OptMAVEn--a new framework for the de novo design of antibody variable region models targeting specific antigen epitopes.

Tong Li,Costas D Maranas,Robert J Pantazes,Stefan Dübel

doi:10.1371/journal.pone.0105954

Abstract

Antibody-based therapeutics provides 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 low affinity for the desired antigen. However, these methods remain time consuming, cannot target a specific epitope and do not lead to broad design principles informing other studies. Computational design methods can overcome some of these limitations by using biophysics models to rationally select antibody parts that maximize affinity for a target antigen epitope. This has been addressed to some extend by OptCDR for the design of complementary determining regions. Here, we extend this earlier contribution by addressing the de novo design of a model of the entire antibody variable region against a given antigen epitope while safeguarding for immunogenicity (Optimal Method for Antibody Variable region Engineering, OptMAVEn). 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. In addition, a humanization procedure was developed and incorporated into OptMAVEn to minimize the potential immunogenicity of the designed antibody models. As case studies, OptMAVEn was applied to design models of neutralizing antibodies targeting influenza hemagglutinin and HIV gp120. For both HA and gp120, novel computational antibody models 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 computational antibody models with both optimized binding affinity to antigens and reduced immunogenicity.

Highlights

Therapeutic antibodies are widely recognized to be among the most promising agents to treat various diseases, including cancers, immune disorders, and infections [1,2]
Our results demonstrate that OptMAVEn is a computational framework for an open challenge that could make significant contribution to the development of a new generation of therapeutic antibodies and vaccines
We have developed OptCDR for the de novo design of antibody binding pockets composed of complementarity determining regions (CDRs) against any specified antigen

Summary

Introduction

Therapeutic antibodies are widely recognized to be among the most promising agents to treat various diseases, including cancers, immune disorders, and infections [1,2]. The low clinical success rate using mouse antibodies reflects that these foreign proteins can be highly immunogenic in humans, and they typically have weak interactions with human complement and antibody receptors, resulting in inefficient effector functions [3] These limitations have largely been overcome by grafting the variable domains of a mouse monoclonal antibody to the constant domains of a human antibody, a process known as chimerization [4,5]. Phage display [8,9], a technique based on the presentation of peptides or protein fragments on the surface of bacteriophages, is most widely used and offers robust and complementary routes to the generation of potent human antibodies Despite these advances in the design of antibodies, current experimental methods still have considerable limitations and cannot: (1) target a specific antigen epitope, (2) provide universally applicable structural design routes, and (3) rationally engineer mutations with significantly reduced immunogenicity

Methods

Results

Conclusion