Aug 2026· Antiviral Research· pp.
106516
· 0 citations· 50 references
Medicine
TL;DR
HRBD demonstrated potent and broad-spectrum inhibition against Pangolin-CoV, SARS-CoV, SARS-CoV, SARS-CoV-2, and its variants, lowering the half-maximal inhibitory concentration (IC50) by approximately 1000-fold compared to the monomeric RBD.
Abstract
Continuous mutation of viruses enables evasion of established immune defenses and therapeutics. Here we report a broad-spectrum therapeutic design, termed HRBD, that mimics viral invasion-associated molecular patterns. HRBD demonstrated potent and broad-spectrum inhibition against Pangolin-CoV, SARS-CoV, SARS-CoV-2, and its variants, lowering the half-maximal inhibitory concentration (IC50) by approximately 1000-fold compared to the monomeric RBD. Furthermore, HRBD effectively suppressed syncytium formation induced by the spike proteins of sarbecoviruses, an effect not observed with the monomeric RBD. In hACE2-transgenic mice challenged with SARS-CoV-2, intranasal administration of HRBD reduced viral loads in lung and tracheal tissues by ∼106-fold, with no detectable immunogenicity. Binding analyses revealed that HRBD achieved approximately 1000-fold stronger avidity for hACE2 compared to the RBD monomer, superior to the high-affinity RBD-62 mutant generated by directed evolution, without affecting hACE2 enzymatic activity or subcellular localization. Oligomerization characterization confirmed that HRBD predominantly formed heptamers, visualized as ∼10 nm diameter rings via transmission electron microscopy. This mimicking strategy offers a viable approach for developing broad-spectrum therapeutics against current and future antigenically variable viruses.
The rapid evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and associated complement overactivation challenge current antiviral strategies that mainly target viral entry. This study aimed to develop dual targeting engineered binding proteins capable of simultaneously blocking viral infection and complement activation. Two proteins, SBP10 and SBP16, were engineered by integrating an ACE2-mimicking peptide with a mannose-binding lectin (MBL) domain. Binding affinity and antiviral activity were evaluated using biochemical and functional assays, including inhibition of S protein-ACE2 interaction, neutralization of multiple SARS-CoV-2 variants, and assessment of lectin pathway-mediated complement activation. Both SBP10 and SBP16 bound the spike protein with low-nanomolar affinity and effectively blocked its interaction with ACE2. The proteins exhibited broad-spectrum neutralizing activity against several variants, including Alpha, Beta, Delta, and Omicron. Moreover, they significantly suppressed spike-induced activation of the lectin complement pathway. In vivo experiments further demonstrated that treatment with SBP10 or SBP16 markedly reduced spike protein-induced lung injury. In conclusion, SBP10 and SBP16 function as dual targeting engineered binding proteins that inhibit viral entry while attenuating complement-mediated inflammation, highlighting a promising therapeutic strategy for controlling SARS-CoV-2 infection and its associated immune dysregulation.
Fan Pu, Yiwan Guo, Xinni Pan et al.· Antiviral Research· 0 citations
SARS-CoV-2 evolution has reduced the efficacy of clinical monoclonal antibodies, underscoring the need for therapeutics targeting conserved viral regions. The Spike (S) heptad repeat 2 (HR2) stem helix is highly conserved across SARS-CoV-2 variants and related betacoronaviruses. Although antibodies to this region can neutralize infection, their natural occurrence and evolution remain poorly understood. We previously identified human neutralizing antibodies to a conserved peptide within this region (HR2 coldspot). Here, we show that plasma IgG reactivity to this region remains rare, even after repeated antigen exposure. Longitudinal analysis over 30 months revealed continued somatic hypermutation of HR2-specific antibodies, yet none surpassed the potency or breadth of hr2.016, which emerged shortly after primary infection. Crystal structures of four HR2 stem helix antibodies revealed convergent recognition across distinct antibody lineages. Comparison of hr2.016 with its non-neutralizing clonal relative hr2.086 showed that structural convergence masks distinct binding kinetics. Surface plasmon resonance and molecular dynamics simulations revealed a more stable interaction network for hr2.016, with slower dissociation and prolonged S residence time. Neutralization required the IgG format, supporting an avidity-driven mechanism. Together, these findings define kinetic and avidity constraints governing neutralization at the HR2 stem helix and position hr2.016 as a resilient therapeutic candidate.
Virginia Crivelli, Concetta Guerra, M. Abernathy et al.· bioRxiv· 0 citations
APP and APLP2 Kunitz domains are identified as potent endogenous inhibitors of TMPRSS2-dependent respiratory virus infection and promising scaffolds for host-directed broad-spectrum antiviral strategies.
Jan Lawrenz, Shreyans Chatterjee, A. R. Alfonso et al.· bioRxiv· 0 citations
The results show the value of nanobody technology for identifying novel neutralising epitopes in the S2 region of beta-coronaviruses with potential for the development of new selective anti-viral agents.
John D. Clarke, Luke M. Jones, I. Buckle et al.· Scientific Reports· 0 citations
This work reframes the understanding of coronavirus spike evolution, revealing that tissue tropism operates through mechanisms fundamentally distinct from those dictating protease preference, and that protease preference and tissue tropism are mechanistically uncoupled.
Diego Cantoni, Wilhelm Furnon, S. Little et al.· bioRxiv· 0 citations
Evaluations utilizing surface plasmon resonance and pseudovirus assays demonstrate that these sublineages exhibit significantly reduced human ACE2 receptor engagement compared to their parental strain, which suggests these variants will soon spread globally and emphasize the critical need for ongoing surveillance to monitor D420N-carrying lineages.
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
