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      4,6-α-Glucanotransferase activity occurs more widespread in Lactobacillus strains and constitutes a separate GH70 subfamily

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          Family 70 glycoside hydrolase glucansucrase enzymes exclusively occur in lactic acid bacteria and synthesize a wide range of α- d-glucan (abbreviated as α-glucan) oligo- and polysaccharides. Of the 47 characterized GH70 enzymes, 46 use sucrose as glucose donor. A single GH70 enzyme was recently found to be inactive with sucrose and to utilize maltooligosaccharides [(1→4)-α- d-glucooligosaccharides] as glucose donor substrates for α-glucan synthesis, acting as a 4,6-α-glucanotransferase (4,6-αGT) enzyme. Here, we report the characterization of two further GH70 4,6-αGT enzymes, i.e., from Lactobacillus reuteri strains DSM 20016 and ML1, which use maltooligosaccharides as glucose donor. Both enzymes cleave α1→4 glycosidic linkages and add the released glucose moieties one by one to the non-reducing end of growing linear α-glucan chains via α1→6 glycosidic linkages (α1→4 to α1→6 transfer activity). In this way, they convert pure maltooligosaccharide substrates into linear α-glucan product mixtures with about 50% α1→6 glycosidic bonds (isomalto/maltooligosaccharides). These new α-glucan products may provide an exciting type of carbohydrate for the food industry. The results show that 4,6-αGTs occur more widespread in family GH70 and can be considered as a GH70 subfamily. Sequence analysis allowed identification of amino acid residues in acceptor substrate binding subsites +1 and +2, differing between GH70 GTF and 4,6-αGT enzymes.

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          The online version of this article (doi:10.1007/s00253-012-3943-1) contains supplementary material, which is available to authorized users.

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          Most cited references 49

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          Clustal W and Clustal X version 2.0.

          The Clustal W and Clustal X multiple sequence alignment programs have been completely rewritten in C++. This will facilitate the further development of the alignment algorithms in the future and has allowed proper porting of the programs to the latest versions of Linux, Macintosh and Windows operating systems. The programs can be run on-line from the EBI web server: The source code and executables for Windows, Linux and Macintosh computers are available from the EBI ftp site
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            Improved prediction of signal peptides: SignalP 3.0.

            We describe improvements of the currently most popular method for prediction of classically secreted proteins, SignalP. SignalP consists of two different predictors based on neural network and hidden Markov model algorithms, where both components have been updated. Motivated by the idea that the cleavage site position and the amino acid composition of the signal peptide are correlated, new features have been included as input to the neural network. This addition, combined with a thorough error-correction of a new data set, have improved the performance of the predictor significantly over SignalP version 2. In version 3, correctness of the cleavage site predictions has increased notably for all three organism groups, eukaryotes, Gram-negative and Gram-positive bacteria. The accuracy of cleavage site prediction has increased in the range 6-17% over the previous version, whereas the signal peptide discrimination improvement is mainly due to the elimination of false-positive predictions, as well as the introduction of a new discrimination score for the neural network. The new method has been benchmarked against other available methods. Predictions can be made at the publicly available web server
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              Is Open Access

              The Carbohydrate-Active EnZymes database (CAZy): an expert resource for Glycogenomics

              The Carbohydrate-Active Enzyme (CAZy) database is a knowledge-based resource specialized in the enzymes that build and breakdown complex carbohydrates and glycoconjugates. As of September 2008, the database describes the present knowledge on 113 glycoside hydrolase, 91 glycosyltransferase, 19 polysaccharide lyase, 15 carbohydrate esterase and 52 carbohydrate-binding module families. These families are created based on experimentally characterized proteins and are populated by sequences from public databases with significant similarity. Protein biochemical information is continuously curated based on the available literature and structural information. Over 6400 proteins have assigned EC numbers and 700 proteins have a PDB structure. The classification (i) reflects the structural features of these enzymes better than their sole substrate specificity, (ii) helps to reveal the evolutionary relationships between these enzymes and (iii) provides a convenient framework to understand mechanistic properties. This resource has been available for over 10 years to the scientific community, contributing to information dissemination and providing a transversal nomenclature to glycobiologists. More recently, this resource has been used to improve the quality of functional predictions of a number genome projects by providing expert annotation. The CAZy resource resides at URL:

                Author and article information

                [ ]Microbial Physiology, Groningen Biomolecular Sciences and Biotechnology Institute (GBB), University of Groningen, Nijenborgh 7, 9747 AG Groningen, The Netherlands
                [ ]Biophysical Chemistry, Groningen Biomolecular Sciences and Biotechnology Institute (GBB), University of Groningen, Nijenborgh 7, 9747 AG Groningen, The Netherlands
                [ ]Carbohydrate Competence Center (CCC), Nijenborgh 7, 9747 AG Groningen, The Netherlands
                [ ]Genencor, Archimedesweg 30, 2333 CN Leiden, The Netherlands
                +31-50-3632150 , +31-50-3632154 ,
                Appl Microbiol Biotechnol
                Appl. Microbiol. Biotechnol
                Applied Microbiology and Biotechnology
                Springer-Verlag (Berlin/Heidelberg )
                25 February 2012
                25 February 2012
                January 2013
                : 97
                : 1
                : 181-193
                © The Author(s) 2012
                Biotechnologically Relevant Enzymes and Proteins
                Custom metadata
                © Springer-Verlag 2013


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