John A. Altin

This paper uses AlphaFold 3 to predict T cell receptor antigen specificity from sequence, achieving high accuracy on unseen epitopes.

This paper demonstrates a personalized SNAP-TIL therapy platform that improves TIL efficacy by enriching them with neoantigen-reactive T-cells, achieving high CD3+ cell content and significant tumor growth inhibition in preclinical models.

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

The paper introduces MHCIIFold-GNN, a method combining structural modeling and graph neural networks to predict CD4 T cell epitopes, achieving state-of-the-art performance.

The paper introduces CRESTA, a method to analyze CD4 T cell epitope specificities and phenotypes, revealing diverse responses to Mycobacterium tuberculosis.