Histiocytoses are clonal hematopoietic disorders frequently driven by mutations in BRAF and MEK1/2 kinases. Currently, however, the developmental origins of histiocytoses in patients are not well understood, and clinically meaningful therapeutic targets outside of BRAF and MEK are undefined. Here we uncover activating mutations in CSF-1R, as well as rearrangements in RET and ALK which confer dramatic responses to selective inhibition of RET (selpercatinib) and crizotinib, respectively, in histiocytosis patients.

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Recently it has become possible to de novo design high affinity protein binding proteins from target structural information alone. There is, however, considerable room for improvement as the overall design success rate is low. Here, we explore the augmentation of energy-based protein binder design using deep learning. We find that using AlphaFold2 or RoseTTAFold to assess the probability that a designed sequence adopts the designed monomer structure, and the probability that this structure binds the target as designed, increases design success rates nearly 10-fold. We find further that sequence design using ProteinMPNN rather than Rosetta considerably increases computational efficiency.