D. K. Gifford

Machine learning model interpretations explain T cell receptor binding

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

A pan-variant mRNA-LNP T cell vaccine protects HLA transgenic mice from mortality after infection with SARS-CoV-2 Beta

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

A high-throughput yeast display approach to profile pathogen proteomes for MHC-II binding

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.

Ultra high diversity factorizable libraries for efficient therapeutic discovery

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

Preliminary Immunogenicity of a Pan-COVID-19 T Cell Vaccine in HLA-A*02:01 Mice

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

Extracting and Interpreting the Effects of Higher Order Sequence Features on Peptide MHC Binding

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.

Machine learning optimization of peptides for presentation by class II MHCs

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.

Predicted Cellular Immunity Population Coverage Gaps for SARS-CoV-2 Subunit Vaccines and their Augmentation by Compact Peptide Sets

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

Robust computational design and evaluation of peptide vaccines for cellular immunity with application to SARS-CoV-2

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

Antibody Complementarity Determining Region Design Using High-Capacity Machine Learning

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.