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      Structure–function characterization reveals new catalytic diversity in the galactose oxidase and glyoxal oxidase family

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          Abstract

          Alcohol oxidases, including carbohydrate oxidases, have a long history of research that has generated fundamental biological understanding and biotechnological applications. Despite a long history of study, the galactose 6-oxidase/glyoxal oxidase family of mononuclear copper-radical oxidases, Auxiliary Activity Family 5 (AA5), is currently represented by only very few characterized members. Here we report the recombinant production and detailed structure–function analyses of two homologues from the phytopathogenic fungi Colletotrichum graminicola and C. gloeosporioides, CgrAlcOx and CglAlcOx, respectively, to explore the wider biocatalytic potential in AA5. EPR spectroscopy and crystallographic analysis confirm a common active-site structure vis-à-vis the archetypal galactose 6-oxidase from Fusarium graminearum. Strikingly, however, CgrAlcOx and CglAlcOx are essentially incapable of oxidizing galactose and galactosides, but instead efficiently catalyse the oxidation of diverse aliphatic alcohols. The results highlight the significant potential of prospecting the evolutionary diversity of AA5 to reveal novel enzyme specificities, thereby informing both biology and applications.

          Abstract

          Auxilliary Activity Family 5 (AA5) comprises mononuclear copper radical oxidases with catalytic diversity that is not well characterised. Here, structural, phylogenetic and biochemical analyses advance our understanding of the potential biological and biotechnology functions of these proteins.

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          Most cited references39

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          Amino acid substitution matrices from protein blocks.

          Methods for alignment of protein sequences typically measure similarity by using a substitution matrix with scores for all possible exchanges of one amino acid with another. The most widely used matrices are based on the Dayhoff model of evolutionary rates. Using a different approach, we have derived substitution matrices from about 2000 blocks of aligned sequence segments characterizing more than 500 groups of related proteins. This led to marked improvements in alignments and in searches using queries from each of the groups.
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            BIONJ: an improved version of the NJ algorithm based on a simple model of sequence data.

            O Gascuel (1997)
            We propose an improved version of the neighbor-joining (NJ) algorithm of Saitou and Nei. This new algorithm, BIONJ, follows the same agglomerative scheme as NJ, which consists of iteratively picking a pair of taxa, creating a new mode which represents the cluster of these taxa, and reducing the distance matrix by replacing both taxa by this node. Moreover, BIONJ uses a simple first-order model of the variances and covariances of evolutionary distance estimates. This model is well adapted when these estimates are obtained from aligned sequences. At each step it permits the selection, from the class of admissible reductions, of the reduction which minimizes the variance of the new distance matrix. In this way, we obtain better estimates to choose the pair of taxa to be agglomerated during the next steps. Moreover, in comparison with NJ's estimates, these estimates become better and better as the algorithm proceeds. BIONJ retains the good properties of NJ--especially its low run time. Computer simulations have been performed with 12-taxon model trees to determine BIONJ's efficiency. When the substitution rates are low (maximum pairwise divergence approximately 0.1 substitutions per site) or when they are constant among lineages, BIONJ is only slightly better than NJ. When the substitution rates are higher and vary among lineages,BIONJ clearly has better topological accuracy. In the latter case, for the model trees and the conditions of evolution tested, the topological error reduction is on the average around 20%. With highly-varying-rate trees and with high substitution rates (maximum pairwise divergence approximately 1.0 substitutions per site), the error reduction may even rise above 50%, while the probability of finding the correct tree may be augmented by as much as 15%.
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              A graphical user interface to the CCP4 program suite.

              CCP4i is a graphical user interface that makes running programs from the CCP4 suite simpler and quicker. It is particularly directed at inexperienced users and tightly linked to introductory and scientific documentation. It also provides a simple project-management system and visualization tools. The system is readily extensible and not specific to CCP4 software.
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                Author and article information

                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group
                2041-1723
                18 December 2015
                2015
                : 6
                : 10197
                Affiliations
                [1 ]Michael Smith Laboratories and Department of Chemistry, University of British Columbia , 2185 East Mall, Vancouver, British Columbia, Canada V6T 1Z4
                [2 ]Department of Chemistry, University of York , Heslington, York YO10 5DD, UK
                [3 ]Institut des Sciences Moléculaires de Marseille—Team BiosCiences UMR 7313-CNRS, Aix-Marseille University , Avenue Escadrille Normandie Niemen, Marseille 13397, France
                [4 ]INRA, UMR1163 Biodiversité et Biotechnologie Fongiques Marseille F-13288, France
                [5 ]Architecture et Fonction des Macromolécules Biologiques, CNRS—Aix-Marseille University , 163 Avenue de Luminy, Marseille 13288, France
                [6 ]INRA, USC 1408 AFMB , Marseille 13288, France
                [7 ]Department of Biological Sciences, King Abdulaziz University , Jeddah, Saudi Arabia
                Author notes
                [*]

                These authors contributed equally to this work.

                Article
                ncomms10197
                10.1038/ncomms10197
                4703870
                26680532
                ec4ad570-776f-4d2e-bc87-1d2a4791be97
                Copyright © 2015, Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/

                History
                : 23 June 2015
                : 16 November 2015
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