Porcine deltacoronavirus (PDCoV) can experimentally infect a variety of animals. Human infection by PDCoV has also been reported. Consistently, PDCoV can use aminopeptidase N (APN) from different host species as receptors to enter cells. To understand this broad receptor usage and interspecies transmission of PDCoV, we determined the crystal structures of the receptor binding domain (RBD) of PDCoV spike protein bound to human APN (hAPN) and porcine APN (pAPN), respectively. The structures of the two complexes exhibit high similarity. PDCoV RBD binds to common regions on hAPN and pAPN, which are different from the sites engaged by two alphacoronaviruses: HCoV-229E and porcine respiratory coronavirus (PRCoV). Based on structure guided mutagenesis, we identified conserved residues on hAPN and pAPN that are essential for PDCoV binding and infection. We report the detailed mechanism for how a deltacoronavirus recognizes homologous receptors and provide insights into the cross-species transmission of PDCoV.

Middle East respiratory syndrome coronavirus (MERS-CoV) and the pangolin MERS-like coronavirus MjHKU4r-CoV-1 employ dipeptidyl peptidase 4 (DPP4) as an entry receptor. MjHKU4r-CoV-1 could infect transgenic mice expressing human DPP4. To understand the mechanism of MjHKU4r-CoV-1 entry into cells, we determined the crystal structures of the receptor binding domain (RBD) of MjHKU4r-CoV-1 spike protein bound to human DPP4 (hDPP4) and Malayan pangolin DPP4 (MjDPP4), respectively. The overall hDPP4-binding mode of MjHKU4r-CoV-1 RBD is similar to that of MERS-CoV RBD. MjHKU4r-CoV-1 RBD shows higher binding affinity to hDPP4 compared to the bat MERS-like coronavirus Ty-BatCoV-HKU4. Via swapping residues between MjHKU4r-CoV-1 RBD and Ty-BatCoV-HKU4 RBD, we identified critical determinants on MjHKU4r-CoV-1 that are responsible for virus usage of hDPP4. Our study suggests that MjHKU4r-CoV-1 is more adapted to the human receptor compared to the bat HKU4 coronavirus and highlights the potential of virus emergence into the human population.

Human astrovirus (HAstV) is a major cause of viral enteritis in children and the older adults worldwide. The eight serotypes of classical HAstVs (HAstV1-8), employ the neonatal Fc receptor (FcRn) as an entry receptor. To elucidate the receptor binding mechanism of HAstVs, we determined the crystal structure of the HAstV8 spike protein complexed with human FcRn. The structure reveals that the HAstV8 spike protein engaged FcRn via a conserved surface depression, which is also present across the other seven classical HAstVs serotypes. The binding interface of HAstV8 spike protein on FcRn largely overlaps with the footprints of three clinically approved FcRn blockers to treat an autoimmune disease. Accordingly, these FcRn inhibitors potently suppress astrovirus infection, significantly reducing viral RNA levels in astrovirus permissive Caco2 cells. Therefore, our study reports a conserved receptor recognition mechanism among human astroviruses and suggests the potential of repurposing clinically approved therapeutics to treat astrovirus infection.

The design of antibody complementarity-determining regions (CDRs) is pivotal for therapeutic development, yet remains challenged by the complexity of the antibody–antigen interface. Existing methods, which largely rely on pairwise relationships, struggle to capture intrinsic multibody interactions and global structural constraints. To address this limitation, we propose the Relation-Aware Graph–Hypergraph Network (RA-GHN), a novel framework that systematically unifies graph-based pairwise modeling with biophysical hypergraphs to capture high-order co-operative effects. Specifically, we construct a heterogeneous hypergraph representation featuring three complementary hyperedge types: epitope–paratope interaction patches, spatial structural clusters, and high-degree hub structures, enabling a comprehensive encoding of interface physics. Architecturally, RA-GHN adopts a dual-pathway design that synergistically integrates a Relation-Aware Equivariant Graph Network (RA-EGN) with an Equivariant Hypergraph Encoder , strictly preserving SE (3)-equivariance throughout the sequence–structure co-design process. Furthermore, to mitigate folding hallucination and sequence overfitting commonly observed in generative models, we introduce the Structure–Sequence Decoupling Index (SSDI), which employs a harmonic mean to non-linearly balance structural physical reliability with sequence novelty. Extensive experiments demonstrate that RA-GHN significantly outperforms state-of-the-art baselines across standard benchmarks and affinity optimization tasks, validating that the fusion of high-order geometric modeling with rigorous evaluation criteria enables the generation of physically feasible and functionally innovative antibodies. Highlights: RA-GHN co-designs antibody sequence and structure via hypergraphs. Biophysical hyperedges capture high-order effects at binding interfaces. Dual equivariant pathways synergistically guarantee local geometry and global shape. SSDI metric addresses folding hallucination in antibody evaluation.