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      An intron endonuclease facilitates interference competition between co-infecting viruses

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          Abstract

          Introns containing homing endonucleases are widespread in nature and have long been assumed to be selfish elements that provide no benefit to the host organism. These genetic elements are common in viruses, but whether they confer a selective advantage is unclear. Here we studied an intron-encoded homing endonuclease in bacteriophage ΦPA3 and found that it contributes to viral competition by interfering with the replication of a co-infecting phage, ΦKZ. We show that gp210 targets a specific sequence in ΦKZ, which prevents the assembly of progeny viruses. This work demonstrates how a homing endonuclease can be deployed to engage in interference competition among viruses and provide a relative fitness advantage. Given the ubiquity of homing endonucleases, this selective advantage likely has widespread evolutionary implications in diverse plasmid and viral competition as well as virus-host interactions.

          Summary

          A homing endonuclease provides a relative fitness advantage during viral competition.

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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 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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              ColabFold: making protein folding accessible to all

              ColabFold offers accelerated prediction of protein structures and complexes by combining the fast homology search of MMseqs2 with AlphaFold2 or RoseTTAFold. ColabFold’s 40−60-fold faster search and optimized model utilization enables prediction of close to 1,000 structures per day on a server with one graphics processing unit. Coupled with Google Colaboratory, ColabFold becomes a free and accessible platform for protein folding. ColabFold is open-source software available at https://github.com/sokrypton/ColabFold and its novel environmental databases are available at https://colabfold.mmseqs.com . ColabFold is a free and accessible platform for protein folding that provides accelerated prediction of protein structures and complexes using AlphaFold2 or RoseTTAFold.
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                Author and article information

                Journal
                0404511
                7473
                Science
                Science
                Science (New York, N.Y.)
                0036-8075
                1095-9203
                9 November 2024
                05 July 2024
                04 July 2024
                05 December 2024
                : 385
                : 6704
                : 105-112
                Affiliations
                [1 ]Department of Molecular Biology, University of California, San Diego, La Jolla, CA
                [2 ]Department of Ecology, Behavior and Evolution, University of California, San Diego, La Jolla, CA
                [3 ]Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA
                [4 ]Howard Hughes Medical Institute, University of California, San Diego, La Jolla, CA
                Author notes
                [5]

                These authors contributed equally to this work

                Author Contributions

                E.A.B., C.M., T.G.L., R.K.L., A.P., S.R., G.N.M, J.L., E.A., and S.S. conducted experiments. E.A.B., C.M., T.G.L., J.R.M., and K.D.C. analyzed data and created figures. E.A.B. and J.P. conceptualized the original manuscript. J.R.M., K.P., E.V., K.D.C., and J.P. supervised the work, provided feedback on the manuscript, and secured funding.

                []To whom correspondence should be addressed: jpogliano@ 123456ucsd.edu
                Article
                NIHMS2034457
                10.1126/science.adl1356
                11620839
                38963841
                bcb3555b-d3b1-4a29-8fd2-70cc680e67d2

                This work is licensed under a Creative Commons Attribution 4.0 International License, which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.

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