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      Metagenome-derived SusD-homologs affiliated with Bacteroidota bind to synthetic polymers

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          ABSTRACT

          Starch utilization system (Sus)D-homologs are well known for their carbohydrate-binding capabilities and are part of the sus operon in microorganisms affiliated with the phylum Bacteroidota. Until now, SusD-like proteins have been characterized regarding their affinity toward natural polymers. In this study, three metagenomic SusD homologs (designated SusD1, SusD38489, and SusD70111) were identified and tested with respect to binding to natural and non-natural polymers. SusD1 and SusD38489 are cellulose-binding modules, while SusD70111 preferentially binds chitin. Employing translational fusion proteins with superfolder GFP (sfGFP), pull-down assays, and surface plasmon resonance (SPR) has provided evidence for binding to polyethylene terephthalate (PET) and other synthetic polymers. Structural analysis suggested that a Trp triad might be involved in protein adsorption. Mutation of these residues to Ala resulted in an impaired adsorption to microcrystalline cellulose (MC), but not so to PET and other synthetic polymers. We believe that the characterized SusDs, alongside the methods and considerations presented in this work, will aid further research regarding bioremediation of plastics.

          IMPORTANCE

          SusD1 and SusD38489 can be considered for further applications regarding their putative adsorption toward fossil-fuel based polymers. This is the first time that SusD homologs from the polysaccharide utilization loci (PUL), largely described for the phylum Bacteroidota, are characterized as synthetic polymer-binding proteins.

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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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            UCSF Chimera--a visualization system for exploratory research and analysis.

            The design, implementation, and capabilities of an extensible visualization system, UCSF Chimera, are discussed. Chimera is segmented into a core that provides basic services and visualization, and extensions that provide most higher level functionality. This architecture ensures that the extension mechanism satisfies the demands of outside developers who wish to incorporate new features. Two unusual extensions are presented: Multiscale, which adds the ability to visualize large-scale molecular assemblies such as viral coats, and Collaboratory, which allows researchers to share a Chimera session interactively despite being at separate locales. Other extensions include Multalign Viewer, for showing multiple sequence alignments and associated structures; ViewDock, for screening docked ligand orientations; Movie, for replaying molecular dynamics trajectories; and Volume Viewer, for display and analysis of volumetric data. A discussion of the usage of Chimera in real-world situations is given, along with anticipated future directions. Chimera includes full user documentation, is free to academic and nonprofit users, and is available for Microsoft Windows, Linux, Apple Mac OS X, SGI IRIX, and HP Tru64 Unix from http://www.cgl.ucsf.edu/chimera/. Copyright 2004 Wiley Periodicals, Inc.
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              AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading.

              AutoDock Vina, a new program for molecular docking and virtual screening, is presented. AutoDock Vina achieves an approximately two orders of magnitude speed-up compared with the molecular docking software previously developed in our lab (AutoDock 4), while also significantly improving the accuracy of the binding mode predictions, judging by our tests on the training set used in AutoDock 4 development. Further speed-up is achieved from parallelism, by using multithreading on multicore machines. AutoDock Vina automatically calculates the grid maps and clusters the results in a way transparent to the user. Copyright 2009 Wiley Periodicals, Inc.
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                Author and article information

                Contributors
                Role: Formal analysisRole: InvestigationRole: MethodologyRole: ValidationRole: Writing – original draftRole: Writing – review and editing
                Role: MethodologyRole: ResourcesRole: ValidationRole: Writing – original draftRole: Writing – review and editing
                Role: Data curationRole: Formal analysisRole: ResourcesRole: Writing – original draftRole: Writing – review and editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: SupervisionRole: Writing – original draftRole: Writing – review and editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: SupervisionRole: Writing – original draftRole: Writing – review and editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: ResourcesRole: SupervisionRole: VisualizationRole: Writing – original draftRole: Writing – review and editing
                Role: Editor
                Journal
                Appl Environ Microbiol
                Appl Environ Microbiol
                aem
                Applied and Environmental Microbiology
                American Society for Microbiology (1752 N St., N.W., Washington, DC )
                0099-2240
                1098-5336
                July 2024
                02 July 2024
                02 July 2024
                : 90
                : 7
                : e00933-24
                Affiliations
                [1 ]Department of Microbiology and Biotechnology, University of Hamburg; , Hamburg, Germany
                [2 ]Institute of Molecular Physiology, Johannes-Gutenberg University of Mainz; , Mainz, Germany
                University of Milano-Bicocca; , Milan, Italy
                Author notes
                Address correspondence to Wolfgang R. Streit, wolfgang.streit@ 123456uni-hamburg.de

                The authors declare no conflict of interest.

                Author information
                https://orcid.org/0000-0001-5785-6246
                https://orcid.org/0000-0003-0631-6156
                https://orcid.org/0000-0003-2248-3544
                https://orcid.org/0000-0002-7499-5325
                https://orcid.org/0000-0001-7617-7396
                Article
                00933-24 aem.00933-24
                10.1128/aem.00933-24
                11267923
                38953372
                426c7abf-b238-4b95-8d83-41f2a5ac3d31
                Copyright © 2024 Silverio et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.

                History
                : 17 May 2024
                : 04 June 2024
                Page count
                supplementary-material: 2, authors: 7, Figures: 7, Tables: 3, Equations: 2, References: 80, Pages: 20, Words: 11095
                Funding
                Funded by: Bundesministerium für Bildung und Forschung (BMBF);
                Award ID: (LipoBiocat 031B0837B)
                Award Recipient :
                Funded by: Universität Hamburg (UH);
                Award Recipient :
                Categories
                Biotechnology
                applied-and-industrial-microbiology, Applied and Industrial Microbiology
                Custom metadata
                July 2024

                Microbiology & Virology
                polyethylene terephthalate (pet),bis(2-hydroxyethyl) terephthalate (bhet),low-density polyethylene (ldpe),polyamide 6 (nylon 6; pa6),plastics,cellulose,chitin

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