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      Genomic screening of 16 UK native bat species through conservationist networks uncovers coronaviruses with zoonotic potential

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          Abstract

          There has been limited characterisation of bat-borne coronaviruses in Europe. Here, we screened for coronaviruses in 48 faecal samples from 16 of the 17 bat species breeding in the UK, collected through a bat rehabilitation and conservationist network. We recovered nine complete genomes, including two novel coronavirus species, across six bat species: four alphacoronaviruses, a MERS-related betacoronavirus, and four closely related sarbecoviruses. We demonstrate that at least one of these sarbecoviruses can bind and use the human ACE2 receptor for infecting human cells, albeit suboptimally. Additionally, the spike proteins of these sarbecoviruses possess an R-A-K-Q motif, which lies only one nucleotide mutation away from a furin cleavage site (FCS) that enhances infectivity in other coronaviruses, including SARS-CoV-2. However, mutating this motif to an FCS does not enable spike cleavage. Overall, while UK sarbecoviruses would require further molecular adaptations to infect humans, their zoonotic risk warrants closer surveillance.

          Abstract

          Certain bats species have previously been identified as ancestral sources of coronaviruses that infect humans but there is limited data on the genomic diversity or zoonotic potential of viruses infecting bats in the UK. Here, the authors use deep sequencing and in vitro assays to characterise coronaviruses recovered from 48 bat faecal samples.

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          Fast gapped-read alignment with Bowtie 2.

          As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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            MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability

            We report a major update of the MAFFT multiple sequence alignment program. This version has several new features, including options for adding unaligned sequences into an existing alignment, adjustment of direction in nucleotide alignment, constrained alignment and parallel processing, which were implemented after the previous major update. This report shows actual examples to explain how these features work, alone and in combination. Some examples incorrectly aligned by MAFFT are also shown to clarify its limitations. We discuss how to avoid misalignments, and our ongoing efforts to overcome such limitations.
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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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                Author and article information

                Contributors
                v.savolainen@imperial.ac.uk
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                27 June 2023
                27 June 2023
                2023
                : 14
                : 3322
                Affiliations
                [1 ]GRID grid.83440.3b, ISNI 0000000121901201, UCL Genetics Institute, , University College London, ; Gower St, London, WC1E 6BT UK
                [2 ]GRID grid.451388.3, ISNI 0000 0004 1795 1830, The Francis Crick Institute, ; 1 Midland Rd, London, NW1 1AT UK
                [3 ]GRID grid.7445.2, ISNI 0000 0001 2113 8111, Georgina Mace Centre for the Living Planet, Department of Life Sciences, , Imperial College London, Silwood Park Campus, ; Ascot, SL5 7PY UK
                [4 ]GRID grid.7445.2, ISNI 0000 0001 2113 8111, Department of Infectious Disease, Imperial College London, , St Marys Medical School, ; Paddington, London W2 1PG UK
                [5 ]GRID grid.5333.6, ISNI 0000000121839049, Protein Production and Structure Core Facility (PTPSP), , School of Life Sciences, École Polytechnique Fédérale de Lausanne, ; Rte Cantonale, 1015 Lausanne, Switzerland
                [6 ]GRID grid.5333.6, ISNI 0000000121839049, Laboratory of Biological Electron Microscopy (LBEM), , School of Basic Science, École Polytechnique Fédérale de Lausanne, ; Rte Cantonale, 1015 Lausanne, Switzerland
                [7 ]GRID grid.4563.4, ISNI 0000 0004 1936 8868, Queen’s Medical Centre, , University of Nottingham, ; Derby Rd, Lenton, Nottingham, NG7 2UH UK
                [8 ]GRID grid.63622.33, ISNI 0000 0004 0388 7540, The Pirbright Institute, ; Surrey, GU24 0NF UK
                [9 ]GRID grid.4991.5, ISNI 0000 0004 1936 8948, Nuffield Department of Medicine, , University of Oxford, ; Oxford, OX3 7BN UK
                [10 ]GRID grid.473889.9, ISNI 0000 0001 2288 3420, The Bat Conservation Trust, , Studio 15 Cloisters House, Cloisters Business Centre, ; 8 Battersea Park Road, London, SW8 4BG UK
                Author information
                http://orcid.org/0000-0003-3536-8465
                http://orcid.org/0000-0001-7077-2928
                http://orcid.org/0009-0001-2000-1460
                http://orcid.org/0009-0005-0980-1802
                http://orcid.org/0000-0002-9040-7597
                http://orcid.org/0000-0002-3193-6077
                http://orcid.org/0000-0002-7005-1394
                http://orcid.org/0000-0002-5640-2266
                http://orcid.org/0000-0002-4450-5911
                http://orcid.org/0000-0003-3678-5631
                http://orcid.org/0000-0001-6158-2559
                http://orcid.org/0000-0002-2615-3932
                http://orcid.org/0000-0001-8800-6981
                http://orcid.org/0000-0002-3948-0895
                http://orcid.org/0000-0002-6211-2310
                http://orcid.org/0000-0003-1978-7715
                http://orcid.org/0000-0001-5350-9984
                Article
                38717
                10.1038/s41467-023-38717-w
                10300128
                37369644
                95e5b10e-1be3-4d22-bf78-df8a6632581f
                © 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 20 March 2023
                : 5 May 2023
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100000270, RCUK | Natural Environment Research Council (NERC);
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2023

                Uncategorized
                ecological epidemiology,viral genetics,virology,genomics
                Uncategorized
                ecological epidemiology, viral genetics, virology, genomics

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