bioRxivCancer BiologyPreprint
Path sampling simulations reveal how the Q61L mutation restricts the dynamics of KRas
S. Roet, F. Hooft, P. G. Bolhuis, D. W. H. Swenson, J. Vreede
Abstract
The GTPase KRas is a signaling protein in networks for cell differentiation, growth, and division. KRas mutations can prolong activation of these networks, resulting in tumor formation. When active, KRas tightly binds GTP. Several oncogenic mutations affect the conversion between this rigid state and inactive, more flexible states. Detailed understanding of these transitions may provide valuable insights into how mutations affect KRas. Path sampling simulations, which focus on transitions, show KRas visiting several states, which are the same for wild type and the oncogenic mutant Q61L. Large differences occur when converting between these states, indicating the dramatic effect of the Q61L mutation on KRas dynamics. For Q61L a route to the flexible state is inaccessible, thus shifting the equilibrium to more rigid states. Our methodology presents a novel way to predict dynamical effects of KRas mutations, which may aid in identifying potential therapeutic targets. Author summaryCancer cells frequently contain mutations in the protein KRas. However, KRas is a challenging target for anti-cancer drugs, in part because the dynamic behavior of flexible regions in the protein is difficult to characterize experimentally, and occurs on timescales that are too long for straightforward molecular dynamics simulations. We have used path sampling, an advanced simulation technique that overcomes long timescales, to obtain atomistic insight into the dynamics of KRas. Comparing the oncogenic mutant Q61L to the wild type revealed that the mutation closes off one transition channel for deactivating KRas. Our approach opens up the way for predicting the dynamical effects of mutations in KRas, which may aid in identifying potential therapeutic targets.
- assay: protein structure
- research focus: protein folding & dynamics
- research focus: drug response / drug discovery
- research focus: benchmarking
- approach: deep learning
- approach: statistical model
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