Computational redesign of Beta-27 Fab with substantially better predicted binding affinity to the SARS-CoV-2 Omicron...

During the COVID-19 pandemic, SARS-CoV-2 has caused large numbers of morbidity and mortality, and the Omicron variant (B.1.1.529) was an important variant of concern. To enter human cells, the receptor-binding domain (RBD) of the S1 subunit of SARS-CoV-2 (SARS-CoV-2-RBD) binds to the peptidase domain (PD) of Angiotensin-converting enzyme 2 (ACE2) receptor. Disrupting the binding interactions between SARS-CoV-2-RBD and ACE2-PD using neutralizing antibodies is an effective COVID-19 therapeutic solution. Previous study found that Beta-27 Fab, which was obtained by digesting the full IgG antibodies that were isolated from a patient infected with SARS-CoV-2 Beta variant, can neutralize Victoria, Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), and Delta (B.1.617.2) variants. This study employed computational protein design and molecular dynamics (MD) to investigate and enhance the binding affinity of Beta-27 Fab to SARS-CoV-2-RBD Omicron variant. MD results show that five best designed Beta-27 Fabs (Beta-27-D01 Fab, Beta-27-D03 Fab, Beta-27-D06 Fab, Beta-27-D09 Fab and Beta-27-D10 Fab) were predicted to bind to Omicron RBD in the area, where ACE2 binds, with significantly better binding affinities than Beta-27 Fab and ACE2. Their enhanced binding affinities are mostly caused by increased binding interactions of CDR L2 and L3. They are promising candidates that could potentially be employed to disrupt the binding between ACE2 and Omicron RBD.