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      An interchangeable prion-like domain is required for Ty1 retrotransposition

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          Significance

          Retrovirus-like retrotransposons help shape the genome evolution of their hosts and replicate within cytoplasmic particles. How their building blocks associate and assemble within the cell is poorly understood. Here, we report a prion- like domain (PrLD) in the budding yeast retrotransposon Ty1 Gag protein that builds virus-like particles. The PrLD has similar sequence properties to prions and disordered protein domains that can drive the formation of assemblies that range from liquid to solid. We demonstrate that the Ty1 PrLD can function as a prion and that certain prion sequences can replace the PrLD and support Ty1 transposition. This interchangeable system is a useful platform to study disordered sequences in living cells.

          Abstract

          Retrotransposons and retroviruses shape genome evolution and can negatively impact genome function. Saccharomyces cerevisiae and its close relatives harbor several families of LTR-retrotransposons, the most abundant being Ty1 in several laboratory strains. The cytosolic foci that nucleate Ty1 virus-like particle (VLP) assembly are not well understood. These foci, termed retrosomes or T-bodies, contain Ty1 Gag and likely Gag-Pol and the Ty1 mRNA destined for reverse transcription. Here, we report an intrinsically disordered N-terminal prion- like domain (PrLD) within Gag that is required for transposition. This domain contains amino acid composition similar to known yeast prions and is sufficient to nucleate prionogenesis in an established cell-based prion reporter system. Deleting the Ty1 PrLD results in dramatic VLP assembly and retrotransposition defects but does not affect Gag protein level. Ty1 Gag chimeras in which the PrLD is replaced with other sequences, including yeast and mammalian prionogenic domains, display a range of retrotransposition phenotypes from wild type to null. We examine these chimeras throughout the Ty1 replication cycle and find that some support retrosome formation, VLP assembly, and retrotransposition, including the yeast Sup35 prion and the mouse PrP prion. Our interchangeable Ty1 system provides a useful, genetically tractable in vivo platform for studying PrLDs, complete with a suite of robust and sensitive assays. Our work also invites study into the prevalence of PrLDs in additional mobile elements.

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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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            Considerations and Challenges in Studying Liquid-Liquid Phase Separation and Biomolecular Condensates

            Evidence is now mounting that liquid-liquid phase separation (LLPS) underlies the formation of membraneless compartments in cells. This realization has motivated major efforts to delineate the function of such biomolecular condensates in normal cells and their roles in contexts ranging from development to age-related disease. There is great interest in understanding the underlying biophysical principles and the specific properties of biological condensates with the goal of bringing insights into a wide range of biological processes and systems. The explosion of physiological and pathological contexts involving LLPS requires clear standards for their study. Here, we propose guidelines for rigorous experimental characterization of LLPS processes in vitro and in cells, discuss the caveats of common experimental approaches, and point out experimental and theoretical gaps in the field.
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              Phase separation by low complexity domains promotes stress granule assembly and drives pathological fibrillization.

              Stress granules are membrane-less organelles composed of RNA-binding proteins (RBPs) and RNA. Functional impairment of stress granules has been implicated in amyotrophic lateral sclerosis, frontotemporal dementia, and multisystem proteinopathy-diseases that are characterized by fibrillar inclusions of RBPs. Genetic evidence suggests a link between persistent stress granules and the accumulation of pathological inclusions. Here, we demonstrate that the disease-related RBP hnRNPA1 undergoes liquid-liquid phase separation (LLPS) into protein-rich droplets mediated by a low complexity sequence domain (LCD). While the LCD of hnRNPA1 is sufficient to mediate LLPS, the RNA recognition motifs contribute to LLPS in the presence of RNA, giving rise to several mechanisms for regulating assembly. Importantly, while not required for LLPS, fibrillization is enhanced in protein-rich droplets. We suggest that LCD-mediated LLPS contributes to the assembly of stress granules and their liquid properties and provides a mechanistic link between persistent stress granules and fibrillar protein pathology in disease.
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                Author and article information

                Contributors
                Journal
                Proc Natl Acad Sci U S A
                Proc Natl Acad Sci U S A
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                17 July 2023
                25 July 2023
                17 July 2023
                : 120
                : 30
                : e2303358120
                Affiliations
                [1] aDepartment of Biochemistry and Molecular Biology, University of Georgia , Athens, GA 30602
                [2] bRobert P. Apkarian Integrated Electron Microscopy Core at Emory University , Atlanta, GA 30322
                Author notes
                1To whom correspondence may be addressed. Email: djgarf@ 123456uga.edu .

                Edited by Harmit Malik, Fred Hutchinson Cancer Center, Seattle, WA; received March 2, 2023; accepted June 12, 2023

                Author information
                https://orcid.org/0000-0001-6559-1030
                https://orcid.org/0009-0006-1275-9884
                https://orcid.org/0000-0002-3664-8277
                https://orcid.org/0000-0001-6234-2426
                Article
                202303358
                10.1073/pnas.2303358120
                10372613
                37459521
                09505950-6327-4704-ba6c-cf835ffc9160
                Copyright © 2023 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).

                History
                : 02 March 2023
                : 12 June 2023
                Page count
                Pages: 10, Words: 5924
                Funding
                Funded by: HHS | National Institutes of Health (NIH), FundRef 100000002;
                Award ID: R01GM124216
                Award Recipient : Sean Beckwith Award Recipient : Emily J Nomberg Award Recipient : Abigail C Newman Award Recipient : Jeannette V Taylor Award Recipient : Ricardo C Guerrero-Ferreira Award Recipient : David J Garfinkel
                Funded by: HHS | National Institutes of Health (NIH), FundRef 100000002;
                Award ID: F32GM139247
                Award Recipient : Sean Beckwith Award Recipient : Emily J Nomberg Award Recipient : Abigail C Newman Award Recipient : Jeannette V Taylor Award Recipient : Ricardo C Guerrero-Ferreira Award Recipient : David J Garfinkel
                Funded by: HHS | National Institutes of Health (NIH), FundRef 100000002;
                Award ID: UL1TR000454
                Award Recipient : Sean Beckwith Award Recipient : Emily J Nomberg Award Recipient : Abigail C Newman Award Recipient : Jeannette V Taylor Award Recipient : Ricardo C Guerrero-Ferreira Award Recipient : David J Garfinkel
                Funded by: HHS | National Institutes of Health (NIH), FundRef 100000002;
                Award ID: S10RR025679
                Award Recipient : Sean Beckwith Award Recipient : Emily J Nomberg Award Recipient : Abigail C Newman Award Recipient : Jeannette V Taylor Award Recipient : Ricardo C Guerrero-Ferreira Award Recipient : David J Garfinkel
                Categories
                research-article, Research Article
                genetics, Genetics
                419
                Biological Sciences
                Genetics

                retrotransposon,virus-like particle,prion-like domain,saccharomyces cerevisiae

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