California Institute for Quantitative Biosciences (QB3), University of California, Berkeley, Berkeley, United States; Howard Hughes Medical Institute, University of California, Berkeley, Berkeley, United States; Department of Chemistry, University of California, Berkeley, Berkeley, United States
Laura M Nocka
California Institute for Quantitative Biosciences (QB3), University of California, Berkeley, Berkeley, United States; Howard Hughes Medical Institute, University of California, Berkeley, Berkeley, United States; Department of Chemistry, University of California, Berkeley, Berkeley, United States
California Institute for Quantitative Biosciences (QB3), University of California, Berkeley, Berkeley, United States; Howard Hughes Medical Institute, University of California, Berkeley, Berkeley, United States; Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States; Department of Chemistry, Columbia University, New York, United States
Kent Gorday
California Institute for Quantitative Biosciences (QB3), University of California, Berkeley, Berkeley, United States; Howard Hughes Medical Institute, University of California, Berkeley, Berkeley, United States; Biophysics Graduate Group, University of California, Berkeley, Berkeley, United States
Naomi R Latorraca
California Institute for Quantitative Biosciences (QB3), University of California, Berkeley, Berkeley, United States; Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
California Institute for Quantitative Biosciences (QB3), University of California, Berkeley, Berkeley, United States; Howard Hughes Medical Institute, University of California, Berkeley, Berkeley, United States; Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
Sage Templeton
California Institute for Quantitative Biosciences (QB3), University of California, Berkeley, Berkeley, United States; Howard Hughes Medical Institute, University of California, Berkeley, Berkeley, United States
David Lee
California Institute for Quantitative Biosciences (QB3), University of California, Berkeley, Berkeley, United States; Howard Hughes Medical Institute, University of California, Berkeley, Berkeley, United States
California Institute for Quantitative Biosciences (QB3), University of California, Berkeley, Berkeley, United States; Howard Hughes Medical Institute, University of California, Berkeley, Berkeley, United States; Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
Jeffrey G Pelton
California Institute for Quantitative Biosciences (QB3), University of California, Berkeley, Berkeley, United States
California Institute for Quantitative Biosciences (QB3), University of California, Berkeley, Berkeley, United States; Department of Chemistry, University of California, Berkeley, Berkeley, United States; Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
California Institute for Quantitative Biosciences (QB3), University of California, Berkeley, Berkeley, United States; Department of Chemistry, University of California, Berkeley, Berkeley, United States
California Institute for Quantitative Biosciences (QB3), University of California, Berkeley, Berkeley, United States; Howard Hughes Medical Institute, University of California, Berkeley, Berkeley, United States; Department of Chemistry, University of California, Berkeley, Berkeley, United States; Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
Cancer mutations in Ras occur predominantly at three hotspots: Gly 12, Gly 13, and Gln 61. Previously, we reported that deep mutagenesis of H-Ras using a bacterial assay identified many other activating mutations (Bandaru et al., 2017). We now show that the results of saturation mutagenesis of H-Ras in mammalian Ba/F3 cells correlate well with the results of bacterial experiments in which H-Ras or K-Ras are co-expressed with a GTPase-activating protein (GAP). The prominent cancer hotspots are not dominant in the Ba/F3 data. We used the bacterial system to mutagenize Ras constructs of different stabilities and discovered a feature that distinguishes the cancer hotspots. While mutations at the cancer hotspots activate Ras regardless of construct stability, mutations at lower-frequency sites (e.g. at Val 14 or Asp 119) can be activating or deleterious, depending on the stability of the Ras construct. We characterized the dynamics of three non-hotspot activating Ras mutants by using NMR to monitor hydrogen-deuterium exchange (HDX). These mutations result in global increases in HDX rates, consistent with destabilization of Ras. An explanation for these observations is that mutations that destabilize Ras increase nucleotide dissociation rates, enabling activation by spontaneous nucleotide exchange. A further stability decrease can lead to insufficient levels of folded Ras – and subsequent loss of function. In contrast, the cancer hotspot mutations are mechanism-based activators of Ras that interfere directly with the action of GAPs. Our results demonstrate the importance of GAP surveillance and protein stability in determining the sensitivity of Ras to mutational activation.
