ABSTRACT
Abstract--Nanobodies (Nbs or VHHs) are single-domain antibodies (sdAbs) derived from camelid heavy-chain antibodies. The variable region of these nanobodies has special and unique characteristics, such as small size, good tissue penetration, and cost-effective production, making nanobodies a good candidate for the diagnosis and treatment of viruses. Identifying effective nanobodies against the COVID-19 would help us defeat this dangerous virus or other unknown variants in future. Herein, we introduce an in silico screening strategy for optimizing stable conformation of anti-SARS-CoV-2 nanobodies. Firstly, various complexes containing nanobodies were downloaded from the RCSB database, which were identified from immunized llamas. The primary docking between nanobodies and the SARS-CoV-2 spike protein receptor-binding domain was performed through ClusPro program, with the manually screening that leaving the reasonable conformation to the next step. Then, the binding distances of atoms between the antigen-antibody interfaces was measured through the Neighbor Search algorithm. Finally, filtered nanobodies were acquired according to HADDOCK scores through HADDOCK docking the Covid spike protein with nanobodies under restrictions of calculated molecular distance between active residues and antigenic epitopes less than 4.5 A. In this way, those nanobodies which with more reasonable conformation and with stronger neutralizing efficacy were acquired. This three-steps screening strategy iteratively in Silico greatly improved the accuracy of screening desired nanobodies compared to using only ClusPro docking or default HADDOCK docking settings. It provides new ideas for the screening of novel antibodies and computer-aided screening methods.