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. 2013 Apr 23;110(17):E1555-64.
doi: 10.1073/pnas.1303645110. Epub 2013 Apr 8.

Redesign of a Cross-Reactive Antibody to Dengue Virus With Broad-Spectrum Activity and Increased in Vivo Potency

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Free PMC article

Redesign of a Cross-Reactive Antibody to Dengue Virus With Broad-Spectrum Activity and Increased in Vivo Potency

Kannan Tharakaraman et al. Proc Natl Acad Sci U S A. .
Free PMC article

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 protein-protein 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.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.
Fig. 1.
Sensitivity and specificity of MLR and ZRANK methods evaluated on test dataset (44 native-like and 633 decoy structures). (A) Effect of window size on prediction accuracy. The window size represents the number of predicted positives. Prediction accuracy (or sensitivity) is determined by the number of test case structures (44 in total) correctly predicted. The rmsd threshold used for clustering ligand conformations was varied (3 Å, 5 Å, and 10 Å), and its effect on prediction accuracy was analyzed. (B) ROC curves at rmsd cutoffs 3 Å, 5 Å, and 10 Å for MLR and ZRANK predictions. The area under ROC curves at rmsd cutoffs 3 Å (MLR, 0.948; ZRANK, 0.862), 5 Å (MLR, 0.95; ZRANK, 0.718), and 10 Å (MLR, 0.943; ZRANK, 0.717) indicate that MLR is more efficient at recognizing native-like structures.
Fig. 2.
Fig. 2.
Sequence and structural determinants of poor DV4 binding. (A) Sequence alignment of EDIII region of representative strains from each of the four serotypes. Putative antibody binding residues are highlighted in yellow. Residues at 307, 329, 361, 364, 385, 388, and 390 differentiate DV4 from the remainder of the sequences; these are marked in red and numbered. Residue contacts made by the five antibody mutations are boxed. (B) Structural model of 4E11–EDIII interaction. Sequence positions that discriminate DV4 from other strains are labeled, and the side chains of amino acids therein are represented as sticks.
Fig. 3.
Fig. 3.
In vitro neutralizing activity of antibodies assessed by FRNT. Neutralization assays were performed with DV1–4 and antibodies 4E11 WT and 4E5A. Serial dilutions of antibody were mixed with equal amounts of virus and added to Vero cell monolayers followed by a viscous overlay. After 4–6 d, cells were fixed, and foci were immunostained and counted. Data points represent averages of duplicates with error bars representing SD. A standard four-parameter logistic model was fit to the data using least squares regression.
Fig. 4.
Fig. 4.
In vivo DV2 challenge model with prophylactic antibody administration. AG129 mice were administered 4E5A antibody (1 mg/kg or 5 mg/kg) or vehicle (PBS) 1 d before infection with DV2. Sera were collected 3 d postinfection, and virus was titered by quantitative PCR, with log10(CCID50/mL) titer extrapolated from a standard curve of a sample with known titer. The dashed line represents the approximate limit of detection, and error bars represent the SEM.

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