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      Molecular view of ER membrane remodeling by the Sec61/TRAP translocon

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          Abstract

          Protein translocation across the endoplasmic reticulum (ER) membrane is an essential step during protein entry into the secretory pathway. The conserved Sec61 protein‐conducting channel facilitates polypeptide translocation and coordinates cotranslational polypeptide‐processing events. In cells, the majority of Sec61 is stably associated with a heterotetrameric membrane protein complex, the translocon‐associated protein complex (TRAP), yet the mechanism by which TRAP assists in polypeptide translocation remains unknown. Here, we present the structure of the core Sec61/TRAP complex bound to a mammalian ribosome by cryogenic electron microscopy (cryo‐EM). Ribosome interactions anchor the Sec61/TRAP complex in a conformation that renders the ER membrane locally thinner by significantly curving its lumenal leaflet. We propose that TRAP stabilizes the ribosome exit tunnel to assist nascent polypeptide insertion through Sec61 and provides a ratcheting mechanism into the ER lumen mediated by direct polypeptide interactions.

          Abstract

          The structure of the heterotetrameric TRAP complex reveals the mode of interaction with the ribosome and the Sec61 protein translocation channel. Molecular dynamics simulations suggest that Sec61/TRAP induce membrane thinning, which stabilizes an open conformation of the Sec61 lateral gate.

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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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            cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination

            A software tool, cryoSPARC, addresses the speed bottleneck in cryo-EM image processing, enabling automated macromolecular structure determination in hours on a desktop computer without requiring a starting model.
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              New tools for automated high-resolution cryo-EM structure determination in RELION-3

              Here, we describe the third major release of RELION. CPU-based vector acceleration has been added in addition to GPU support, which provides flexibility in use of resources and avoids memory limitations. Reference-free autopicking with Laplacian-of-Gaussian filtering and execution of jobs from python allows non-interactive processing during acquisition, including 2D-classification, de novo model generation and 3D-classification. Per-particle refinement of CTF parameters and correction of estimated beam tilt provides higher resolution reconstructions when particles are at different heights in the ice, and/or coma-free alignment has not been optimal. Ewald sphere curvature correction improves resolution for large particles. We illustrate these developments with publicly available data sets: together with a Bayesian approach to beam-induced motion correction it leads to resolution improvements of 0.2–0.7 Å compared to previous RELION versions.
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                Author and article information

                Contributors
                ville.paavilainen@helsinki.fi
                Journal
                EMBO Rep
                EMBO Rep
                10.1002/(ISSN)1469-3178
                EMBR
                embor
                EMBO Reports
                John Wiley and Sons Inc. (Hoboken )
                1469-221X
                1469-3178
                20 November 2023
                December 2023
                20 November 2023
                : 24
                : 12 ( doiID: 10.1002/embr.v24.12 )
                : e57910
                Affiliations
                [ 1 ] Institute of Biotechnology University of Helsinki Helsinki Finland
                [ 2 ] Protein Biochemistry and Structural Biology Omass Therapeutics Ltd Oxford UK
                [ 3 ] Division of Infection Medicine, Department of Clinical Sciences Lund University Lund Sweden
                Author notes
                [*] [* ] Corresponding author. Tel: +358 504484600; E‐mail: ville.paavilainen@ 123456helsinki.fi

                [ † ]

                These authors contributed equally to this work

                [ ‡ ]

                Correction added on 6 December 2023, after first online publication: The middle name initial of Juha T Huiskonen has been added.

                Author information
                https://orcid.org/0000-0003-3851-1204
                https://orcid.org/0000-0003-4858-364X
                https://orcid.org/0000-0003-1329-8056
                https://orcid.org/0000-0002-0348-7323
                https://orcid.org/0000-0002-5922-4549
                https://orcid.org/0000-0002-3160-7767
                Article
                EMBR202357910
                10.15252/embr.202357910
                10702806
                37983950
                44b94b9a-4f47-4864-b0af-80f13ac733df
                © 2023 The Authors. Published under the terms of the CC BY 4.0 license

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 27 October 2023
                : 01 August 2023
                : 01 November 2023
                Page count
                Figures: 7, Tables: 0, Pages: 16, Words: 15845
                Funding
                Funded by: Academy of Finland (AKA) , doi 10.13039/501100002341;
                Award ID: 338836
                Award ID: 314672
                Award ID: 314669
                Award ID: 338160
                Funded by: Jane and Aatos Erkko Foundation , doi 10.13039/501100004012;
                Funded by: Sigrid Juselius Foundation
                Categories
                Article
                Articles
                Custom metadata
                2.0
                06 December 2023
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.3.5 mode:remove_FC converted:07.12.2023

                Molecular biology
                cryo‐em,membrane proteins,protein translocation,secretory proteins,structural biology,membranes & trafficking

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