A High-Throughput Computational Pipeline for Selection of Effective Antibody Therapeutics Against Viruses

Continuous evolution of viruses such as severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) generates multiple variants that can evade approved neutralizing antibodies (NAbs) or other antibody therapeutics. Frequently retesting whether a panel of NAbs has retained their neutralization potential against such emerging variants is costly and time-consuming. In such a case, computational tools that mimic the experiment binding affinity of the target protein of the virus and NAbs could play a pivotal role in providing researchers with quick understanding and guidance for using a set of NAbs effective against new virus variants. Hence, we developed and validated a computational pathway for binding affinity prediction in protein complexes. We demonstrated the application of this computational pathway in the rapid screening of a panel of NAbs for their efficacy in targeting the SARS-CoV-2 Omicron variant using benchmarking with the experimental data. We believe that the proposed computational pathway could help antiviral researchers in the initial screening of multiple NAbs against viruses of interest.