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      PAAR Proteins Are Versatile Clips That Enrich the Antimicrobial Weapon Arsenals of Prokaryotes

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          ABSTRACT

          Protein toxins secreted by prokaryotes have been found to affect the pathogenicity of pathogens or directly mediate antagonistic interactions between prokaryotes. PAAR proteins are important carriers of toxic effectors and are located at the forefront of either the type VI secretion system (T6SS) or the extracellular contractile injection system (eCIS). This study systematically investigated PAAR homologues and related toxic effectors. We found that PAAR homologues were divided into 8 types and 16 subtypes and distributed in 23.1% of bacterial genomes and 7.8% of archaeal genomes. PAAR proteins of all types fold into a highly similar conical structure, even from relatively diverse underlying sequences. PAAR homologues associated with different secretion systems display a mixed phylogenetic relationship, indicating that PAAR proteins from such a subtype can be assembled on either a T6SS or an eCIS. More than 1,300 PAAR-related toxic effector genes were identified; one PAAR subtype can be associated with toxins of over 40 families, and toxins from one family can be associated with more than 10 PAAR subtypes. A large-scale comparison of Earth Microbiome Project data and prokaryotic genomes revealed that prokaryotes encoding PAAR genes are widely present in diverse environments worldwide, and taxa encoding multiple PAAR gene copies exhibit a wider distribution in environments than other taxa. Overall, our studies highlighted that PAAR proteins are versatile clips loaded with antimicrobial toxin bullets for secretion weapons (T6SS and eCIS), greatly enriching the weapon arsenal of prokaryotes, which, often together with VgrG, help prokaryotes fight for survival advantages in crowded environments.

          IMPORTANCE Infectious diseases caused by microbial pathogens are severe threats to human health and economic development. To respond to these threats, it is necessary to understand how microorganisms survive in and adapt to complex environments. Microorganic toxins, which are widely distributed in nature, are the key weapons in life domain interactions. PAAR proteins are important carriers of prokaryotic toxic effectors. We reveal the versatility of PAAR proteins between secretory systems and the massive diversity of toxic effectors carried by PAAR proteins, which helps prokaryotes enrich their arsenal and expand their ability to attack their neighbors. A large number of PAAR homologues and related toxic effectors enhance the survival competitiveness of prokaryotic populations. In conclusion, our work provides an example for large-scale analysis of the global distribution and ecological functions of prokaryotic functional genes.

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          MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability

          We report a major update of the MAFFT multiple sequence alignment program. This version has several new features, including options for adding unaligned sequences into an existing alignment, adjustment of direction in nucleotide alignment, constrained alignment and parallel processing, which were implemented after the previous major update. This report shows actual examples to explain how these features work, alone and in combination. Some examples incorrectly aligned by MAFFT are also shown to clarify its limitations. We discuss how to avoid misalignments, and our ongoing efforts to overcome such limitations.
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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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              VMD: Visual molecular dynamics

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                Author and article information

                Contributors
                Role: Editor
                Journal
                mSystems
                mSystems
                msystems
                mSystems
                American Society for Microbiology (1752 N St., N.W., Washington, DC )
                2379-5077
                7 December 2021
                Nov-Dec 2021
                7 December 2021
                : 6
                : 6
                : e00953-21
                Affiliations
                [a ] State Key Laboratory of Microbial Technology, Institute of Microbial Technology, Shandong Universitygrid.27255.37, , Qingdao, China
                [b ] Suzhou Research Institute, Shandong Universitygrid.27255.37, , Suzhou, China
                Ocean University of China
                Author notes

                Zheng Zhang and Ya Liu contributed equally to this article. Author order was determined in order of decreasing seniority.

                The authors declare no conflict of interest.

                For a commentary on this article, see https://doi.org/10.1128/mSystems.01386-21.

                Author information
                https://orcid.org/0000-0001-9971-6006
                https://orcid.org/0000-0001-8336-6638
                Article
                00953-21 msystems.00953-21
                10.1128/mSystems.00953-21
                8651086
                34874775
                8360c017-bf94-4dfd-8191-bed846d691c5
                Copyright © 2021 Zhang et al.

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

                History
                : 21 July 2021
                : 25 October 2021
                Page count
                supplementary-material: 10, Figures: 6, Tables: 0, Equations: 0, References: 47, Pages: 15, Words: 9378
                Funding
                Funded by: Special Investigation on Scientific and Technological Basic Resources;
                Award ID: 2017FY100300
                Award Recipient :
                Funded by: National Key Research and Development Program;
                Award ID: 2018YFA0900400
                Award ID: 2018YFA0901704
                Award Recipient :
                Funded by: Key Research and Development Program of Shandong Province;
                Award ID: 2018GSF121015
                Award Recipient :
                Funded by: Fundamental Research Funds of Shandong University;
                Award ID: 2020GN113
                Award Recipient :
                Funded by: National Natural Science Foundation of China (NSFC), FundRef https://doi.org/10.13039/501100001809;
                Award ID: 32070030
                Award Recipient :
                Funded by: Natural Science Foundation of Jiangsu Province (Jiangsu Natural Science Foundation), FundRef https://doi.org/10.13039/501100004608;
                Award ID: BK20190199
                Award Recipient :
                Categories
                Research Article
                microbial-ecology, Microbial Ecology
                Custom metadata
                November/December 2021

                paar protein,toxic effector,contractile injection system,cis,prokaryotic genomes,earth microbiome project,emp

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