382315 Optmaven: De Novo Design of Antibody Variable Regions

Tuesday, November 18, 2014: 1:24 PM
205 (Hilton Atlanta)
Tong Li, chemical engineering, PSU, state college, PA, Robert J. Pantazes, Chemical Engineering, University of California, Santa Barbara, Santa Barbara, CA and Costas D. Maranas, Department of Chemical Engineering, The Pennsylvania State University, University Park, PA

Antibody-based therapeutics provide novel and efficacious treatments for a number of diseases. Traditional experimental approaches for designing therapeutic antibodies rely on raising antibodies against a target antigen in an immunized animal 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. The design of only complementarity determining regions (CDR) was the focus of earlier efforts based on the OptCDR framework. Here we extend this earlier framework for the de novo design of the entire antibody variable region against a given antigen epitope. OptMAVEn simulates in silico the in vivo steps of antibody generation and evolution, and is capable of capturing key critical structural features responsible for affinity maturation of antibodies. In addition, a new humanization procedure was developed, tested, and incorporated into OptMAVEn to minimize the immunogenicity of the designed antibodies. As case studies, OptMAVEn was applied to design neutralizing antibodies targeting influenza hemagglutinin (HA) and HIV gp120. For both HA and gp120, novel antibodies with numerous interactions with their target epitopes were generated. The observed rates of mutations and types of amino acid changes during in silico affinity maturation are consistent with what has been observed during in vivo affinity maturation. The results demonstrate that OptMAVEn can efficiently generate diverse antibodies with both optimized binding affinity to antigens and reduced immunogenicity. The computational framework presented here should facilitate the development of a new generation of therapeutic antibodies.

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