Affinity enhancement of an in vivo matured therapeutic antibody using structure‐based computational design

Abstract

Improving the affinity of a high-affinity protein-protein interaction is a challenging problem that has practical applications in the development of therapeutic biomolecules. We used a combination of structure-based computational methods to optimize the binding affinity of an antibody fragment to the I-domain of the integrin VLA1. Despite the already high affinity of the antibody (Kd ;7 nM) and the moderate resolution (2.8 Å ) of the starting crystal structure, the affinity was increased by an order of magnitude primarily through a decrease in the dissociation rate. We determined the crystal structure of a high-affinity quadruple mutant complex at 2.2 Å . The structure shows that the design makes the predicted contacts. Structural evidence and mutagenesis experiments that probe a hydrogen bond network illustrate the importance of satisfying hydrogen bonding requirements while seeking higheraffinity mutations. The large and diverse set of interface mutations allowed refinement of the mutant binding affinity prediction protocol and improvement of the single-mutant success rate. Our results indicate that structure-based computational design can be successfully applied to further improve the binding of high-affinity antibodies.Keywords: antibody; affinity maturation; computational protein design; protein-protein interactions; binding energy prediction Supplemental material: see www.proteinscience.orgComputational techniques for small molecule design have recently become an established part of the drug discovery process, and many studies have been published in which structure-based design has led to high-affinity molecules (Jorgensen 2004). In contrast, there has been considerably less usage of computational design techniques in the field of protein engineering. This is due in part to the effectiveness of directed evolution experimental techniques (Crameri et al. 1996;Hanes et al. 1998), the computational complexity of treating full proteins, and the relative scarcity of structural information on engineered proteins. Very recently there have been a number of successes in computational protein design, such as the redesign of an internal domain-domain interface of an endonuclease (Chevalier et al. 2002), the design of a novel protein fold , the design of specific enzymatic activity into a periplasmic binding protein (Dwyer et al. 2004), and alteration of DNase-inhibitor pair binding specificity (Kortemme et al. 2004). It is now foreseeable that biomolecule therapeutic design could be addressed using computational techniques.Antibodies are the most widely used format for protein therapeutic applications for a variety of reasons, including high affinity and the ability to trigger immune responses

Computational design of antibody-affinity improvement beyond in vivo maturation

“…Previous work has addressed many problems related to the design of improved protein-protein binding affinity, such as the design of stable protein folds 2-4 , binding pockets for peptides and small molecules [5][6][7] , altered protein-protein specificity [8][9][10][11][12] , and altered enzymatic activity [13][14][15] . The design of improved antigen-binding affinity has met with limited success, however [16][17][18][19] . Challenges for protein-protein affinity design include conformational change upon binding, interfacial trapped water molecules, polar and charged side chains, and the trade-off of protein-solvent with protein-protein interactions from the unbound to bound state.…”

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

“…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)(5)(6). However, such approaches require a 3D structure of the antibody-antigen complex and rarely result in affinity gains greater than 10-fold.…”

Affinity maturation of antibodies assisted by in silico modeling

“…In the ideal case where high-resolution antibody structures or, preferably, antibody-antigen complex structures are available, determination of contact residues is straightforward and this information can be applied to guide the maturation process (19). If experimentally determined structures are not available but the paratope has been reliably mapped, a 3D model of the variable domains can be constructed (20) and the residues affecting affinity can be projected onto it (21), thereby facilitating the selection of candidate positions for maturation.…”