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      Phase separation properties of RPA combine high-affinity ssDNA binding with dynamic condensate functions at telomeres

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          Abstract

          RPA has been shown to protect single-stranded DNA (ssDNA) intermediates from instability and breakage. RPA binds ssDNA with sub-nanomolar affinity, yet dynamic turnover is required for downstream ssDNA transactions. How ultrahigh-affinity binding and dynamic turnover are achieved simultaneously is not well understood. Here we reveal that RPA has a strong propensity to assemble into dynamic condensates. In solution, purified RPA phase separates into liquid droplets with fusion and surface wetting behavior. Phase separation is stimulated by sub-stoichiometric amounts of ssDNA, but not RNA or double-stranded DNA, and ssDNA gets selectively enriched in RPA condensates. We find the RPA2 subunit required for condensation and multi-site phosphorylation of the RPA2 N-terminal intrinsically disordered region to regulate RPA self-interaction. Functionally, quantitative proximity proteomics links RPA condensation to telomere clustering and integrity in cancer cells. Collectively, our results suggest that RPA-coated ssDNA is contained in dynamic RPA condensates whose properties are important for genome organization and stability.

          Abstract

          Here, the authors show that replication protein A (RPA) tends to self-assemble into dynamic condensates, in a manner that is stimulated by ssDNA and regulated by RPA2 phosphorylation. RPA condensates are functionally important for telomere clustering and RAD52-dependent telomere maintenance.

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          Most cited references70

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          limma powers differential expression analyses for RNA-sequencing and microarray studies

          limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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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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              Liquid phase condensation in cell physiology and disease.

              Phase transitions are ubiquitous in nonliving matter, and recent discoveries have shown that they also play a key role within living cells. Intracellular liquid-liquid phase separation is thought to drive the formation of condensed liquid-like droplets of protein, RNA, and other biomolecules, which form in the absence of a delimiting membrane. Recent studies have elucidated many aspects of the molecular interactions underlying the formation of these remarkable and ubiquitous droplets and the way in which such interactions dictate their material properties, composition, and phase behavior. Here, we review these exciting developments and highlight key remaining challenges, particularly the ability of liquid condensates to both facilitate and respond to biological function and how their metastability may underlie devastating protein aggregation diseases.
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                Author and article information

                Contributors
                matthias.altmeyer@uzh.ch
                Journal
                Nat Struct Mol Biol
                Nat Struct Mol Biol
                Nature Structural & Molecular Biology
                Nature Publishing Group US (New York )
                1545-9993
                1545-9985
                9 March 2023
                9 March 2023
                2023
                : 30
                : 4
                : 451-462
                Affiliations
                [1 ]GRID grid.7400.3, ISNI 0000 0004 1937 0650, Department of Molecular Mechanisms of Disease, , University of Zurich (UZH), ; Zurich, Switzerland
                [2 ]GRID grid.29078.34, ISNI 0000 0001 2203 2861, Institute for Research in Biomedicine, Faculty of Biomedical Sciences, , Università della Svizzera italiana (USI), ; Bellinzona, Switzerland
                [3 ]GRID grid.5801.c, ISNI 0000 0001 2156 2780, Department of Biology, , Institute of Biochemistry, Eidgenössische Technische Hochschule (ETH), ; Zurich, Switzerland
                [4 ]GRID grid.7400.3, ISNI 0000 0004 1937 0650, Functional Genomics Center Zurich, University of Zurich (UZH) & Eidgenössische Technische Hochschule (ETH), ; Zurich, Switzerland
                Author information
                http://orcid.org/0000-0002-7787-6938
                http://orcid.org/0000-0001-6643-7860
                http://orcid.org/0000-0001-5756-9773
                http://orcid.org/0000-0003-3780-1170
                Article
                932
                10.1038/s41594-023-00932-w
                10113159
                36894693
                9cd3d22a-8fcb-480d-a304-3d8f53114838
                © 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
                : 31 January 2022
                : 27 January 2023
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                © Springer Nature America, Inc. 2023

                Molecular biology
                chromosomes,dna damage and repair,dna replication,telomeres,cancer
                Molecular biology
                chromosomes, dna damage and repair, dna replication, telomeres, cancer

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