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      Visualization of translation and protein biogenesis at the ER membrane

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          Abstract

          The dynamic ribosome–translocon complex, which resides at the endoplasmic reticulum (ER) membrane, produces a major fraction of the human proteome 1, 2 . It governs the synthesis, translocation, membrane insertion, N-glycosylation, folding and disulfide-bond formation of nascent proteins. Although individual components of this machinery have been studied at high resolution in isolation 37 , insights into their interplay in the native membrane remain limited. Here we use cryo-electron tomography, extensive classification and molecular modelling to capture snapshots of mRNA translation and protein maturation at the ER membrane at molecular resolution. We identify a highly abundant classical pre-translocation intermediate with eukaryotic elongation factor 1a (eEF1a) in an extended conformation, suggesting that eEF1a may remain associated with the ribosome after GTP hydrolysis during proofreading. At the ER membrane, distinct polysomes bind to different ER translocons specialized in the synthesis of proteins with signal peptides or multipass transmembrane proteins with the translocon-associated protein complex (TRAP) present in both. The near-complete atomic model of the most abundant ER translocon variant comprising the protein-conducting channel SEC61, TRAP and the oligosaccharyltransferase complex A (OSTA) reveals specific interactions of TRAP with other translocon components. We observe stoichiometric and sub-stoichiometric cofactors associated with OSTA, which are likely to include protein isomerases. In sum, we visualize ER-bound polysomes with their coordinated downstream machinery.

          Abstract

          Structural studies of the ribosome-associated endoplasmic reticulum translocon complex based on cryo-electron tomography and molecular modelling reveal multiple intermediate states and interactions between the components of the complex and its cofactors.

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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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            MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.

            Efficient analysis of very large amounts of raw data for peptide identification and protein quantification is a principal challenge in mass spectrometry (MS)-based proteomics. Here we describe MaxQuant, an integrated suite of algorithms specifically developed for high-resolution, quantitative MS data. Using correlation analysis and graph theory, MaxQuant detects peaks, isotope clusters and stable amino acid isotope-labeled (SILAC) peptide pairs as three-dimensional objects in m/z, elution time and signal intensity space. By integrating multiple mass measurements and correcting for linear and nonlinear mass offsets, we achieve mass accuracy in the p.p.b. range, a sixfold increase over standard techniques. We increase the proportion of identified fragmentation spectra to 73% for SILAC peptide pairs via unambiguous assignment of isotope and missed-cleavage state and individual mass precision. MaxQuant automatically quantifies several hundred thousand peptides per SILAC-proteome experiment and allows statistically robust identification and quantification of >4,000 proteins in mammalian cell lysates.
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              <i>Coot</i> : model-building tools for molecular graphics

              Acta Crystallographica Section D Biological Crystallography, 60(12), 2126-2132
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                Author and article information

                Contributors
                j.m.m.fedry@uu.nl
                f.g.forster@uu.nl
                Journal
                Nature
                Nature
                Nature
                Nature Publishing Group UK (London )
                0028-0836
                1476-4687
                25 January 2023
                25 January 2023
                2023
                : 614
                : 7946
                : 160-167
                Affiliations
                [1 ]GRID grid.5477.1, ISNI 0000000120346234, Structural Biochemistry, Bijvoet Center for Biomolecular Research, , Utrecht University, ; Utrecht, The Netherlands
                [2 ]GRID grid.433187.a, Thermo Fisher Scientific, ; Eindhoven, The Netherlands
                [3 ]GRID grid.5477.1, ISNI 0000000120346234, Biomolecular Mass Spectrometry and Proteomics Group, , Utrecht Institute for Pharmaceutical Sciences, Utrecht University, ; Utrecht, The Netherlands
                [4 ]GRID grid.5477.1, ISNI 0000000120346234, Netherlands Proteomics Center, , Utrecht University, Utrecht University, ; Utrecht, The Netherlands
                Author information
                http://orcid.org/0000-0001-7231-7742
                http://orcid.org/0000-0002-2354-1286
                http://orcid.org/0000-0002-1668-0253
                http://orcid.org/0000-0002-4480-5439
                http://orcid.org/0000-0002-6592-4269
                http://orcid.org/0000-0002-6044-2746
                Article
                5638
                10.1038/s41586-022-05638-5
                9892003
                36697828
                1dbbe053-1cbd-4aef-907c-4c0a8c9147be
                © 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 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/.

                History
                : 27 January 2022
                : 7 December 2022
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                © The Author(s), under exclusive licence to Springer Nature Limited 2023

                Uncategorized
                cryoelectron tomography,endoplasmic reticulum,ribosomal proteins
                Uncategorized
                cryoelectron tomography, endoplasmic reticulum, ribosomal proteins

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