bioRxivBiophysicsPreprint
The binding landscape of Ras/effector interactions provides insights on Ras switching and oncogenesis
Atilio Tomazini, Sagara Gurusinghe, Sergey Bogomolov, Julia M ShifmaniD
Abstract
Protein-protein interactions (PPIs) regulate essential cellular processes, yet the evolutionary principles that shape their specificity, adaptability, and dysregulation in disease remain poorly understood. Here, we investigate the evolution of a signaling PPI between the small GTPase Ras and its effector Nore1A, a tumor suppressor involved in Ras-mediated signaling. Ras acts as a molecular switch, engaging effectors in its GTP-bound active state and releasing them after GTP hydrolysis, whereas oncogenic mutations such as G12D lock Ras in the active state, promoting aberrant signaling in cancer. Using yeast surface display, deep mutational scanning, and data normalization, we systematically mapped the Ras/Nore1A binding landscape under different physiological and oncogenic states. We generated and screened $\sim$33,000 Nore1A single and double mutants against RasWT and RasG12D in both GTP- and GDP-bound states. Our data revealed that most Nore1A mutations produce small-to-moderate decrease in Ras affinity, while a small number of substitutions enhance affinity. Unexpectedly, GTP hydrolysis causes minimal remodeling of the interaction landscape. Conversely, the oncogenic RasG12D mutation reshapes the binding landscape, revealing many Nore1A variants with enhanced and selective affinity for mutant Ras. Molecular dynamics (MD) simulations of Nore1A-Ras variant complexes suggest that the non-optimality of the Nore1A/RasG12D binding landscape arises, in part, from the greater intrinsic flexibility of RasG12D and the pronounced reduction in flexibility induced by affinity-enhancing Nore1A mutations. These findings demonstrate that disease-associated mutations can create new evolutionary trajectories in signaling PPIs by changing interaction dynamics and provide a framework for engineering selective protein-based inhibitors targeting oncogenic Ras variants.
- organism: human
- assay: proteomics
- research focus: protein folding & dynamics
- approach: deep learning
- approach: statistical model
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