Abstract
RAS proteins are small membrane-anchored GTPases that regulate key cellular signaling networks. It has been recently shown that different anionic lipid types can affect the spatiotemporal properties of RAS through dimerization/clustering and signaling fidelity. To understand the effects of anionic lipids on key spatiotemporal properties of RAS, we dissected 1 ms of data from all-atom molecular dynamics simulations for KRAS4B on two model anionic lipid membranes that have 30% of POPS mixed with neutral POPC and 8% of PIP2 mixed with POPC. We unveiled the orientation space of KRAS4B, whose kinetics were slower and more distinguishable on the membrane containing PIP2 than the membrane containing POPS. Particularly, the PIP2-mixed membrane can differentiate a third kinetic orientation state from the other two known orientation states. We observed that each orientation state may yield different binding modes with an RAF kinase, which is required for activating the MAPK/ERK signaling pathway. However, an overall occluded probability, for which RAF kinases cannot bind KRAS4B, remains unchanged on the two different membranes. We identified rare fast diffusion modes of KRAS4B that appear coupled with orientations exposed to cytosolic RAF. Particularly, on the membrane having PIP2, we found nonlinear correlations between the orientation states and the conformations of the cationic farnesylated hypervariable region, which acts as an anchor in the membrane. Using diffusion coefficients estimated from the all-atom simulations, we quantified the effect of PIP2 and POPS on the KRAS4B dimerization via Green's function reaction dynamics simulations, in which the averaged dimerization rate is 12.5% slower on PIP2-mixed membranes.
Original language | English |
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Pages (from-to) | 5434-5453 |
Number of pages | 20 |
Journal | Journal of Physical Chemistry B |
Volume | 124 |
Issue number | 26 |
DOIs | |
State | Published - Jul 2 2020 |
Externally published | Yes |
Funding
V.A.N. is a Director’s postdoctoral fellow at LANL and is fully supported by this fellowship (20170692PRD4). C.N. was also partially funded by a Director’s postdoctoral fellowship (20160676PRD4). This work has been supported in part by the JDACS4C program established by the U.S. Department of Energy (DOE) and the National Cancer Institute of the National Institutes of Health. This work was performed under the auspices of the U.S. DOE by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344, Los Alamos National Laboratory (LANL) under Contract DE-AC5206NA25396, Oak Ridge National Laboratory under Contract DE-AC05-00OR22725, and Frederick National Laboratory for Cancer Research under Contract HHSN261200800001E. S.S., C.N., and A.E.G. have been partially supported by LDRD funds (Projects XWJX and XX01). S.S., C.N., and A.E.G. acknowledge discussions with the DOE/NCI Pilot 2 and Uncertainty Quantification teams. Computational resources were provided by the LANL Institutional Computing Program, which is supported by the U.S. DOE National Nuclear Security Administration under Contract DE-AC52-06NA25396.
Funders | Funder number |
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National Institutes of Health | |
U.S. Department of Energy | |
National Cancer Institute | |
National Nuclear Security Administration | DE-AC52-06NA25396 |
Lawrence Livermore National Laboratory | DE-AC52-07NA27344 |
Oak Ridge National Laboratory | DE-AC05-00OR22725 |
Laboratory Directed Research and Development | XX01 |
Los Alamos National Laboratory | DE-AC5206NA25396 |
Frederick National Laboratory for Cancer Research | HHSN261200800001E |