Amino acid interaction networks provide a new lens for therapeutic antibody discovery and anti-viral drug optimization.

Identification of epitopes on viral proteins for the design/identification of broadly-neutralizing monoclonal antibodies (bnAbs) or specific immunogens for vaccine development is hampered by target amino acid diversity. Recently, bnAbs have been isolated for variable viruses by screening B cells from infected individuals for neutralization breadth. Epitope mapping and structural analysis of bnAbs revealed, while some of these bnAbs target glycan moieties, most target protein regions that are conserved in sequence and/or structure. However, almost universally viruses develop mutations that allow escape from neutralization suggesting protein function may not be dependent on the observed conservation. An alternative method for identification of conserved amino acid sequences utilizes an amino acid network-based approach. Calculation of a significant interaction network (SIN) score allows for selection of amino acids that are conserved and constrained within the protein system. Amino acids with high SIN scores are predicted to mutate at lower frequency due to the impact mutation has on the structure/function of a protein. By ascertaining regions of high SIN score, therapeutics can be appropriately designed to target these regions of low mutability. Further, the use of atomic interaction networks to examine protein structure and protein-protein interfaces can complement existing structure-based computational approaches for therapeutic engineering.