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      Characterization and structural analysis of the endo-1,4- β-xylanase GH11 from the hemicellulose-degrading Thermoanaerobacterium saccharolyticum useful for lignocellulose saccharification

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          Abstract

          Xylanases are important for the enzymatic breakdown of lignocellulose-based biomass to produce biofuels and other value-added products. We report functional and structural analyses of TsaGH11, an endo-1,4- β-xylanase from the hemicellulose-degrading bacterium, Thermoanaerobacterium saccharolyticum. TsaGH11 was shown to be a thermophilic enzyme that favors acidic conditions with maximum activity at pH 5.0 and 70 °C. It decomposes xylans from beechwood and oat spelts to xylose-containing oligosaccharides with specific activities of 5622.0 and 3959.3 U mg −1, respectively. The kinetic parameters, K m and k cat towards beechwood xylan, are 12.9 mg mL −1 and 34,015.3 s −1, respectively, resulting in k cat /K m value of 2658.7 mL mg −1 s −1, higher by 10 2–10 3 orders of magnitude compared to other reported GH11s investigated with the same substrate, demonstrating its superior catalytic performance. Crystal structures of TsaGH11 revealed a β-jelly roll fold, exhibiting open and close conformations of the substrate-binding site by distinct conformational flexibility to the thumb region of TsaGH11. In the room-temperature structure of TsaGH11 determined by serial synchrotron crystallography, the electron density map of the thumb domain of the TsaGH11 molecule, which does not affect crystal packing, is disordered, indicating that the thumb domain of TsaGH11 has high structural flexibility at room temperature, with the water molecules in the substrate-binding cleft being more disordered than those in the cryogenic structure. These results expand our knowledge of GH11 structural flexibility at room temperature and pave the way for its application in industrial biomass degradation.

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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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            <i>Coot</i> : model-building tools for molecular graphics

            Acta Crystallographica Section D Biological Crystallography, 60(12), 2126-2132
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              SignalP 5.0 improves signal peptide predictions using deep neural networks

              Signal peptides (SPs) are short amino acid sequences in the amino terminus of many newly synthesized proteins that target proteins into, or across, membranes. Bioinformatic tools can predict SPs from amino acid sequences, but most cannot distinguish between various types of signal peptides. We present a deep neural network-based approach that improves SP prediction across all domains of life and distinguishes between three types of prokaryotic SPs.
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                Author and article information

                Contributors
                structure@kookmin.ac.kr
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                13 October 2023
                13 October 2023
                2023
                : 13
                : 17332
                Affiliations
                [1 ]Department of Food Science and Technology, Institute of Agriculture and Life Science, Gyeongsang National University, ( https://ror.org/00saywf64) Jinju, 52828 South Korea
                [2 ]Department of Biotechnology and Enzyme Catalysis, Institute of Biochemistry, University of Greifswald, ( https://ror.org/00r1edq15) Felix-Hausdorff-Str. 4, 17489 Greifswald, Germany
                [3 ]School of Food Science and Biotechnology, Kyungpook National University, ( https://ror.org/040c17130) Daegu, 41566 South Korea
                [4 ]Department of Biotechnology, Graduate School, Korea University, ( https://ror.org/047dqcg40) Seoul, 02841 South Korea
                [5 ]College of General Education, Kookmin University, ( https://ror.org/0049erg63) Seoul, 02707 South Korea
                Article
                44495
                10.1038/s41598-023-44495-8
                10576002
                37833340
                87a0d1f4-ad95-4fda-b559-e0f0f62be2fc
                © Springer Nature Limited 2023

                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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 7 March 2023
                : 9 October 2023
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100003725, National Research Foundation of Korea;
                Award ID: NRF-2022R1I1A1A01072158
                Award ID: NRF-2017M3A9F6029736
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100003627, Rural Development Administration;
                Award ID: PJ01577003
                Award Recipient :
                Funded by: ProGen
                Award ID: ProGen
                Award Recipient :
                Categories
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                © Springer Nature Limited 2023

                Uncategorized
                biochemistry,structural biology
                Uncategorized
                biochemistry, structural biology

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