Redesign of a cross-reactive antibody to dengue virus with broad-spectrum activity and increased in vivo potency

Abstract

Affinity improvement of proteins, including antibodies, by computational chemistry broadly relies on physics-based energy functions coupled with refinement. However, achieving significant enhancement of binding affinity (>10-fold) remains a challenging exercise, particularly for cross-reactive antibodies. We describe here an empirical approach that captures key physicochemical features common to antigen-antibody interfaces to predict proteinprotein interaction and mutations that confer increased affinity. We apply this approach to the design of affinity-enhancing mutations in 4E11, a potent cross-reactive neutralizing antibody to dengue virus (DV), without a crystal structure. Combination of predicted mutations led to a 450-fold improvement in affinity to serotype 4 of DV while preserving, or modestly increasing, affinity to serotypes 1-3 of DV. We show that increased affinity resulted in strong in vitro neutralizing activity to all four serotypes, and that the redesigned antibody has potent antiviral activity in a mouse model of DV challenge. Our findings demonstrate an empirical computational chemistry approach for improving protein-protein docking and engineering antibody affinity, which will help accelerate the development of clinically relevant antibodies. (1). Engineering improved affinity and specificity of these compounds can augment their potency and safety while decreasing required dosages. Production of antibodies with binding properties of interest typically relies on methods involving screening large numbers of clones generated by the immune system or by mutant libraries (2, 3). Alternatively, computer-based design offers the potential to rationally mutate available antibodies for improved properties, including enhanced affinity and specificity to target antigens. Recently, several successful examples of antibody affinity improvement by computational methods using physical modeling with energy minimization have been described (4-6). However, such approaches require a 3D structure of the antibody-antigen complex and rarely result in affinity gains greater than 10-fold. Further, these approaches are sensitive to precise atomic coordinates, rendering them inapplicable to computer-generated models. More significantly, enhancement of affinity in the context of an antibody that recognizes multiple antigens (i.e., crossreactive) remains a particular challenge.Dengue is the most medically relevant arboviral disease in humans, with an estimated 3.6 billion people at risk for infection. More than 200 million infections of dengue virus (DV) are estimated to occur globally each year (7). The incidence, geographical outreach, and number of severe disease cases of dengue are increasing (8, 9), making DV of increasing concern as a human pathogen. The complex of DVs is composed of four distinct serotypes (designated DV1-4) (10), which vary from one another at the amino acid level by 25-40%. The sequence and antigenic variability of DVs have challenged efforts to develop an effective vaccine or therapeutic against a...

Rational Engineering and Characterization of an mAb that Neutralizes Zika Virus by Targeting a Mutationally Constrained Quaternary Epitope

“…The CDR change was accepted if the post-minimization CDR-antigen interaction energy was favorable when compared to the interaction energy before making the CDR change. Promising mutations from each interaction were used to remodel the antibody, dock against the antigen and generate a new antigen-antibody model using MLR as described before (Tharakaraman et al, 2013). The data from each iteration was used in remodeling the CDR loops, and subsequently to dock and refine the antigen-antibody model using the MLR metric (Tharakaraman et al, 2013).…”

“…Promising mutations from each interaction were used to remodel the antibody, dock against the antigen and generate a new antigen-antibody model using MLR as described before (Tharakaraman et al, 2013). The data from each iteration was used in remodeling the CDR loops, and subsequently to dock and refine the antigen-antibody model using the MLR metric (Tharakaraman et al, 2013). Certain CDR mutations had a detrimental effect on the interatomic network so different variants were tried or compensatory mutations were made in the spatially proximal CDR or framework regions.…”

A broadly neutralizing human monoclonal antibody is effective against H7N9

“…In the case of anti-HIV antibodies that target the CD4-binding site, structurebased design of a single mutant, Gly54→Phe, resulted in a 10-fold enhancement in activity (21); additional changes resulted in decreasing possible pathways for resistance development (22,23). Also, a recent study examined an antidengue antibody and engineered it to increase its affinity to serotype 4 resulting in increased potency to this serotype (24). Notably, this last study was completed in the absence of structural information and utilized an informatics-based approach, using existing information about antibody-antigen engagement.…”

Amino acid interaction networks provide a new lens for therapeutic antibody discovery and anti-viral drug optimization

“…Marvin et al [46] Yes Single Energetic minimization 6 9-fold Clark et al [47] Yes Single Energetic minimization 4 10-fold Lippow et al [48] Yes Single Energetic minimization 6 140-fold Farady et al [49] Yes Multiple (2) Rule-based/energetics-based 1 14-fold Diskin et al [50] Yes Many Structure-guided 1 3-4-fold Tharakaraman et al [35 ] No Multiple (4) Empirical informatics 5 450-fold Thakkar et al [51] Yes Multiple (2) Shape/hydrophobicity mutations are verified experimentally by biochemical binding assessment. While there are multiple methods available to identify amino acid changes within the paratope that can increase antibody-antigen contacts, the socalled collateral damage, in the form of decreased antibody stability or reduced binding to antigen due to repositioning of neighboring amino acids, is difficult to a prioi predict.…”

“…One example of such a quantitative metric is the amino acid interface fitness (AIF) [35 ], which captures the propensity of inter-protein pairwise contacts of amino acids in the complementarity determining region (CDR) positions. AIF-enhancing mutations are the ones within the CDRs or the framework region (FWR) that contribute to more favorable epitope contacts, as evaluated by increasing the connectivity network.…”