Philip H Bradley

The paper introduces a computational framework to efficiently identify significantly neighbor enriched TCR sequences, demonstrating its utility in analyzing memory T cells and T cell responses to vaccination and viral infections.

IMMREP25 evaluates TCR:pMHC binding prediction for unseen peptides, showing improved performance with structural modeling but still below random guessing.

TCR2HLA infers HLA genotypes from TCR repertoires, enabling identification of immunologically relevant metaclonotypes and discovery of HLA-associated TCRs.

This paper introduces ADAPT, a deep learning framework for designing TCRs and antibodies that bind to specific pMHC targets, validated through cryo-EM structures and functional testing.

The paper develops computational tools to identify cancer-reactive T cells and neoantigen-specific TCRs, showing their variability across cancer types and predictive value for immunotherapy response.

This paper introduces a computational method to predict TCR-pMHC interactions and design immunogenic peptides, achieving high accuracy and demonstrating success in T-cell activation.

The paper analyzes 6 million T cells from 91 studies to identify TCR sequence convergence patterns and their association with functional and cellular T cell phenotypes.

The paper uses statistical analysis of TCR repertoire data to show microhomology influences V(D)J recombination outcomes in humans.

Tumor cell methylation of KRASG12V epitope in HLA-A2 permits immune evasion, and new TCRs were generated to overcome this with engineered cell therapy.

The paper introduces a neural network model, DePTH, to predict TCR-HLA associations from amino acid sequences and shows its association with cancer patient survival outcomes.

The paper fine-tunes AlphaFold to predict peptide binding specificity for MHC and other domains, achieving performance comparable to sequence-based methods.

This paper computationally designs self-assembling protein rotary machines that sample internal rotational degrees of freedom based on energy landscapes.