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      A nanobody inhibiting porcine reproductive and respiratory syndrome virus replication via blocking self-interaction of viral nucleocapsid protein

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          ABSTRACT

          Porcine reproductive and respiratory syndrome (PRRS) is a serious global pig industry disease. Understanding the mechanism of viral replication and developing efficient antiviral strategies are necessary for combating with PRRS virus (PRRSV) infection. Recently, nanobody is considered to be a promising antiviral drug, especially for respiratory viruses. The present study evaluated two nanobodies against PRRSV nucleocapsid (N) protein (PRRSV-N-Nb1 and -Nb2) for their anti-PRRSV activity in vitro and in vivo. The results showed that intracellularly expressed PRRSV-N-Nb1 significantly inhibited PRRSV-2 replication in MARC-145 cells (approximately 100%). Then, the PRRSV-N-Nb1 fused with porcine IgG Fc (Nb1-pFc) as a delivering tag was produced and used to determine its effect on PRRSV-2 replication in porcine alveolar macrophages (PAMs) and pigs. The inhibition rate of Nb1-pFc against PRRSV-2 in PAMs could reach >90%, and it can also inhibit viral replication in vivo. Epitope mapping showed that the motif Serine 105 (S105) in PRRSV-2 N protein was the key amino acid binding to PRRSV-N-Nb1, which is also pivotal for the self-interaction of N protein via binding to Arginine 97. Moreover, viral particles were not successfully rescued when the S105 motif was mutated to Alanine (S105A). Attachment, entry, genome replication, release, docking model analysis, and blocking enzyme-linked immunosorbent assay (ELISA) indicated that the binding of PRRSV-N-Nb1 to N protein could block its self-binding, which prevents the viral replication of PRRSV. PRRSV-N-Nb1 may be a promising drug to counter PRRSV-2 infection. We also provided some new insights into the molecular basis of PRRSV N protein self-binding and assembly of viral particles.

          IMPORTANCE

          Porcine reproductive and respiratory syndrome virus (PRRSV) causes serious economic losses to the swine industry worldwide, and there are no highly effective strategies for prevention. Nanobodies are considered a promising novel approach for treating diseases because of their ease of production and low costing. Here, we showed that PRRSV-N-Nb1 against PRRSV-N protein significantly inhibited PRRSV-2 replication in vitro and in vivo. Furthermore, we demonstrated that the motif Serine 105 (S105) in PRRSV-N protein was the key amino acid to interact with PRRSV-N-Nb1 and bond to its motif R97, which is important for the self-binding of N protein. The PRRSV-N-Nb1 could block the self-interaction of N protein following viral assembly. These findings not only provide insights into the molecular basis of PRRSV N protein self-binding as a key factor for viral replication for the first time but also highlight a novel target for the development of anti-PRRSV replication drugs.

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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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            Analyzing real-time PCR data by the comparative CT method

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              The ClusPro web server for protein–protein docking

              ClusPro is a web server that performs rigid-body docking of two proteins by sampling billions of conformations. Low-energy docked structures are clustered, and centers of the largest clusters are used as likely models of the complex.
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                Author and article information

                Contributors
                Role: Data curationRole: MethodologyRole: Project administrationRole: SoftwareRole: Writing – original draft
                Role: Data curationRole: MethodologyRole: Software
                Role: Data curationRole: Methodology
                Role: Data curationRole: Methodology
                Role: Data curationRole: Software
                Role: ResourcesRole: Software
                Role: MethodologyRole: Software
                Role: Formal analysisRole: InvestigationRole: Resources
                Role: Formal analysisRole: Investigation
                Role: InvestigationRole: ResourcesRole: Software
                Role: Data curationRole: Formal analysisRole: Project administrationRole: Resources
                Role: ConceptualizationRole: Formal analysisRole: MethodologyRole: ResourcesRole: SupervisionRole: Writing – review and editing
                Role: Editor
                Journal
                J Virol
                J Virol
                jvi
                Journal of Virology
                American Society for Microbiology (1752 N St., N.W., Washington, DC )
                0022-538X
                1098-5514
                January 2024
                12 December 2023
                12 December 2023
                : 98
                : 1
                : e01319-23
                Affiliations
                [1 ]Department of Preventive Veterinary Medicine, College of Veterinary Medicine, Northwest A&F University; , Yangling, Shaanxi, China
                [2 ]College of Veterinary Medicine, Henan Agricultural University; , Zhengzhou, Henan, China
                [3 ]Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences; , Shenzhen, China
                University of Michigan Medical School; , Ann Arbor, Michigan, USA
                Author notes
                Address correspondence to Angke Zhang, zhangangke1112@ 123456126.com
                Address correspondence to Yani Sun, sunyani@ 123456nwsuaf.edu.cn
                Address correspondence to Qin Zhao, qinzhao_2004@ 123456nwsuaf.edu.cn

                Hong Duan and Xu Chen contributed equally to this article. Author order was determined by the duration worked on this project.

                The authors declare no conflict of interest.

                Author information
                https://orcid.org/0000-0002-1997-5049
                https://orcid.org/0000-0002-4442-8004
                https://orcid.org/0000-0002-4001-1383
                https://orcid.org/0000-0002-2696-8007
                https://orcid.org/0000-0002-8993-7494
                Article
                01319-23 jvi.01319-23
                10.1128/jvi.01319-23
                10804987
                38084961
                5fc903aa-f3fc-4e4d-91f7-8fea5b2b21b1
                Copyright © 2023 Duan et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.

                History
                : 17 November 2023
                : 22 November 2023
                Page count
                supplementary-material: 0, authors: 12, Figures: 11, Tables: 2, References: 55, Pages: 27, Words: 14576
                Funding
                Funded by: MOST | National Key Research and Development Program of China (NKPs);
                Award ID: 2022YFD1800300
                Award Recipient :
                Funded by: MOST | National Natural Science Foundation of China (NSFC);
                Award ID: 32273041
                Award Recipient :
                Funded by: 陕西省科学技术厅 | Natural Science Foundation of Shaanxi Province (Shaanxi Natural Science Foundation);
                Award ID: 2022JC-12
                Award Recipient :
                Funded by: China Postdoctoral Science Foundation (China Postdoctoral Foundation Project);
                Award ID: 2023M730997
                Award Recipient :
                Categories
                Vaccines and Antiviral Agents
                veterinary-microbiology, Veterinary Microbiology
                Custom metadata
                January 2024

                Microbiology & Virology
                porcine reproductive and respiratory syndrome virus,nanobody,nucleocapsid protein,antiviral drug,viral replication

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