The present proposal Hybrid Methods for Prediction and Design of Novel Human Influenza Antibodies will identify and develop novel influenza antibodies. There are three types of influenza viruses known, of which influenza type A is further differentiated into 2 groups, 17 subtypes based on the hemagglutinin (HA). Influenza A subtype H1 and H3 viruses seasonally infect humans. Several high-resolution structures of antibodies engaging influenza A HA have been determined by X-ray crystallography. Antibodies that engage the antigenically diverse head region of the trimeric HA molecules are most often narrow in breadth. Recently, two types of antibodies have been described that exhibit broad neutralization multiple subtypes of influenza. These two types of antibodies bind the more conserved stem region of HA or the conserved sialic acid receptor binding pocket within the head region. H5N1 influenza viruses traditionally infect birds, but have been responsible for several recent outbreaks limited to bird-to-human transmission. Recent research described adaptations of influenza H5N1 that confer respiratory droplet transmissibility from ferret to ferret, which may mimic the future development of a highly pathogenic pandemic human H5 virus in nature. It is poorly understood how humans vaccinated with conventional H5 immunogens might be protected from infection or disease with such mutants, which possess a limited number of surface point mutations in the HA. We present preliminary data on the isolation of human neutralizing monoclonal antibodies to the H5 head domain that recognize both wild-type and respiratory droplet transmissible H5 HAs from humans vaccinated with conventional H5 HA protein vaccine. The first objective of this proposal is to establish a pipeline RAPID to rapidly identify and characterize human antibodies that broadly neutralize influenza viruses an important strategy for the swift response to emerging threats to human health. We recently demonstrated that a new computational method termed multi-state design can recapitulate antibody maturation in silico, i.e., predicts mutations that increase antibody affinity to its target, and also predict antibody sequences encoding antibody proteins that are capable of broadly recognizing multiple target proteins. The second objective of this proposal is to design and test RAPID for (a) in silico maturation of head-binding antibodies to increase affinity for the HA antigen and (b) multi-state design to create stem-binding antibodies that recognize HAs of multiple different clades, groups, or even types.
The present proposal Hybrid Methods for Prediction and Design of Novel Human Influenza Antibodies will identify and develop novel influenza therapeutics though a combination of computational biology with hybrid methods in structural biology.