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      Mechanism of single-stranded DNA annealing by RAD52–RPA complex

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          Abstract

          RAD52 is important for the repair of DNA double-stranded breaks 1, 2 , mitotic DNA synthesis 35 and alternative telomere length maintenance 6, 7 . Central to these functions, RAD52 promotes the annealing of complementary single-stranded DNA (ssDNA) 8, 9 and provides an alternative to BRCA2/RAD51-dependent homologous recombination repair 10 . Inactivation of RAD52 in homologous-recombination-deficient BRCA1- or BRCA2-defective cells is synthetically lethal 11, 12 , and aberrant expression of RAD52 is associated with poor cancer prognosis 13, 14 . As a consequence, RAD52 is an attractive therapeutic target against homologous-recombination-deficient breast, ovarian and prostate cancers 1517 . Here we describe the structure of RAD52 and define the mechanism of annealing. As reported previously 1820 , RAD52 forms undecameric (11-subunit) ring structures, but these rings do not represent the active form of the enzyme. Instead, cryo-electron microscopy and biochemical analyses revealed that ssDNA annealing is driven by RAD52 open rings in association with replication protein-A (RPA). Atomic models of the RAD52–ssDNA complex show that ssDNA sits in a positively charged channel around the ring. Annealing is driven by the RAD52 N-terminal domains, whereas the C-terminal regions modulate the open-ring conformation and RPA interaction. RPA associates with RAD52 at the site of ring opening with critical interactions occurring between the RPA-interacting domain of RAD52 and the winged helix domain of RPA2. Our studies provide structural snapshots throughout the annealing process and define the molecular mechanism of ssDNA annealing by the RAD52–RPA complex.

          Abstract

          Single-stranded DNA annealing is driven by RAD52 open rings in association with RPA.

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          Fiji: an open-source platform for biological-image analysis.

          Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.
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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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              cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination

              A software tool, cryoSPARC, addresses the speed bottleneck in cryo-EM image processing, enabling automated macromolecular structure determination in hours on a desktop computer without requiring a starting model.
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                Author and article information

                Contributors
                eric.liang@crick.ac.uk
                stephen.west@crick.ac.uk
                Journal
                Nature
                Nature
                Nature
                Nature Publishing Group UK (London )
                0028-0836
                1476-4687
                24 April 2024
                24 April 2024
                2024
                : 629
                : 8012
                : 697-703
                Affiliations
                [1 ]The Francis Crick Institute, ( https://ror.org/04tnbqb63) London, UK
                [2 ]Present Address: Abcam, Cambridge Biomedical Campus, ( https://ror.org/05wzrja17) Cambridge, UK
                Author information
                http://orcid.org/0000-0002-5326-7003
                http://orcid.org/0000-0002-2963-5902
                http://orcid.org/0000-0002-2432-0901
                http://orcid.org/0000-0002-6763-3852
                http://orcid.org/0000-0001-8848-9418
                Article
                7347
                10.1038/s41586-024-07347-7
                11096129
                38658755
                8e4e0940-cdb7-426d-83a4-04269a9c284e
                © The Author(s) 2024

                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
                : 6 June 2023
                : 22 March 2024
                Categories
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                Custom metadata
                © Springer Nature Limited 2024

                Uncategorized
                cryoelectron microscopy,dna,double-strand dna breaks
                Uncategorized
                cryoelectron microscopy, dna, double-strand dna breaks

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