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 approximately 7 nM) and the moderate resolution (2.8 A) 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 A. 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 higher-affinity 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.

Figure 1.

Visualization of the mutated positions (yellow and orange) on the antibody–antigen interface. The view looks down through the antigen onto the complementary determining region (CDR) loops of the antibody. Only residues with an atom within 5 Å of the opposite side of the interface and mutation positions are shown. The antigen residues are colored purple, the light chain is green, and the heavy chain is blue. Positions of beneficial mutations are shown in orange. Expressed mutants at each position are noted to the right of the position number. All structural figures have been made using PyMOL (DeLano Scientific).

Figure 2.

Changes in binding for the mutants. (A) Comparison of calculated with experimental ΔΔG of binding. All mutants with competition ELISA EC50s near wild type were reevaluated using the solution-based KinExA assay (inset). (B) Examples of the solution-phase (KinExA) binding curves for wild type and mutants. Experimental numbers were derived from the ratio of the wild-type affinity to the mutant affinity (ΔΔG) = −RTln(K^WT_D/K^mut_D). All nonbinders and mutants for which only an estimate is available are not shown.

Figure 3.

Visualization of the quadruple mutant crystal structure (dark gray) near side chain repacking mutants. (A) Comparison to the predicted structure (light gray) in the vicinity of the S28Q mutation in the light chain. The predicted structure forms a similar stacking-like interaction between Tyr264 on antigen and the glutamine. The electron density (2F_o − F_c, σ = 1.1) indicates that the tyrosine has swung inward toward the bulk of the antigen. (B) Comparison of the wild-type (light gray) and quadruple mutant crystal (dark gray) structures in the vicinity of the N52Y mutation in the light chain.

Figure 4.

The quadruple mutant crystal structure in the vicinity of the metal-contacting H:Asp101 residue. The H:Gly100 position on the antibody is colored yellow. A pocket in the antigen (purple) lined with polar residues and water molecules (red spheres) is visible below the metal ion (orange sphere). A new residue at the G100 position would extend its C^β atom toward the water molecules on the right.

Figure 5.

Comparison of the wild-type (light gray) and quadruple mutant (dark gray) crystal structures in the vicinity of the H:T50V mutation. The wild-type threonine hydrogen bonds with the tryptophan. When substituted with a valine, the local environment rearranges to eliminate an unsatisfied hydrogen bond.