Structural prediction of antibody‐APRIL complexes by computational docking constrained by antigen saturation mutagenesis library data

IgA nephropathy (IgAN) is the most prevalent cause of primary glomerular disease worldwide, and the cytokine A PRoliferation‐Inducing Ligand (APRIL) is emerging as a key player in IgAN pathogenesis and disease progression. For a panel of anti‐human APRIL antibodies with known antagonistic activity, we sought to define their structural mode of engagement to understand molecular mechanisms of action and aid rational antibody engineering. Reliable computational prediction of antibody‐antigen complexes remains challenging, and experimental methods such as X‐ray co‐crystallography and cryoEM have considerable technical, resource, and throughput barriers. To overcome these limitations, we implemented an integrated and accessible experimental‐computational workflow to more accurately predict structures of antibody‐APRIL complexes. Specifically, a yeast surface display library encoding site‐saturation mutagenized surface positions of APRIL was screened against a panel of anti‐APRIL antibodies to rapidly obtain a comprehensive biochemical profile of mutational impact on binding for each antibody. The experimentally derived mutational profile data were used as quantitative constraints in a computational docking workflow optimized for antibodies, resulting in robust structural models of antibody‐antigen complexes. The model results were confirmed by solving the cocrystal structure of one antibody‐APRIL complex, which revealed strong agreement with our model. The models were used to rationally select and engineer one antibody for cross‐species APRIL binding, which quite often aids further testing in relevant animal models. Collectively, we demonstrate a rapid, integrated computational‐experimental approach to robustly predict antibody‐antigen structures information, which can aid rational antibody engineering and provide insights into molecular mechanisms of action. Accurate computational prediction of the structure of antibody‐antigen complexes remains challenging due, in part, to the difficulty in identifying near‐native models from incorrect poses. A workflow was developed that integrates experimental deep mutational scanning data with antibody‐antigen docking for generation of high‐quality models (confirmed by X‐ray crystallography). This workflow was applied for a panel of antibodies against the antigen APRIL that enabled rational selection and engineering of one antibody for cross‐species antigen binding.