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

Abstract

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.

A Proliferation Inducing Ligand (APRIL) targeted antibody is a safe and effective treatment of murine IgA nephropathy

“…Visterra Hierotope platform (Visterra, Inc., Waltham, MA) was used to identify a "network" of conserved residues constituting a preferred epitope for antibody targeting (a). 27,28 Amino acid positions in APRIL (a proliferation-inducing ligand) with the highest network score are color coded in blue. This predicted epitope points to a region of APRIL corresponding to the high-affinity receptor binding site (CRD2 site) critical for both transmembrane activator and CAMLinteractor (TACI) and B-cell maturation antigen (BCMA) receptor blocking.…”

“…The epitope of VIS649 and stoichiometry of antibodytarget binding have been characterized at high resolution through the concerted use of x-ray crystallography and epitope mapping studies ( Figure 6 [27][28][29] ). In accordance with its activity profile, VIS649 targets a critical quaternary epitope within APRIL that overlaps with the high-affinity (CRD2) receptor binding site and spans the interface between 2 monomers to effectively neutralize oligomeric APRIL, the biologically relevant form of APRIL ( Figure 6a).…”

Informatics and Computational Methods in Natural Product Drug Discovery: A Review and Perspectives

“…These libraries can then be screened in silico (e.g., using molecular docking simulations as described in section 3.1.2) to find structures that can be engineered in the lab. Antibodies, in particular, are particularly well-suited to computational mutagenesis, by modifying amino acids in binding regions (Sivasubramanian et al, 2009; Wollacott et al, 2019). The feasibility of mutagenesis techniques in the context of NP drug discovery was demonstrated by Chen et al, who generated a library of analogs of the 7-residue NP peptide HUN-7293 to optimize its inhibitory effects on cell-adhesion (Chen et al, 2002).…”

Characterizing Epitope Binding Regions of Entire Antibody Panels by Combining Experimental and Computational Analysis of Antibody: Antigen Binding Competition

“…While it is generally not clear from computational scoring alone which docking model is the most accurate, it was shown in both retrospective and prospective studies that a small number (generally 3–5) of binding assays for computationally designed Ag variants can reliably enable the various docking models to be confirmed or rejected and thereby identify the general epitope region [ 9 ]. Complementarily, experimental data can be used to focus docking and energy minimization to better define binding mode or epitope [ 41 ]. In general, combined computational–experimental approaches balance cost and accuracy in characterizing epitope sites.…”