Towards an optimal monoclonal antibody with higher binding affinity to the receptor-binding domain of SARS-CoV-2 spike proteins from different variants

A highly efficient and robust multiple scales in silico protocol, consisting of atomistic constant charge Molecular Dynamics (MD), constant-charge coarse-grain (CG) MD and constant-pH CG Monte Carlo (MC), has been used to study the binding affinities, the free energy of complexation of selected antigen-binding fragments of the monoclonal antibody (mAbs) CR3022 (originally derived from SARS-CoV-1 patients almost two decades ago) and 11 SARS-CoV-2 variants including the wild type. CR3022 binds strongly to the receptor-binding domain (RBD) of SARS-CoV-2 spike protein, but chooses a different site rather than the receptor-binding motif (RBM) of RBD, allowing its combined use with other mAbs against new emerging virus variants. Totally 235,000 mAbs structures were generated using the RosettaAntibodyDesign software, resulting in top 10 scored CR3022-RBD complexes with critical mutations and compared to the native one, all having the potential to block virus-host cell interaction. Of these 10 finalists, two candidates were further identified in the CG simulations to be clearly best against all virus variants, and surprisingly, all 10 candidates and the native CR3022 did exhibit a higher affinity for the Omicron variant with its highest number of mutations (15) of them all considered in this study. The multiscale protocol gives us a powerful rational tool to design efficient mAbs. The electrostatic interactions play a crucial role and appear to be controlling the affinity and complex building. Clearly, mAbs carrying a lower net charge show a higher affinity. Structural determinants could be identified in atomistic simulations and their roles are discussed in detail to further hint at a strategy towards designing the best RBD binder. Although the SARS-CoV-2 was specifically targeted in this work, our approach is generally suitable for many diseases and viral and bacterial pathogens, leukemia, cancer, multiple sclerosis, rheumatoid, arthritis, lupus, and more.