information. We find that the use of a linear classifier was enough to discriminate a protein pair at the family level. However, at the superfamily level, to detect remote homologous pairs was a relatively harder problem. We find that the use of nonlinear classifiers achieve significantly higher accuracies. In this paper, we compare three different pattern classification methods on two problems formulated as detecting evolutionary and functional relationships between pairs of proteins, and from extensive cross validation and feature selection based studies quantify the average limits and uncertainties with which such predictions may be made. Feature selection points to a "knowledge gap" in currently available functional annotations. We demonstrate how the scheme may be employed in a framework to associate an individual protein with an existing family of evolutionarily related proteins
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