Antibody drugs play a critical role in controlling many types of diseases such as infectious diseases, cancer, autoimmune diseases and inflammation. The earliest used technology for the generation of therapeutic antibodies is raising antibodies against a target antigen in immunized mice or directed evolution of antibodies with a low starting affinity for the desired antigen. However, these methods remain time consuming and cannot target a specific antigen epitope. Computational design methods provide the means for using structure information and interaction energies to inform the design of antibodies with high affinity for a given antigen epitope. We have developed a new computational framework named Optimal Method for Antibody Variable region Engineering (OptMAVEn) for de novo design of fully human antibody variable domain to bind any specified antigen. OptMAVEn simulates in silico the in vivo steps of antibody generation and evolution, and is capable of capturing the critical structural features responsible for affinity maturation of antibodies. As one case study, OptMAVEn was applied to design antibodies in the format of single-chain variable fragment (scFv) against a small peptide antigen (2D10). Five de novo designed scFvs, processing distinct sequences compared to all existing nature antibody sequences, were generated and experimentally characterized. Among them, three scFvs show binding affinity to the peptide 2D10 at low nM level. Interestingly, they exhibit considerably different binding modes with peptide 2D10. The results provide proof of principle that computational methods can efficiently de novo design thermally and conformationally stable antibodies with high binding affinity to antigens.

Development and validation of computational methods for de novo design of antibody variable regions

251st National Meeting (2016)

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.