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AbstractThe outbreak of coronavirus disease (COVID-19) in China caused by SARS-CoV-2 virus continually lead to worldwide human infections and deaths. It is currently no specific viral protein targeted therapeutics yet. Viral nucleocapsid protein is a potential antiviral drug target, serving multiple critical functions during the viral life cycle. However, the structural information of SARS-CoV-2 nucleocapsid protein is yet to be clear. Herein, we have determined the 2.7 Å crystal structure of the N-terminal RNA binding domain of SARS-CoV-2 nucleocapsid protein. Although overall structure is similar with other reported coronavirus nucleocapsid protein N-terminal domain, the surface electrostatic potential characteristics between them are distinct. Further comparison with mild virus type HCoV-OC43 equivalent domain demonstrates a unique potential RNA binding pocket alongside the β-sheet core. Complemented by in vitro binding studies, our data provide several atomic resolution features of SARS-CoV-2 nucleocapsid protein N-terminal domain, guiding the design of novel antiviral agents specific targeting to SARS-CoV-2.

Dysregulated immune cell responses have been linked to the severity of coronavirus disease 2019 (COVID-19), but the specific viral factors of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were currently unknown. Herein, we reveal that the Ig-like fold ectodomain of the viral protein SARS-CoV-2 ORF7a interacts with high efficiency to CD14 + monocytes in human peripheral blood, compared to pathogenic protein SARS-CoV ORF7a. The crystal structure of SARS-CoV-2 ORF7a at 2.2 Å resolution reveals three remarkable changes on the amphipathic side of the four-stranded β-sheet, implying a potential functional interface of the viral protein. Importantly, SARS-CoV-2 ORF7a coincubation with CD14 + monocytes ex vivo triggered a decrease in HLA-DR/DP/DQ expression levels and upregulated significant production of proinflammatory cytokines, including IL-6, IL-1β, IL-8, and TNF-α. Our work demonstrates that SARS-CoV-2 ORF7a is an immunomodulating factor for immune cell binding and triggers dramatic inflammatory responses, providing promising therapeutic drug targets for pandemic COVID-19.

Mitofusin-2 (MFN2) is a dynamin-like GTPase that plays a central role in regulating mitochondrial fusion and cell metabolism. Mutations in MFN2 cause the neurodegenerative disease Charcot-Marie-Tooth type 2A (CMT2A). The molecular basis underlying the physiological and pathological relevance of MFN2 is unclear. Here, we present crystal structures of truncated human MFN2 in different nucleotide-loading states. Unlike other dynamin superfamily members including MFN1, MFN2 forms sustained dimers even after GTP hydrolysis via the GTPase domain (G) interface, which accounts for its high membrane-tethering efficiency. The biochemical discrepancy between human MFN2 and MFN1 largely derives from a primate-only single amino acid variance. MFN2 and MFN1 can form heterodimers via the G interface in a nucleotide-dependent manner. CMT2A-related mutations, mapping to different functional zones of MFN2, lead to changes in GTP hydrolysis and homo/hetero-association ability. Our study provides fundamental insight into how mitofusins mediate mitochondrial fusion and the ways their disruptions cause disease.

Text recognition has attracted considerable research interests because of its various applications. The cutting-edge text recognition methods are based on attention mechanisms. However, most of attention methods usually suffer from serious alignment problem due to its recurrency alignment operation, where the alignment relies on historical decoding results. To remedy this issue, we propose a decoupled attention network (DAN), which decouples the alignment operation from using historical decoding results. DAN is an effective, flexible and robust end-to-end text recognizer, which consists of three components: 1) a feature encoder that extracts visual features from the input image; 2) a convolutional alignment module that performs the alignment operation based on visual features from the encoder; and 3) a decoupled text decoder that makes final prediction by jointly using the feature map and attention maps. Experimental results show that DAN achieves state-of-the-art performance on multiple text recognition tasks, including offline handwritten text recognition and regular/irregular scene text recognition. Codes will be released.1