Project Summary/Abstract Structural biology has a fundamental role to play in the advancement of cancer biology and the development of cancer therapeutics. With the rapid developments in experimental structural determination (both crystallography and cryo-EM spectroscopy), structure prediction methods (primarily AlphaFold2 and RosettaFold), and molecular simulation methods, we are poised to bring new levels structural information to cancer research. In this project, we will analyze the structural variation and dynamics of protein families commonly associated with cancer development or targets of cancer therapeutics using existing clustering methods for protein loops and new unsupervised learning techniques from the field of deep learning. We will develop methods for using AlphaFold2 to predict the structures of active and inactive kinases using templates based on our classification of active and inactive states of kinases and multiple sequence alignments optimized for this task. In relevant cases, these structure predictions will include the N and C terminal tails and other domains which may interact with the kinase domains. We will integrate AlphaFold2 structure predictions of protein homo- and heterooligomeric complexes with our database of common interfaces and assemblies found across the structures of proteins in the PDB. Interactions observed in crystals that are replicated by AlphaFold2 present well-founded hypotheses for functional protein interactions. This will be applied specifically for all human kinases where homodimer interactions play an important role in activation and inhibition. We will continue our structural bioinformatics studies of antibodies and expand this work to T-cell receptors, and investigate the utility of deep learning methods for computational antibody and TCR design. Finally, we will bring new structure prediction technologies and our statistical analysis of protein structures to the ongoing research programs of laboratory and clinical colleagues at Fox Chase Cancer Center and Temple University School of Medicine.