A Computational Framework for Determining the Breadth of Antibodies Against Highly Mutable Pathogens

Abstract

Highly mutable pathogens pose daunting challenges for antibody design. The usual criteria of high potency and specificity are often insufficient to design antibodies that provide long-lasting protection. This is due, in part, to the ability of the pathogen to rapidly acquire mutations that permit them to evade the designed antibodies. To overcome these limitations, design of antibodies with a larger neutralizing breadth can be pursued. Such broadly neutralizing antibodies (bnAbs) should remain targeted to a specific epitope, yet show robustness against pathogen mutability, thereby neutralizing a higher number of antigens. This is particularly important for highly mutable pathogens, like the influenza virus and the human immunodeficiency virus (HIV). The protocol describes a method for computing the "breadth" of a given antibody, an essential aspect of antibody design.

Keywords: Antibody breadth; Antibody design; Antibody–antigen binding; Coronavirus; Dengue; HIV; Influenza; Machine learning; Molecular modeling; bnAbs.