RAS GTPases are important mediators of oncogenesis in humans. However, pharmacological inhibition of RAS has proved challenging. Here, we describe a functionally critical region of RAS located outside the effector lobe that can be targeted for inhibition. We developed a synthetic binding protein (monobody), termed NS1, that bound with high affinity to both GTP- and GDP-bound states of H- and K-RAS but not N-RAS. NS1 potently inhibited growth factor signaling and oncogenic H- and K-RAS-mediated signaling and transformation but did not block oncogenic N-RAS, BRAF or MEK1. NS1 bound the α4-β6-α5 region of RAS disrupting RAS dimerization/nanoclustering, which in turn blocked CRAF:BRAF heterodimerization and activation. These results establish the importance of the α4-β6-α5 interface in RAS-mediated signaling and define a previously unrecognized site in RAS for inhibiting RAS function.

Generative modeling for protein engineering is key to solving fundamental problems in synthetic biology, medicine, and material science. We pose protein engineering as an unsupervised sequence generation problem in order to leverage the exponentially growing set of proteins that lack costly, structural annotations. We train a 1.2B-parameter language model, ProGen, on ∼280M protein sequences conditioned on taxonomic and keyword tags such as molecular function and cellular component. This provides ProGen with an unprecedented range of evolutionary sequence diversity and allows it to generate with fine-grained control as demonstrated by metrics based on primary sequence similarity, secondary structure accuracy, and conformational energy.

The task of protein sequence design is central to nearly all rational protein engineering problems, and enormous effort has gone into the development of energy functions to guide design. Here, we investigate the capability of a deep neural network model to automate design of sequences onto protein backbones, having learned directly from crystal structure data and without any human-specified priors. The model generalizes to native topologies not seen during training, producing experimentally stable designs. We evaluate the generalizability of our method to a de novo TIM-barrel scaffold. The model produces novel sequences, and high-resolution crystal structures of two designs show excellent agreement with in silico models. Our findings demonstrate the tractability of an entirely learned method for protein sequence design.

Defects in the innate immune system in the lung with attendant bacterial infections contribute to lung tissue damage, respiratory insufficiency, and ultimately death in the pathogenesis of cystic fibrosis (CF). Professional phagocytes, including alveolar macrophages (AMs), have specialized pathways that ensure efficient killing of pathogens in phagosomes. Phagosomal acidification facilitates the optimal functioning of degradative enzymes, ultimately contributing to bacterial killing. Generation of low organellar pH is primarily driven by the V-ATPases, proton pumps that use cytoplasmic ATP to load H+ into the organelle. Critical to phagosomal acidification are various channels derived from the plasma membrane, including the anion channel cystic fibrosis transmembrane conductance regulator, which shunt the transmembrane potential generated by movement of protons. Here we show that the transient receptor potential canonical-6 (TRPC6) calcium-permeable channel in the AM also functions to shunt the transmembrane potential generated by proton pumping and is capable of restoring microbicidal function to compromised AMs in CF and enhancement of function in non-CF cells. TRPC6 channel activity is enhanced via translocation to the cell surface (and then ultimately to the phagosome during phagocytosis) in response to G-protein signaling activated by the small molecule (R)-roscovitine and its derivatives. These data show that enhancing vesicular insertion of the TRPC6 channel to the plasma membrane may represent a general mechanism for restoring phagosome activity in conditions, where it is lost or impaired.