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      Prokaryotic innate immunity through pattern recognition of conserved viral proteins

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          Abstract

          Many organisms have evolved specialized immune pattern-recognition receptors, including nucleotide-binding oligomerization domain–like receptors (NLRs) of the STAND superfamily that are ubiquitous in plants, animals, and fungi. Although the roles of NLRs in eukaryotic immunity are well established, it is unknown whether prokaryotes use similar defense mechanisms. Here, we show that antiviral STAND (Avs) homologs in bacteria and archaea detect hallmark viral proteins, triggering Avs tetramerization and the activation of diverse N-terminal effector domains, including DNA endonucleases, to abrogate infection. Cryo–electron microscopy reveals that Avs sensor domains recognize conserved folds, active-site residues, and enzyme ligands, allowing a single Avs receptor to detect a wide variety of viruses. These findings extend the paradigm of pattern recognition of pathogen-specific proteins across all three domains of life.

          STAND against viral invaders

          The innate immune systems of animals, plants, and fungi universally use nucleotide binding oligomerization domain–like receptors (NLRs) of the STAND superfamily to detect molecular patterns common to pathogens. Gao et al . show that NLR-based immune pattern recognition is also prevalent in bacteria and archaea, something that was not known before. In particular, the authors characterized four families of NLR-like genes, finding that they are specific sensors for two highly conserved bacteriophage proteins. Upon binding to the target, these NLRs activate diverse effector domains, including nucleases, to prevent phage propagation. These findings demonstrate that pattern recognition of pathogen-specific proteins is a common mechanism of immunity across all domains of life. —DJ

          Abstract

          Bacteria and archaea have innate immune receptors that recognize conserved viral proteins and prevent infection.

          Abstract

          INTRODUCTION

          Many organisms have evolved specialized immune pattern-recognition receptors, including nucleotide-binding oligomerization domain-like receptors (NLRs) of the STAND superfamily that are ubiquitous in plants, animals, and fungi. NLRs oligomerize upon recognition of pathogen-associated molecular patterns, leading to the activation of an effector domain that mediates an inflammatory or cell death response. Although the roles of NLRs in eukaryotic immunity are well established, it is unknown whether prokaryotes use similar defense mechanisms.

          RATIONALE

          We previously identified a set of bacterial and archaeal STAND nucleoside triphosphatases (NTPases), dubbed Avs (antiviral STAND), that protect bacteria from tailed phages through an unknown mechanism. Like eukaryotic NLRs, Avs proteins have a characteristic tripartite domain architecture consisting of a central NTPase, an extended C-terminal sensor, and an N-terminal effector. Here, we investigate the defense mechanism of these Avs proteins.

          RESULTS

          Using genetic screens in Escherichia coli , we characterized four Avs families (Avs1 to Avs4) and found that they detect hallmark viral proteins that are expressed during infection. In particular, Avs1 to Avs3 recognize the large terminase subunit, and Avs4 recognizes the portal. These two proteins together make up the conserved DNA packaging machinery of tailed phages. Coexpression of an Avs protein with its cognate target in E. coli resulted in cell death.

          We assessed the specificity of Avs target recognition with a panel of terminases and portals from 24 tailed phages, spanning nine major families. Notably, a single Avs protein was capable of recognizing a large variety of targets (terminase or portal), with less than 5% sequence identity in some cases.

          We next reconstituted Avs activity in vitro, focusing on representatives from Salmonella enterica (SeAvs3) and E. coli (EcAvs4), both of which contain N-terminal PD-DExK nuclease effectors. In the presence of their cognate target, SeAvs3 and EcAvs4 mediated degradation of double-stranded DNA. Nuclease activity required the presence of Mg 2+ and adenosine triphosphate (ATP); however, the hydrolysis of ATP was not strictly required. Single-stranded DNA and RNA substrates were not cleaved.

          We determined the cryo–electron microscopy structures of the SeAvs3-terminase and EcAvs4-portal complexes, revealing that both form tetramers in which the C-terminal sensor domain of each Avs subunit binds to a single target protein. Binding is mediated by shape complementarity across an extended interface, consistent with fold recognition. Additionally, SeAvs3 directly recognizes terminase active-site residues and its ATP ligand. Tetramerization of both SeAvs3 and EcAvs4 is mediated by their STAND ATPase domains and allows the N-terminal nucleases to adopt active dimeric configurations.

          Bioinformatic analysis of Avs proteins across prokaryotic lineages revealed at least 18 distinct types of N-terminal effectors that are modularly swapped between Avs homologs, as well as widespread distribution of avs genes across phyla with extensive horizontal gene transfer. Finally, we also discovered phage-encoded Avs inhibitors, highlighting an extensive arms race between prokaryotes and viruses.

          CONCLUSION

          NLR-like defense proteins in bacteria and archaea recognize the conserved folds of hallmark viral proteins and assemble into tetramers that activate diverse N-terminal effectors. The mechanism of these proteins highlights the similarity between the defense strategies of prokaryotes and eukaryotes and extends the paradigm of pattern recognition of pathogen-specific proteins across all three domains of life.

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          Most cited references80

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          Is Open Access

          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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            Is Open Access

            IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies

            Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3-97.1%.
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              Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.

              S Altschul (1997)
              The BLAST programs are widely used tools for searching protein and DNA databases for sequence similarities. For protein comparisons, a variety of definitional, algorithmic and statistical refinements described here permits the execution time of the BLAST programs to be decreased substantially while enhancing their sensitivity to weak similarities. A new criterion for triggering the extension of word hits, combined with a new heuristic for generating gapped alignments, yields a gapped BLAST program that runs at approximately three times the speed of the original. In addition, a method is introduced for automatically combining statistically significant alignments produced by BLAST into a position-specific score matrix, and searching the database using this matrix. The resulting Position-Specific Iterated BLAST (PSI-BLAST) program runs at approximately the same speed per iteration as gapped BLAST, but in many cases is much more sensitive to weak but biologically relevant sequence similarities. PSI-BLAST is used to uncover several new and interesting members of the BRCT superfamily.
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                Author and article information

                Contributors
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                Journal
                Science
                Science
                American Association for the Advancement of Science (AAAS)
                0036-8075
                1095-9203
                August 12 2022
                August 12 2022
                : 377
                : 6607
                Affiliations
                [1 ]Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
                [2 ]Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
                [3 ]McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
                [4 ]Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
                [5 ]Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
                [6 ]National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.
                Article
                10.1126/science.abm4096
                35951700
                b974236b-4374-4149-a2ae-efc4aab39582
                © 2022
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