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      The molecular mechanisms of human separase regulation

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          Abstract

          Sister chromatid segregation is the final irreversible step of mitosis. It is initiated by a complex regulatory system that ultimately triggers the timely activation of a conserved cysteine protease named separase. Separase cleaves the cohesin protein ring that links the sister chromatids and thus facilitates their separation and segregation to the opposite poles of the dividing cell. Due to the irreversible nature of this process, separase activity is tightly controlled in all eukaryotic cells. In this mini-review, we summarize the latest structural and functional findings on the regulation of separase, with an emphasis on the regulation of the human enzyme by two inhibitors, the universal inhibitor securin and the vertebrate-specific inhibitor CDK1–cyclin B. We discuss the two fundamentally different inhibitory mechanisms by which these inhibitors block separase activity by occluding substrate binding. We also describe conserved mechanisms that facilitate substrate recognition and point out open research questions that will guide studies of this fascinating enzyme for years to come.

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          Most cited references59

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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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              AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models

              The AlphaFold Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk ) is an openly accessible, extensive database of high-accuracy protein-structure predictions. Powered by AlphaFold v2.0 of DeepMind, it has enabled an unprecedented expansion of the structural coverage of the known protein-sequence space. AlphaFold DB provides programmatic access to and interactive visualization of predicted atomic coordinates, per-residue and pairwise model-confidence estimates and predicted aligned errors. The initial release of AlphaFold DB contains over 360,000 predicted structures across 21 model-organism proteomes, which will soon be expanded to cover most of the (over 100 million) representative sequences from the UniRef90 data set.
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                Author and article information

                Journal
                Biochem Soc Trans
                Biochem Soc Trans
                BST
                Biochemical Society Transactions
                Portland Press Ltd.
                0300-5127
                1470-8752
                28 June 2023
                4 May 2023
                : 51
                : 3
                : 1225-1233
                Affiliations
                [1 ]Department of Molecular and Cellular Biology, University of Geneva, CH-1211 Geneva, Switzerland
                [2 ]Department of Physiology, University of California, San Francisco, San Francisco, CA 94143, U.S.A.
                Author notes
                Correspondence: Jun Yu ( Jun.Yu@ 123456unige.ch ) or Andreas Boland ( Andreas.Boland@ 123456unige.ch )
                Author information
                http://orcid.org/0000-0003-1218-6714
                Article
                BST-51-1225
                10.1042/BST20221400
                10317155
                37140261
                55100a68-cd51-451b-b56f-f9078834d608
                © 2023 The Author(s)

                This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY). Open access for this article was enabled by the participation of the University of Geneva in an all-inclusive Read & Publish agreement with Portland Press and the Biochemical Society under a transformative agreement with Individual.

                History
                : 20 February 2023
                : 11 April 2023
                : 18 April 2023
                Categories
                Cell Cycle, Growth & Proliferation
                Enzymology
                Post-Translational Modifications
                Structural Biology
                Cancer
                Review Articles

                Biochemistry
                cancer,cell cycle,cryo-electron microscopy,enzyme–substrate interactions,inhibition,structural biology

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