Computational prioritization of RBD-targeting nanobodies across SARS-CoV-2 Omicron sublineages, followed by rational biparatopic design and targeted experimental validation.

The ongoing evolution of SARS-CoV-2, particularly Omicron sublineages harboring extensive RBD mutations, compromises the effectiveness of many existing neutralizing nanobodies. Here, we present an integrated computational-experimental framework to prioritize nanobodies with favorable predicted interaction profiles across mutated RBD backgrounds and to explore their potential for biparatopic engineering. Thirty-two nanobody-RBD complexes from the Protein Data Bank were computationally screened across eight Omicron sublineages (BA.1-XBB.1.5). Selected nanobodies were then further evaluated against the Wuhan reference strain and XBB.1.5-the predominant sublineage at the onset of laboratory testing-using molecular simulations and conventional virus neutralization tests (cVNTs) performed in three biological replicates. These analyses led to the prioritization of MR17 and Sb14. In subsequent cVNT assays, MR17 showed a significantly higher neutralization titer against XBB.1.5 than against the Wuhan reference strain, whereas Sb14 showed comparable titers against both targets. A biparatopic Sb14-MR17 construct exhibited a significantly higher neutralization titer against XBB.1.5 than either monomeric nanobody. In addition, the interaction profiles of selected candidates with the emerging NB.1.8.1 RBD were evaluated computationally. Collectively, these results highlight multi-sublineage computational triage, together with targeted experimental validation, as a practical strategy for prioritizing nanobody candidates under ongoing SARS-CoV-2 antigenic drift.