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.
Improving the temperature characteristics and catalytic efficiency of a mesophilic xylanase from Aspergillus oryzae, AoXyn11A, by iterative mutagenesis based on in silico design
