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      DNA segment capture by Smc5/6 holocomplexes

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          Abstract

          Three distinct structural maintenance of chromosomes (SMC) complexes facilitate chromosome folding and segregation in eukaryotes, presumably by DNA loop extrusion. How SMCs interact with DNA to extrude loops is not well understood. Among the SMC complexes, Smc5/6 has dedicated roles in DNA repair and preventing a buildup of aberrant DNA junctions. In the present study, we describe the reconstitution of ATP-dependent DNA loading by yeast Smc5/6 rings. Loading strictly requires the Nse5/6 subcomplex which opens the kleisin neck gate. We show that plasmid molecules are topologically entrapped in the kleisin and two SMC subcompartments, but not in the full SMC compartment. This is explained by the SMC compartment holding a looped DNA segment and by kleisin locking it in place when passing between the two flanks of the loop for neck-gate closure. Related segment capture events may provide the power stroke in subsequent DNA extrusion steps, possibly also in other SMC complexes, thus providing a unifying principle for DNA loading and extrusion.

          Abstract

          Here, the authors biochemically demonstrate how the Smc5/6 SMC compartment holds a DNA loop by topologically entrapping DNA in two SMC subcompartments and the kleisin compartment. This mechanism requires the Nse5/6 loader to open the neck gate before DNA entrance.

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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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              Protein complex prediction with AlphaFold-Multimer

              While the vast majority of well-structured single protein chains can now be predicted to high accuracy due to the recent AlphaFold [1] model, the prediction of multi-chain protein complexes remains a challenge in many cases. In this work, we demonstrate that an AlphaFold model trained specifically for multimeric inputs of known stoichiometry, which we call AlphaFold-Multimer, significantly increases accuracy of predicted multimeric interfaces over input-adapted single-chain AlphaFold while maintaining high intra-chain accuracy. On a benchmark dataset of 17 heterodimer proteins without templates (introduced in [2]) we achieve at least medium accuracy (DockQ [3] ≥ 0.49) on 14 targets and high accuracy (DockQ ≥ 0.8) on 6 targets, compared to 9 targets of at least medium accuracy and 4 of high accuracy for the previous state of the art system (an AlphaFold-based system from [2]). We also predict structures for a large dataset of 4,433 recent protein complexes, from which we score all non-redundant interfaces with low template identity. For heteromeric interfaces we successfully predict the interface (DockQ ≥ 0.23) in 67% of cases, and produce high accuracy predictions (DockQ ≥ 0.8) in 23% of cases, an improvement of +25 and +11 percentage points over the flexible linker modification of AlphaFold [4] respectively. For homomeric interfaces we successfully predict the interface in 69% of cases, and produce high accuracy predictions in 34% of cases, an improvement of +5 percentage points in both instances.
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                Author and article information

                Contributors
                stephan.gruber@unil.ch
                Journal
                Nat Struct Mol Biol
                Nat Struct Mol Biol
                Nature Structural & Molecular Biology
                Nature Publishing Group US (New York )
                1545-9993
                1545-9985
                3 April 2023
                3 April 2023
                2023
                : 30
                : 5
                : 619-628
                Affiliations
                GRID grid.9851.5, ISNI 0000 0001 2165 4204, Department of Fundamental Microbiology, Faculty of Biology and Medicine, , University of Lausanne, ; Lausanne, Switzerland
                Author information
                http://orcid.org/0000-0002-0150-0395
                http://orcid.org/0000-0002-8881-3705
                Article
                956
                10.1038/s41594-023-00956-2
                10191858
                37012407
                6da25bec-a4b4-4fc3-b576-470b672656c7
                © The Author(s) 2023

                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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 9 October 2022
                : 1 March 2023
                Categories
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                © Springer Nature America, Inc. 2023

                Molecular biology
                dna,centromeres,chromosomes,cohesion
                Molecular biology
                dna, centromeres, chromosomes, cohesion

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