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      Surf4, cargo trafficking, lipid metabolism, and therapeutic implications

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          ABSTRACT

          Surfeit 4 is a polytopic transmembrane protein that primarily resides in the endoplasmic reticulum (ER) membrane. It is ubiquitously expressed and functions as a cargo receptor, mediating cargo transport from the ER to the Golgi apparatus via the canonical coat protein complex II (COPII)-coated vesicles or specific vesicles. It also participates in ER–Golgi protein trafficking through a tubular network. Meanwhile, it facilitates retrograde transportation of cargos from the Golgi apparatus to the ER through COPI-coated vesicles. Surf4 can selectively mediate export of diverse cargos, such as PCSK9 very low-density lipoprotein (VLDL), progranulin, α1-antitrypsin, STING, proinsulin, and erythropoietin. It has been implicated in facilitating VLDL secretion, promoting cell proliferation and migration, and increasing replication of positive-strand RNA viruses. Therefore, Surf4 plays a crucial role in various physiological and pathophysiological processes and emerges as a promising therapeutic target. However, the molecular mechanisms by which Surf4 selectively sorts diverse cargos for ER–Golgi protein trafficking remain elusive. Here, we summarize the most recent advances in Surf4, focusing on its role in lipid metabolism.

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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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            Search and sequence analysis tools services from EMBL-EBI in 2022

            The EMBL-EBI search and sequence analysis tools frameworks provide integrated access to EMBL-EBI’s data resources and core bioinformatics analytical tools. EBI Search ( https://www.ebi.ac.uk/ebisearch ) provides a full-text search engine across nearly 5 billion entries, while the Job Dispatcher tools framework ( https://www.ebi.ac.uk/services ) enables the scientific community to perform a diverse range of sequence analysis using popular bioinformatics applications. Both allow users to interact through user-friendly web applications, as well as via RESTful and SOAP-based APIs. Here, we describe recent improvements to these services and updates made to accommodate the increasing data requirements during the COVID-19 pandemic. Graphical Abstract Overview of the tools and data resources provided by EBI Search and Job Dispatcher services accessible via their webpage and programmatic interfaces.
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              Expression profiling reveals off-target gene regulation by RNAi.

              RNA interference is thought to require near-identity between the small interfering RNA (siRNA) and its cognate mRNA. Here, we used gene expression profiling to characterize the specificity of gene silencing by siRNAs in cultured human cells. Transcript profiles revealed siRNA-specific rather than target-specific signatures, including direct silencing of nontargeted genes containing as few as eleven contiguous nucleotides of identity to the siRNA. These results demonstrate that siRNAs may cross-react with targets of limited sequence similarity.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                J Mol Cell Biol
                J Mol Cell Biol
                jmcb
                Journal of Molecular Cell Biology
                Oxford University Press
                1674-2788
                1759-4685
                September 2022
                29 November 2022
                29 November 2022
                : 14
                : 9
                : mjac063
                Affiliations
                Group on the Molecular and Cell Biology of Lipids and Department of Pediatrics, Faculty of Medicine and Dentistry, University of Alberta , Edmonton, AB T6R 2G3, Canada
                Group on the Molecular and Cell Biology of Lipids and Department of Pediatrics, Faculty of Medicine and Dentistry, University of Alberta , Edmonton, AB T6R 2G3, Canada
                Institute of Atherosclerosis in Shandong First Medical University (Shandong Academy of Medical Sciences) , Taian 271016, China
                Group on the Molecular and Cell Biology of Lipids and Department of Pediatrics, Faculty of Medicine and Dentistry, University of Alberta , Edmonton, AB T6R 2G3, Canada
                Author notes
                Correspondence to: Da-Wei Zhang, E-mail: dzhang@ 123456ualberta.ca
                Correspondence to: Shucun Qin, E-mail: scqin@ 123456sdfmu.edu.cn
                Author information
                https://orcid.org/0000-0001-8279-8041
                Article
                mjac063
                10.1093/jmcb/mjac063
                9929512
                36574593
                3e423ba0-ec4d-448d-a30f-e2745920f932
                © The Author(s) (2022). Published by Oxford University Press on behalf of Journal of Molecular Cell Biology, CEMCS, CAS.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 22 June 2022
                : 30 July 2022
                : 06 September 2022
                : 15 February 2023
                Page count
                Pages: 11
                Funding
                Funded by: Canadian Institutes of Health Research, DOI 10.13039/501100000024;
                Award ID: PS 178091
                Funded by: National Natural Science Foundation of China, DOI 10.13039/501100001809;
                Award ID: NSFC 81929002
                Funded by: Natural Sciences and Engineering Research Council of Canada, DOI 10.13039/501100000038;
                Award ID: RGPIN-2016-06479
                Funded by: Canadian Institutes of Health Research, DOI 10.13039/501100000024;
                Award ID: PS 155994
                Funded by: Shandong First Medical University, DOI 10.13039/501100015507;
                Award ID: 2019QL010
                Award ID: 2019PT009
                Categories
                Review
                AcademicSubjects/SCI01180

                vldl secretion,cargo receptor,pcsk9,lipid metabolism,atherosclerosis,trafficking

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