Reed Harrison

Only within the last couple of years, we have witnessed how artificial intelligence (AI) is reshaping the way biological research is done. While experimental validation remains essential, AI now allows us to predict protein structures directly from amino acid sequences  — a breakthrough made possible by the wealth of high-quality data in the Protein Data Bank (PDB).

The ability to accurately predict protein structure is critical for understanding how proteins work, how proteins interact with other molecules, and how they can be targeted by drugs. Until recently, accurately predicting protein structure was a challenging task, especially when there is little or no homology to known structures.

Chimeric Antigen Receptor T (CAR T) cell therapy is a revolutionary treatment option for cancer therapy. (1,2) CAR consists of an extracellular antigen recognition domain (usually a Single-Chain Fragment Variable (scFv)), a hinge, a transmembrane domain, and intracellular costimulatory and signaling domains. (3,4) Following the recognition of a specified antigen on the cancer cell surface, CAR T cells induce cytotoxicity by triggering endogenous T cell activation pathways.