A new intracellular targeting motif in the cytoplasmic tail of the spike protein may act as a target to inhibit SARS-CoV-2 assembly.

Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a threat to global public health, underscoring the urgent need for the development of preventive and therapeutic measures. The spike (S) protein of SARS-CoV-2, which mediates receptor binding and subsequent membrane fusion to promote viral entry, is a major target for current drug development and vaccine design. The S protein comprises a large N-terminal extracellular domain, a transmembrane domain, and a short cytoplasmic tail (CT) at the C-terminus. CT truncation of the S protein has been previously reported to promote the infectivity of SARS-CoV and SARS-CoV-2 pseudoviruses. However, the underlying molecular mechanism has not been precisely elucidated. In addition, the CT of various viral membrane glycoproteins play an essential role in the assembly of virions, yet the role of the S protein CT in SARS-CoV-2 infection remains unclear. In this study, through constructing a series of mutations of the CT of the S protein and analyzing their impact on the packaging of the SARS-CoV-2 pseudovirus and live SARS-CoV-2 virus, we identified V1264L1265 as a new intracellular targeting motif in the CT of the S protein, that regulates the transport and subcellular localization of the spike protein through the interactions with cytoskeleton and vesicular transport-related proteins, ARPC3, SCAMP3, and TUBB8, thereby modulating SARS-CoV-2 pseudovirus and live SARS-CoV-2 virion assembly. Either disrupting the V1264L1265 motif or reducing the expression of ARPC3, SCAMP3, and TUBB8 significantly repressed the assembly of the live SARS-CoV-2 virion, raising the possibility that the V1264L1265 motif and the host responsive pathways involved could be new drug targets for the treatment of SARS-CoV-2 infection. Our results extend the understanding of the role played by the S protein CT in the assembly of pseudoviruses and live SARS-CoV-2 virions, which will facilitate the application of pseudoviruses to the study of SARS-CoV-2 and provide potential strategies for the treatment of SARS-CoV-2 infection.

Improving antibody affinity through in vitro mutagenesis in complementarity determining regions.

High-affinity antibodies are widely used in diagnostics and for the treatment of human diseases. However, most antibodies are isolated from semi-synthetic libraries by phage display and do not possess in vivo affinity maturation, which is triggered by antigen immunization. It is therefore necessary to engineer the affinity of these antibodies by way of in vitro assaying. In this study, we optimized the affinity of two human monoclonal antibodies which were isolated by phage display in a previous related study. For the 42A1 antibody, which targets the liver cancer antigen glypican-3, the variant T57H in the second complementarity-determining region of the heavy chain (CDR-H2) exhibited a 2.6-fold improvement in affinity, as well as enhanced cell-binding activity. For the I4A3 antibody to severe acute respiratory syndrome coronavirus 2, beneficial single mutations in CDR-H2 and CDR-H3 were randomly combined to select the best synergistic mutations. Among these, the mutation S53P-S98T improved binding affinity (about 3.7 fold) and the neutralizing activity (about 12 fold) compared to the parent antibody. Taken together, single mutations of key residues in antibody CDRs were enough to increase binding affinity with improved antibody functions. The mutagenic combination of key residues in different CDRs creates additive enhancements. Therefore, this study provides a safe and effective in vitro strategy for optimizing antibody affinity.

Toward Combatting COVID-19: A Risk Assessment System.

The coronavirus disease 2019 (COVID-19) has rapidly become a significant public health emergency all over the world since it was first identified in Wuhan, China, in December 2019. Until today, massive disease-related data have been collected, both manually and through the Internet of Medical Things (IoMT), which can be potentially used to analyze the spread of the disease. On the other hand, with the help of IoMT, the analysis results of the current status of COVID-19 can be delivered to people in real time to enable situational awareness, which may help mitigate the disease spread in communities. However, current accessible data on COVID-19 are mostly at a macrolevel, such as for each state, county, or metropolitan area. For fine-grained areas, such as for each city, community, or geographical coordinate, COVID-19 data are usually not available, which prevents us from obtaining information on the disease spread in closer neighborhoods around us. To address this problem, in this article, we propose a two-level risk assessment system. In particular, we define a "risk index." Then, we develop a risk assessment model, called MK-DNN, by taking advantage of the multikernel density estimation (MKDE) and deep neural network (DNN). We train MK-DNN at the macrolevel (for each metro area), which subsequently enables us to obtain the risk indices at the microlevel (for each geographic coordinate). Moreover, a heuristic validation method is further designed to help validate the obtained microlevel risk indices. Simulations conducted on real-world data demonstrate the accuracy and validity of our proposed risk assessment system.