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      Plastid ancestors lacked a complete Entner-Doudoroff pathway, limiting plants to glycolysis and the pentose phosphate pathway

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          Abstract

          The Entner–Doudoroff (ED) pathway provides an alternative to glycolysis. It converts 6-phosphogluconate (6-PG) to glyceraldehyde-3-phosphate and pyruvate in two steps consisting of a dehydratase (EDD) and an aldolase (EDA). Here, we investigate its distribution and significance in higher plants and determine the ED pathway is restricted to prokaryotes due to the absence of EDD genes in eukaryotes. EDDs share a common origin with dihydroxy-acid dehydratases (DHADs) of the branched chain amino acid pathway (BCAA). Each dehydratase features strict substrate specificity. E. coli EDD dehydrates 6-PG to 2-keto-3-deoxy-6-phosphogluconate, while DHAD only dehydrates substrates from the BCAA pathway. Structural modeling identifies two divergent domains which account for their non-overlapping substrate affinities. Coupled enzyme assays confirm only EDD participates in the ED pathway. Plastid ancestors lacked EDD but transferred metabolically promiscuous EDA, which explains the absence of the ED pathway from the Viridiplantae and sporadic persistence of EDA genes across the plant kingdom.

          Abstract

          The Enter-Doudoroff (ED) pathway is an alternative to glycolysis present in some prokaryotes. Evans et al. show that its dehydratase enzyme, evolved from a branched chain amino acid pathway paralog, acquired a new function through mutations in its active site.

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          Highly accurate protein structure prediction with AlphaFold

          Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
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            AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility.

            We describe the testing and release of AutoDock4 and the accompanying graphical user interface AutoDockTools. AutoDock4 incorporates limited flexibility in the receptor. Several tests are reported here, including a redocking experiment with 188 diverse ligand-protein complexes and a cross-docking experiment using flexible sidechains in 87 HIV protease complexes. We also report its utility in analysis of covalently bound ligands, using both a grid-based docking method and a modification of the flexible sidechain technique. (c) 2009 Wiley Periodicals, Inc.
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              UCSF ChimeraX : Structure visualization for researchers, educators, and developers

              UCSF ChimeraX is the next-generation interactive visualization program from the Resource for Biocomputing, Visualization, and Informatics (RBVI), following UCSF Chimera. ChimeraX brings (a) significant performance and graphics enhancements; (b) new implementations of Chimera's most highly used tools, many with further improvements; (c) several entirely new analysis features; (d) support for new areas such as virtual reality, light-sheet microscopy, and medical imaging data; (e) major ease-of-use advances, including toolbars with icons to perform actions with a single click, basic "undo" capabilities, and more logical and consistent commands; and (f) an app store for researchers to contribute new tools. ChimeraX includes full user documentation and is free for noncommercial use, with downloads available for Windows, Linux, and macOS from https://www.rbvi.ucsf.edu/chimerax.
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                Author and article information

                Contributors
                michaelandrew.phillips@utoronto.ca
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                6 February 2024
                6 February 2024
                2024
                : 15
                : 1102
                Affiliations
                [1 ]Department of Cell and Systems Biology, University of Toronto, ( https://ror.org/03dbr7087) Toronto, ON M5S 3G5 Canada
                [2 ]Department of Biology, University of Toronto—Mississauga, ( https://ror.org/03dbr7087) Mississauga, ON L5L 1C6 Canada
                Author information
                http://orcid.org/0000-0001-6775-1149
                http://orcid.org/0000-0002-4465-858X
                http://orcid.org/0000-0002-4071-3166
                http://orcid.org/0000-0002-6082-9764
                http://orcid.org/0000-0001-7276-119X
                Article
                45384
                10.1038/s41467-024-45384-y
                10847513
                38321044
                24276e0f-18e8-413e-b48e-1275a8c249cc
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 17 August 2023
                : 20 January 2024
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100000038, Gouvernement du Canada | Natural Sciences and Engineering Research Council of Canada (Conseil de Recherches en Sciences Naturelles et en Génie du Canada);
                Award ID: RGPIN-2017-06400
                Award ID: RGPIN-2023-05615
                Award Recipient :
                Funded by: Gouvernement du Canada | Natural Sciences and Engineering Research Council of Canada (Conseil de Recherches en Sciences Naturelles et en Génie du Canada)
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2024

                Uncategorized
                metabolic pathways,plant evolution,chloroplasts
                Uncategorized
                metabolic pathways, plant evolution, chloroplasts

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