Mikhail Shugay

The paper develops a pipeline to identify and track antigen-specific T cell responses to cancer immunotherapy in mice using TCR repertoire analysis.

The paper develops a classifier for detecting SARS-CoV-2 exposure using T-cell repertoire profiling and machine learning, achieving high accuracy across different protocols.

This study analyzes T-cell receptor repertoires to identify how thymic selection shapes TCR diversity and lineage commitment through changes in CDR3 properties and MHC haplotype effects.

The paper uses template-based modeling to generate TCR:antigen:MHC structures and identifies amino acid contact features that influence antigen recognition.

The paper shows HLA alleles preferentially present peptides from proteins with specific molecular functions, linking these preferences to HLA allele differences and disease susceptibility.

The paper analyzes structural and sequencing data to show minimal constraints on TCRβ chain pairing, allowing maximal diversity in the naive T-cell repertoire.