High-binding affinity nanobody against SARS-CoV-2 XBB.1.5: Computational-based protein engineering

The emergence of the SARS-CoV-2 variant XBB.1.5 has triggered a global health crisis by enhancing viral entry into cells via its spike protein. This study addresses the urgent need to develop neutralizing nanobodies (Nbs) to counteract the SARS-CoV-2 virus. Our aim was to identify a lead Nb and enhance its binding affinity to the receptor-binding motif (RBM) on the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein (S-protein) using computational methods. A total of 29 Nbs were screened against the XBB.1.5 RBD using the HDOCK server to select a lead Nb. This investigation revealed that Nb_7KGK exhibited the highest binding affinity. Subsequently, unfavorable residues of Nb_7KGK were mutated to further enhance binding affinity. As expected, aromatic residues (tyrosine, tryptophan, histidine) were primarily mutated to improve binding affinity, resulting in a new Nb variant named Nb_7KGK(7), with heightened affinity. The engineered Nb_7KGK(7) demonstrated improved chemical interactions with the RBD. Predicted physicochemical properties, such as pI value, total charge, Clashscore, and MolProbity score of the engineered Nb, were also improved. This study highlights the potential of computational design as a preliminary step toward developing effective Nbs against the emerging SARS-CoV-2 XBB.1.5 variant.