Application of an integrated computational antibody engineering platform to design SARS-CoV-2 neutralizers

Abstract

As the COVID-19 pandemic continues to spread, hundreds of new initiatives including studies on existing medicines are running to fight the disease. To deliver a potentially immediate and lasting treatment to current and emerging SARS-CoV-2 variants, new collaborations and ways of sharing are required to create as many paths forward as possible. Here we leverage our expertise in computational antibody engineering to rationally design/optimize three previously reported SARS-CoV neutralizing antibodies and share our proposal towards anti-SARS-CoV-2 biologics therapeutics. SARS-CoV neutralizing antibodies, m396, 80R, and CR-3022 were chosen as templates due to their diversified epitopes and confirmed neutralization potency against SARS. Structures of variable fragment (Fv) in complex with receptor binding domain (RBD) from SARS-CoV or SARS-CoV2 were subjected to our established in silico antibody engineering platform to improve their binding affinity to SARS-CoV2 and developability profiles. The selected top mutations were ensembled into a focused library for each antibody for further screening. In addition, we convert the selected binders with different epitopes into the trispecific format, aiming to increase potency and to prevent mutational escape. Lastly, to avoid antibody induced virus activation or enhancement, we applied NNAS and DQ mutations to the Fc region to eliminate effector functions and extend half-life.

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

“…This provides a strategy to target specific regions of allosteric interactions therapeutically. Recently, Riahi et al (Riahi et al, 2021) presented a combined physics-based and machine learned-based computational Ab engineering platform to improve the binding affinity to SARS-CoV-2. They minimized (protonated, if needed) the Protein Data Bank (PDB) structures (Burley et al, 2017) using the “Molecular Operating Environment” (MOE) program (Vilar et al, 2008), and continued with ROSETTA (Ó Conchúir et al, 2015; Weitzner et al, 2017) and FastRelax ROSETTA (Maguire et al, 2021) to later apply machine learning.…”

Epistasis at the SARS-CoV-2 Receptor-Binding Domain Interface and the Propitiously Boring Implications for Vaccine Escape

“…An alternative approach is to apply backbone minimization and side chain repacking within a local region of the structure centered at the modified site as implemented within Rosetta through the Flex ddG protocol ( 45 ). This approach has been used to accurately predict mutations conferring increased infectivity for SARS-CoV-2 ( 46 ) and rationally design NAb against the antigen ( 47 , 48 ) but is significantly slower than introducing mutations without even local optimization. With or without local optimization prior to filtering, global repacking and minimization may be desired to confirm top candidates.…”

Epistasis at the SARS-CoV-2 RBD Interface and the Propitiously Boring Implications for Vaccine Escape

“…Alternative approaches have been reviewed (41)(42)(43)(44), but the minimal approach pursued maximizes the breadth of candidate mutations considered and is able to recapitulate experimental results. As an alternative to constructing the variant ensembles starting with the crystal structure, we considered starting with the conformational ensemble constructed for the WT and applying a reduced protocol of iterative minimization and repacking for conformational optimization.…”

“…An alternative approach is to apply backbone minimization and sidechain repacking within a local region of the structure centered at the modified site as implemented within Rosetta through the Flex ddG protocol (44). This approach has been used to accurately predict mutations conferring increased infectivity for SARS-CoV-2 (45) and rationally design NAb against the antigen (46,47) The total score and dG separated of the mutant structures were computed. Unlike in earlier steps where the unbound state was repacked during the dG separated calculation, no repacking was conducted for the reasons described above.…”