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Templeton, S.

Publications and source records attributed to Templeton, S..

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A Saturation-Mutagenesis Analysis of the Interplay Between Stability and Activation in Ras

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. eLife, 2017). We now show that the results of saturation mutagenesis of H-Ras in mammalian Ba/F3 cells correlate well with 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 the 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.

biophysics↗

Mutagenesis-visualization: analysis of site saturation mutagenesis datasets in Python

SummarySite-saturation mutagenesis experiments have been transformative in our study of protein function. Despite the rich data generated from such experiments, current tools for processing, analyzing, and visualizing the data offer only a limited set of static visualization tools that are difficult to customize. Furthermore, usage of the tools requires extensive experience and programming knowledge, slowing the research process for those in the biological field who are unfamiliar with programming. Here, we introduce mutagenesis-visualization, a Python package for creating publication-quality figures for site-saturation mutagenesis datasets without the need for prior Python or statistics experience, where each of the graphs is generated with a one-line command. The plots can be rendered as native Matplotlib objects (easy to stylize) or Plotly objects (interactive graphs). Additionally, the software offers the possibility to visualize the datasets on Pymol. Availability and implementationThe software can be installed from PyPI or GitHub using the pip package manager and is compatible with Python [≥] 3.8. The documentation can be found at readthedocs and the source code on GitHub.

bioinformatics↗