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      Targeted photodynamic neutralization of SARS-CoV-2 mediated by singlet oxygen

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          Abstract

          Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus has been on a rampage for more than two years. Vaccines in combination with neutralizing antibodies (NAbs) against SARS-CoV-2 carry great hope in the treatment and final elimination of coronavirus disease 2019 (COVID-19). However, the relentless emergence of variants of concern (VOC), including the most recent Omicron variants, presses for novel measures to counter these variants that often show immune evasion. Hereby we developed a targeted photodynamic approach to neutralize SARS-CoV-2 by engineering a genetically encoded photosensitizer (SOPP3) to a diverse list of antibodies targeting the wild-type (WT) spike protein, including human antibodies isolated from a 2003 Severe acute respiratory syndrome (SARS) patient, potent monomeric and multimeric nanobodies targeting receptor-binding domain (RBD), and non-neutralizing antibodies (non-NAbs) targeting the more conserved N-terminal domain (NTD). As confirmed by pseudovirus neutralization assay, this targeted photodynamic approach significantly increased the efficacy of these antibodies, especially that of non-NAbs, against not only the WT but also the Delta strain and the heavily immune escape Omicron strain (BA.1). Subsequent measurement of infrared phosphorescence at 1270 nm confirmed the generation of singlet oxygen ( 1O 2) in the photodynamic process. Mass spectroscopy assay uncovered amino acids in the spike protein targeted by 1O 2. Impressively, Y145 and H146 form an oxidization “hotspot”, which overlaps with the antigenic “supersite” in NTD. Taken together, our study established a targeted photodynamic approach against the SARS-CoV-2 virus and provided mechanistic insights into the photodynamic modification of protein molecules mediated by 1O 2.

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          The online version contains supplementary material available at 10.1007/s43630-023-00381-w.

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          SARS-CoV-2 Cell Entry Depends on ACE2 and TMPRSS2 and Is Blocked by a Clinically Proven Protease Inhibitor

          Summary The recent emergence of the novel, pathogenic SARS-coronavirus 2 (SARS-CoV-2) in China and its rapid national and international spread pose a global health emergency. Cell entry of coronaviruses depends on binding of the viral spike (S) proteins to cellular receptors and on S protein priming by host cell proteases. Unravelling which cellular factors are used by SARS-CoV-2 for entry might provide insights into viral transmission and reveal therapeutic targets. Here, we demonstrate that SARS-CoV-2 uses the SARS-CoV receptor ACE2 for entry and the serine protease TMPRSS2 for S protein priming. A TMPRSS2 inhibitor approved for clinical use blocked entry and might constitute a treatment option. Finally, we show that the sera from convalescent SARS patients cross-neutralized SARS-2-S-driven entry. Our results reveal important commonalities between SARS-CoV-2 and SARS-CoV infection and identify a potential target for antiviral intervention.
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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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              Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding

              Summary Background In late December, 2019, patients presenting with viral pneumonia due to an unidentified microbial agent were reported in Wuhan, China. A novel coronavirus was subsequently identified as the causative pathogen, provisionally named 2019 novel coronavirus (2019-nCoV). As of Jan 26, 2020, more than 2000 cases of 2019-nCoV infection have been confirmed, most of which involved people living in or visiting Wuhan, and human-to-human transmission has been confirmed. Methods We did next-generation sequencing of samples from bronchoalveolar lavage fluid and cultured isolates from nine inpatients, eight of whom had visited the Huanan seafood market in Wuhan. Complete and partial 2019-nCoV genome sequences were obtained from these individuals. Viral contigs were connected using Sanger sequencing to obtain the full-length genomes, with the terminal regions determined by rapid amplification of cDNA ends. Phylogenetic analysis of these 2019-nCoV genomes and those of other coronaviruses was used to determine the evolutionary history of the virus and help infer its likely origin. Homology modelling was done to explore the likely receptor-binding properties of the virus. Findings The ten genome sequences of 2019-nCoV obtained from the nine patients were extremely similar, exhibiting more than 99·98% sequence identity. Notably, 2019-nCoV was closely related (with 88% identity) to two bat-derived severe acute respiratory syndrome (SARS)-like coronaviruses, bat-SL-CoVZC45 and bat-SL-CoVZXC21, collected in 2018 in Zhoushan, eastern China, but were more distant from SARS-CoV (about 79%) and MERS-CoV (about 50%). Phylogenetic analysis revealed that 2019-nCoV fell within the subgenus Sarbecovirus of the genus Betacoronavirus, with a relatively long branch length to its closest relatives bat-SL-CoVZC45 and bat-SL-CoVZXC21, and was genetically distinct from SARS-CoV. Notably, homology modelling revealed that 2019-nCoV had a similar receptor-binding domain structure to that of SARS-CoV, despite amino acid variation at some key residues. Interpretation 2019-nCoV is sufficiently divergent from SARS-CoV to be considered a new human-infecting betacoronavirus. Although our phylogenetic analysis suggests that bats might be the original host of this virus, an animal sold at the seafood market in Wuhan might represent an intermediate host facilitating the emergence of the virus in humans. Importantly, structural analysis suggests that 2019-nCoV might be able to bind to the angiotensin-converting enzyme 2 receptor in humans. The future evolution, adaptation, and spread of this virus warrant urgent investigation. Funding National Key Research and Development Program of China, National Major Project for Control and Prevention of Infectious Disease in China, Chinese Academy of Sciences, Shandong First Medical University.
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                Author and article information

                Contributors
                zhoulei@szbl.ac.cn
                Journal
                Photochem Photobiol Sci
                Photochem Photobiol Sci
                Photochemical & Photobiological Sciences
                Springer International Publishing (Cham )
                1474-905X
                1474-9092
                8 February 2023
                : 1-18
                Affiliations
                [1 ]GRID grid.510951.9, ISNI 0000 0004 7775 6738, Institute of Molecular Physiology, Shenzhen Bay Laboratory, ; Shenzhen, Guangdong Province People’s Republic of China
                [2 ]GRID grid.224260.0, ISNI 0000 0004 0458 8737, Department of Physiology and Biophysics, School of Medicine, , Virginia Commonwealth University, ; Richmond, VA USA
                Author information
                http://orcid.org/0000-0002-3838-1836
                Article
                381
                10.1007/s43630-023-00381-w
                9906597
                36753022
                81df047c-7f66-449b-99d3-377e8d7f67e1
                © The Author(s), under exclusive licence to European Photochemistry Association, European Society for Photobiology 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

                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
                : 30 July 2022
                : 23 January 2023
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 32171150
                Award Recipient :
                Categories
                Original Papers

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