In the present study, by building on the previous development of a DC-SIGN-targeting virus-like structure (VLS) vaccine platform and a comprehensive characterization of SARS-CoV-2 structural biology, particularly insights into the role of the nucleocapsid (N) protein in eliciting cytotoxic T lymphocyte (CTL) responses during infection, we designed a SARS-CoV-2 virion-mimetic structural vaccine that encapsulates an mRNA encoding the spike S1 antigen complexed with N protein complexes, with S1 proteins loaded on its surface. This characterized virion-mimetic structural vaccine not only induces the production of high-efficiency antibodies against both the spike and N proteins but also elicits robust S1-specific and N-specific CTL responses in animal models. Furthermore, the generated antibodies exhibit cross-reactive neutralizing activity against multiple SARS-CoV-2 variants and provide protective immunity against challenge with mutant viruses in immunized hosts. This SARS-CoV-2 virion-mimetic structure effectively recapitulates natural infection pathways, comprehensively activating the innate immune system and thereby creating an optimal microenvironment for eliciting potent and broad-spectrum adaptive immune responses.
Jingjing Zhang, Fen Zeng, Yanmei Li et al.· Scientific Reports· 0 citations
Nanobodies, the single-domain counterparts of traditional antibodies, are approximately one-tenth the size yet retain the ability to bind their target antigens tightly and specifically. A major practical bottleneck in developing functional nanobodies is the panning process required to identify them from the vast immunized cDNA libraries derived from camelids. To overcome this bottleneck, we developed a computational framework that progressed from next-generation sequencing (NGS)-derived candidate nanobody sequences to predicted structures using AlphaFold2, and prioritized nanobodies based on predicted binding energy scores. The nanobody-antigen binding poses were predicted using an ensemble docking strategy, which was selected over single-structure docking to better account for antigen conformational flexibility. We demonstrated that a physics-based docking method followed by MM/GBSA re-scoring delivered favorable performance in recovering native nanobody-antigen binding poses, outperforming sequence-only AlphaFold3 in our preliminary benchmark test. Applied to three antigen systems—Mesothelin (MSLN), PD-1, and Nectin-4—our computational workflow successfully prioritized candidate nanobodies. At least seven out of ten (70%) of the top-ranked candidates for each target exhibited strong binding by flow cytometry, with ELISA and surface plasmon resonance (SPR) further confirming nanomolar-level binding for representative candidates. Additionally, compared with conventional random-selection-based monoclonal clone picking, our workflow improved hit recovery while reducing redundancy, enabling the identification of functional nanobodies across a broader range of NGS copy-number ranks rather than only the most abundant post-panning clones. These results support the practical utility of the framework for enriching functional nanobodies from experimentally pre-enriched NGS-derived pools.
Yinghao Guo, Renfang Guan, Lunde Jin et al.· Chemical Science· 0 citations