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      Molecular mechanism targeting condensin for chromosome condensation

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          Abstract

          Genomes are organised into DNA loops by the Structural Maintenance of Chromosomes (SMC) proteins. SMCs establish functional chromosomal sub-domains for DNA repair, gene expression and chromosome segregation, but how SMC activity is specifically targeted is unclear. Here, we define the molecular mechanism targeting the condensin SMC complex to specific chromosomal regions in budding yeast. A conserved pocket on the condensin HAWK subunit Ycg1 binds to chromosomal receptors carrying a related motif, CR1. In early mitosis, CR1 motifs in receptors Sgo1 and Lrs4 recruit condensin to pericentromeres and rDNA, to facilitate sister kinetochore biorientation and rDNA condensation, respectively. We additionally find that chromosome arm condensation begins as sister kinetochores come under tension, in a manner dependent on the Ycg1 pocket. We propose that multiple CR1-containing proteins recruit condensin to chromosomes and identify several additional candidates based on their sequence. Overall, we uncover the molecular mechanism that targets condensin to functionalise chromosomal domains to achieve accurate chromosome segregation during mitosis.

          Synopsis

          How the condensin complex is recruited to genomic domains to promote their condensation is incompletely understood. This study identifies a generalised molecular mechanism by which condensin is targeted to specific chromosomal loci.

          • Budding yeast condensin is recruited to chromosomes via a conserved binding pocket on its Ycg1/CAP-G subunit.

          • The Ycg1 pocket binds CR1 motifs in several chromosomal proteins.

          • CR1 motifs in Sgo1 and Lrs4 recruit condensin to pericentromeres and rDNA, respectively.

          • Condensin recruited to pericentromeres by Sgo1 promotes sister kinetochore biorientation in the absence of spindle tension.

          • Chromosome arm and rDNA condensation is initiated by spindle tension, dependent on the Ycg1 binding pocket.

          Abstract

          A pocket in the CAP-G/Ycg1 subunit binds CR1 motifs in several chromosomal proteins to recruit condensin to specific chromosomal regions.

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          Most cited references75

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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.
            • Record: found
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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.
              • Record: found
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              The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences

              The PRoteomics IDEntifications (PRIDE) database ( https://www.ebi.ac.uk/pride/ ) is the world's largest data repository of mass spectrometry-based proteomics data. PRIDE is one of the founding members of the global ProteomeXchange (PX) consortium and an ELIXIR core data resource. In this manuscript, we summarize the developments in PRIDE resources and related tools since the previous update manuscript was published in Nucleic Acids Research in 2019. The number of submitted datasets to PRIDE Archive (the archival component of PRIDE) has reached on average around 500 datasets per month during 2021. In addition to continuous improvements in PRIDE Archive data pipelines and infrastructure, the PRIDE Spectra Archive has been developed to provide direct access to the submitted mass spectra using Universal Spectrum Identifiers. As a key point, the file format MAGE-TAB for proteomics has been developed to enable the improvement of sample metadata annotation. Additionally, the resource PRIDE Peptidome provides access to aggregated peptide/protein evidences across PRIDE Archive. Furthermore, we will describe how PRIDE has increased its efforts to reuse and disseminate high-quality proteomics data into other added-value resources such as UniProt, Ensembl and Expression Atlas.

                Author and article information

                Contributors
                adele.marston@ed.ac.uk
                Journal
                EMBO J
                EMBO J
                The EMBO Journal
                Nature Publishing Group UK (London )
                0261-4189
                1460-2075
                17 December 2024
                17 December 2024
                February 2025
                : 44
                : 3
                : 705-735
                Affiliations
                [1 ]Centre for Cell Biology, Institute of Cell Biology, University of Edinburgh, ( https://ror.org/01nrxwf90) Edinburgh, EH9 3BF United Kingdom
                [2 ]Institute of Biotechnology, Technische Universität Berlin, ( https://ror.org/03v4gjf40) Gustav-Meyer-Allee 25, 13355 Berlin, Germany
                Author information
                http://orcid.org/0000-0003-2594-6217
                http://orcid.org/0000-0002-4376-8242
                http://orcid.org/0000-0001-5999-1310
                http://orcid.org/0000-0002-3596-9407
                Article
                336
                10.1038/s44318-024-00336-6
                11791182
                39690240
                beb830bd-222a-4c01-b53d-4e8ac4f58695
                © 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/. Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the data associated with this article, unless otherwise stated in a credit line to the data, but does not extend to the graphical or creative elements of illustrations, charts, or figures. This waiver removes legal barriers to the re-use and mining of research data. According to standard scholarly practice, it is recommended to provide appropriate citation and attribution whenever technically possible.

                History
                : 22 May 2024
                : 26 November 2024
                : 2 December 2024
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100010269, Wellcome Trust (WT);
                Award ID: 220780
                Award ID: 108504
                Award ID: 2183052
                Award ID: 203149
                Award ID: 226791
                Categories
                Article
                Custom metadata
                © European Molecular Biology Organization 2025

                Molecular biology
                condensin,shugoshin,lrs4,pericentromeres,rdna,cell cycle,chromatin, transcription & genomics

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