D. K. Gifford

The paper uses machine learning to predict TCR-pMHC binding and identifies sequence features associated with binding affinity.

A T cell vaccine based on conserved epitopes protects HLA transgenic mice from SARS-CoV-2 Beta variant mortality without inducing neutralizing antibodies.

The paper presents a high-throughput yeast display method to assess MHC-II binding of peptides from SARS-CoV-2 and dengue virus, identifying binders missed by computational predictions.

The paper designs factorizable libraries for therapeutic discovery using an optimization method called SAPS to generate diverse and efficient antibody CDR-H3 sequences.

This study tests a T cell vaccine in HLA-A*02:01 mice to assess its immunogenicity against SARS-CoV-2 variants.

This paper extracts and interprets sequence features affecting peptide MHC binding using yeast display data, showing they accurately predict binding and align with structural knowledge.

The paper develops a machine learning method to optimize peptide binding to class II MHCs by modifying anchor residues, validated through a yeast display assay.

The paper identifies gaps in SARS-CoV-2 subunit vaccine coverage for cellular immunity and proposes adding peptides to improve population coverage.

The paper develops a machine learning method to design peptide vaccines for SARS-CoV-2, achieving high predicted coverage and robustness against epitope drift.

This paper introduces a machine learning method to design human IgG antibodies with superior target affinity and specificity, using high-throughput data without requiring target structural information.