Computational Design of Epitope-Specific Functional Antibodies.

The ultimate goal of protein design is to introduce new biological activity. We propose a computational approach for designing functional antibodies by focusing on functional epitopes, integrating large-scale statistical analysis with multiple structural models. Machine learning is used to analyze these models and predict specific residue-residue contacts. We use this approach to design a functional antibody to counter the proinflammatory effect of the cytokine interleukin-17A (IL-17A). X-ray crystallography confirms that the designed antibody binds the targeted epitope and the interaction is mediated by the designed contacts. Cell-based assays confirm that the antibody is functional. Importantly, this approach does not rely on a high-quality 3D model of the designed complex or even a solved structure of the target. As demonstrated here, this approach can be used to design biologically active antibodies, removing some of the main hurdles in antibody design and in drug discovery.

Generation of potent mouse monoclonal antibodies to self-proteins using T-cell epitope "tags".

Immunization of mice or rats with a "non-self" protein is a commonly used method to obtain monoclonal antibodies, and relies on the immune system's ability to recognize the immunogen as foreign. Immunization of an antigen with 100% identity to the endogenous protein, however, will not elicit a robust immune response. To develop antibodies to mouse proteins, we focused on the potential for breaking such immune tolerance by genetically fusing two independent T-cell epitope-containing sequences (from tetanus toxin (TT) and diphtheria toxin fragment A (DTA)) to a mouse protein, mouse ST2 (mST2). Wild-type CD1 mice were immunized with three mST2 tagged proteins (Fc, TT and DTA) and the specific serum response was determined. Only in mice immunized with the T-cell epitope-containing antigens were specific mST2 serum responses detected; hybridomas generated from these mice secreted highly sequence-diverse IgGs that were capable of binding mST2 and inhibiting the interaction of mST2 with its ligand, mouse interleukin (IL)-33 (mIL-33). Of the hundreds of antibodies profiled, we identified five potent antibodies that were able to inhibit IL-33 induced IL-6 release in a mast cell assay; notably one such antibody was sufficiently potent to suppress IL-5 release and eosinophilia infiltration in an Alternaria alternata challenge mouse model of asthma. This study demonstrated, for the first time, that T-cell epitope-containing tags have the ability to break tolerance in wild-type mice to 100% conserved proteins, and it provides a compelling argument for the broader use of this approach to generate antibodies against any mouse protein or conserved ortholog.

Tailored amino acid diversity for the evolution of antibody affinity.

Antibodies are a unique class of proteins with the ability to adapt their binding sites for high affinity and high specificity to a multitude of antigens. Many analyses have been performed on antibody sequences and structures to elucidate which amino acids have a predominant role in antibody interactions with antigens. These studies have generally not distinguished between amino acids selected for broad antigen specificity in the primary immune response and those selected for high affinity in the secondary immune response. By studying a large data set of affinity matured antibodies derived from in vitro directed evolution experiments, we were able to specifically highlight a subset of amino acids associated with affinity improvements. In a comparison of affinity maturations using either tailored or full amino acid diversification, the tailored approach was found to be at least as effective at improving affinity while requiring fewer mutagenesis libraries than the traditional method. The resulting sequence data also highlight the potential for further reducing amino acid diversity for high affinity binding interactions.