Iterative In Silico Screening for Optimizing Stable Conformation of Anti-SARS-CoV-2 Nanobodies

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 were measured through the NeighborSearch 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. To validate the efficacy ranking of the nanobodies we obtained, we calculated the binding affinities ({Delta}G) and dissociation constants (Kd) of all screened nanobodies using the PRODIGY web tool, and predicted the stability changes induced by all possible point mutations in nanobodies using the MAESTROWeb server. Furthermore, we examined the performance of the relationship between nanobodies ranking and their number of mutation-sensitive sites (Spearman correlation > 0.68), the results revealed a robust correlation, indicating that the superior nanobodies identified through our screening process exhibited fewer mutation hotspots and higher stability. This correlation analysis demonstrates the validity of our screening criteria, underscoring the suitability of these nanobodies for future development and practical implementation. In conclusion, this three-step 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.