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      1H, 15N and 13C resonance assignments of the N-terminal domain of the nucleocapsid protein from the endemic human coronavirus HKU1

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          Abstract

          Coronaviruses have become of great medical and scientific interest because of the Covid-19 pandemic. The hCoV-HKU1 is an endemic betacoronavirus that causes mild respiratory symptoms, although the infection can progress to severe lung disease and death. During viral replication, a discontinuous transcription of the genome takes place, producing the subgenomic messenger RNAs. The nucleocapsid protein (N) plays a pivotal role in the regulation of this process, acting as an RNA chaperone and participating in the nucleocapsid assembly. The isolated N-terminal domain of protein N (N-NTD) specifically binds to the transcriptional regulatory sequences and control the melting of the double-stranded RNA. Here, we report the resonance assignments of the N-NTD of HKU1-CoV.

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          NMRPipe: a multidimensional spectral processing system based on UNIX pipes.

          Frank Delaglio, Stephan Grzesiek, GeertenW. Vuister … (1995)
          The NMRPipe system is a UNIX software environment of processing, graphics, and analysis tools designed to meet current routine and research-oriented multidimensional processing requirements, and to anticipate and accommodate future demands and developments. The system is based on UNIX pipes, which allow programs running simultaneously to exchange streams of data under user control. In an NMRPipe processing scheme, a stream of spectral data flows through a pipeline of processing programs, each of which performs one component of the overall scheme, such as Fourier transformation or linear prediction. Complete multidimensional processing schemes are constructed as simple UNIX shell scripts. The processing modules themselves maintain and exploit accurate records of data sizes, detection modes, and calibration information in all dimensions, so that schemes can be constructed without the need to explicitly define or anticipate data sizes or storage details of real and imaginary channels during processing. The asynchronous pipeline scheme provides other substantial advantages, including high flexibility, favorable processing speeds, choice of both all-in-memory and disk-bound processing, easy adaptation to different data formats, simpler software development and maintenance, and the ability to distribute processing tasks on multi-CPU computers and computer networks.
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            The CCPN data model for NMR spectroscopy: development of a software pipeline.

            Wim Vranken, Wayne Boucher, Tim J. Stevens … (2005)
            To address data management and data exchange problems in the nuclear magnetic resonance (NMR) community, the Collaborative Computing Project for the NMR community (CCPN) created a "Data Model" that describes all the different types of information needed in an NMR structural study, from molecular structure and NMR parameters to coordinates. This paper describes the development of a set of software applications that use the Data Model and its associated libraries, thus validating the approach. These applications are freely available and provide a pipeline for high-throughput analysis of NMR data. Three programs work directly with the Data Model: CcpNmr Analysis, an entirely new analysis and interactive display program, the CcpNmr FormatConverter, which allows transfer of data from programs commonly used in NMR to and from the Data Model, and the CLOUDS software for automated structure calculation and assignment (Carnegie Mellon University), which was rewritten to interact directly with the Data Model. The ARIA 2.0 software for structure calculation (Institut Pasteur) and the QUEEN program for validation of restraints (University of Nijmegen) were extended to provide conversion of their data to the Data Model. During these developments the Data Model has been thoroughly tested and used, demonstrating that applications can successfully exchange data via the Data Model. The software architecture developed by CCPN is now ready for new developments, such as integration with additional software applications and extensions of the Data Model into other areas of research.
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              Protein backbone and sidechain torsion angles predicted from NMR chemical shifts using artificial neural networks.

              Yang Shen, Ad Bax (2013)
              A new program, TALOS-N, is introduced for predicting protein backbone torsion angles from NMR chemical shifts. The program relies far more extensively on the use of trained artificial neural networks than its predecessor, TALOS+. Validation on an independent set of proteins indicates that backbone torsion angles can be predicted for a larger, ≥90 % fraction of the residues, with an error rate smaller than ca 3.5 %, using an acceptance criterion that is nearly two-fold tighter than that used previously, and a root mean square difference between predicted and crystallographically observed (ϕ, ψ) torsion angles of ca 12º. TALOS-N also reports sidechain χ(1) rotameric states for about 50 % of the residues, and a consistency with reference structures of 89 %. The program includes a neural network trained to identify secondary structure from residue sequence and chemical shifts.
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                Author and article information

                Contributors
                Gisele Cardoso Amorim:
                ORCID: http://orcid.org/0000-0003-4348-9515
                gisele.amorim@caxias.ufrj.br
                Journal
                Journal ID (nlm-ta): Biomol NMR Assign
                Journal ID (iso-abbrev): Biomol NMR Assign
                Title: Biomolecular Nmr Assignments
                Publisher: Springer Netherlands (Dordrecht )
                ISSN (Print): 1874-2718
                ISSN (Electronic): 1874-270X
                Publication date (Electronic): 3 January 2021
                Pages: 1-5
                Affiliations
                [1 ]GRID grid.8536.8, ISNI 0000 0001 2294 473X, NUMPEX-BIO, Campus Duque de Caxias, , Federal University of Rio de Janeiro (UFRJ), ; Duque de Caxias, RJ 25245-390 Brazil
                [2 ]GRID grid.8536.8, ISNI 0000 0001 2294 473X, Institute of Medical Biochemistry Leopoldo de Meis (IBqM) and National Center for Structural Biology and Bioimaging (CENABIO), , Federal University of Rio de Janeiro (UFRJ), ; Rio de Janeiro, RJ 21941-590 Brazil
                [3 ]GRID grid.410543.7, ISNI 0000 0001 2188 478X, Multiuser Center for Biomolecular Innovation (CMIB) and Department of Physics, Institute of Biosciences, Letters and Exact Sciences (IBILCE), , São Paulo State University (UNESP), ; São José do Rio Preto, SP 15054-000 Brazil
                [4 ]Rio BioNMR Network, Rio de Janeiro, Brazil
                Author information
                http://orcid.org/0000-0001-9730-5764
                http://orcid.org/0000-0003-4464-0520
                http://orcid.org/0000-0001-6744-1669
                http://orcid.org/0000-0002-6439-1931
                http://orcid.org/0000-0001-6046-7006
                http://orcid.org/0000-0003-4348-9515
                Article
                Publisher ID: 9998
                DOI: 10.1007/s12104-020-09998-9
                PMC ID: 7778850
                SO-VID: d581ca04-f504-4a51-8de5-2294e91974e0
                Copyright © © The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature 2021
                License:

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                Date received : 12 October 2020
                Date accepted : 11 December 2020
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100004586, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro;
                Award ID: 255.940/2020
                Award ID: 202.279/2018
                Award ID: 239.229/2018
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100003593, Conselho Nacional de Desenvolvimento Científico e Tecnológico;
                Award ID: 309564/2017-4
                Award Recipient :
                Categories
                Subject: Article

                ScienceOpen disciplines: Biophysics
                Keywords: human betacoronavirus,hku1,nucleocapsid protein,n-terminal domain,nmr assignment
                Data availability:
                ScienceOpen disciplines: Biophysics
                Keywords: human betacoronavirus, hku1, nucleocapsid protein, n-terminal domain, nmr assignment

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