Ted M. Ross

This study develops COBRA HA mRNA vaccines that induce broad immunity against diverse influenza strains in mice.

This study designs influenza A(H3N2) vaccine candidates using protein language models and machine learning, showing two candidates provide broad protection against current and drifted strains in mice.

This study maps H2 influenza virus antigens recognized by ferret and human sera, revealing differences in antigen clustering and predicting antigenically significant sites.

This study evaluates an intranasal influenza vaccine with adjuvants, showing enhanced antibody responses and protection against seasonal influenza viruses in mice.

A single dose of COBRA HA-based inactivated influenza vaccines elicited broadly reactive antibodies and long-term protection against viral challenge in ferrets with pre-existing immunity.

This study shows that delayed innate immune responses to influenza B virus in ferrets lead to weaker antibody responses compared to influenza A virus.

This paper introduces MAGE, a model that generates paired human antibody sequences against specific targets, validated against SARS-CoV-2, H5N1, and RSV-A.

The paper identifies a pan-betacoronavirus antibody that binds the S2 subunit of spike proteins and induces Fc-dependent antiviral responses, offering insights into non-neutralizing antibody protection.

The paper analyzes baseline multi-omic data from influenza vaccine recipients to identify pre-immune states predictive of vaccination responses.

This study evaluates COBRA vaccines formulated with cGAMP microparticles in mice, showing enhanced antibody responses and protection against influenza viruses.

This study shows that pre-existing immunity in ferrets enhances vaccine-induced antibody responses, reducing the need for booster shots and broadening protection against homologous and heterologous influenza strains.

This study uses a network-based approach to analyze sex differences in immune responses to influenza virus infection in ferrets, identifying sex-biased gene expression and pathways.

This study uses high-resolution proteomics to identify 26 differentially expressed proteins that can predict Zika virus infection with 72% accuracy using a machine learning model.