Affinity maturation of cross-reactive CR3022 antibody against the receptor binding domain of SARS-CoV-2 via in silico site-directed mutagenesis

Abstract Introduction: Computational antibody engineering, affinity maturation, and screening greatly aid in vaccine and therapeutic antibody development by increasing the speed and accuracy of predictions. This study presents a protocol for designing affinity enhancing mutants of antibodies through in silico mutagenesis. A SARS-CoV-2 cross-reactive neutralizing antibody, CR3022, is considered as a case study.Methods: Our study aimed at generating antibody candidates from the human antibody CR3022 (derived from convalescent SARS patient) against the RBD of SARS-CoV-2 via in silico affinity maturation using site-directed mutagenesis in mutation hotspots. We optimized the paratope of the CR3022 antibody towards the RBD of SARS-CoV-2 for better binding affinity and stability, employing molecular modeling, docking, dynamics simulations, and molecular mechanics energies combined with generalized Born and surface area (MM-GBSA). Results: Nine antibody candidates were generated post in silico site-directed mutagenesis followed by preliminary screening. Molecular dynamics simulation of 100 nanoseconds and MM-GBSA analysis confirmed L-K45S as a lead antibody with the highest binding affinity against the RBD compared to wild-type and mutant counterparts. Three out of the remaining mutants were also found to have distinct epitopes and binding, possessing a potential to be developed against emerging SARS-CoV-2 variants of concern. Conclusion: The study demonstrates the use of an integrative antibody engineering protocol for enhancing affinity and neutralization potential through mutagenesis using robust open-source computational tools and predictors. This study highlights unique scoring and ranking methods for evaluating docking efficiency. It also underscores the importance of framework mutations for developing broadly neutralizing antibodies.