Plasma membrane (PM) curvature defines cell shape and intracellular organelle morphologies and is a fundamental cell property. Growth/proliferation is more stimulated in flatter cells than the same cells in elongated shapes. PM-anchored K-Ras small GTPase regulates cell growth/proliferation and plays key roles in cancer. The lipid-anchored K-Ras form nanoclusters selectively enriched with specific phospholipids, such as phosphatidylserine (PS), for efficient effector recruitment and activation. K-Ras function may, thus, be sensitive to changing lipid distribution at membranes with different curvatures. Here, we used complementary methods to manipulate membrane curvature of intact/live cells, native PM blebs, and synthetic liposomes. We show that the spatiotemporal organization and signaling of an oncogenic mutant K-RasG12V favor flatter membranes with low curvature. Our findings are consistent with the more stimulated growth/proliferation in flatter cells. Depletion of endogenous PS abolishes K-RasG12V PM curvature sensing. In cells and synthetic bilayers, only mixed-chain PS species, but not other PS species tested, mediate K-RasG12V membrane curvature sensing. Thus, K-Ras nanoclusters act as relay stations to convert mechanical perturbations to mitogenic signaling.

Recent studies have shown that the small GTPase KRAS adopts multiple orientations with respect to the plane of anionic model membranes, whereby either the three C-terminal helices or the three N-terminal b-strands of the catalytic domain face the membrane. This has functional implications because, in the latter, the membrane occludes the effector-interacting surface. However, it remained unclear how membrane reorientation occurs and, critically, whether it occurs in the cell in which KRAS operates as a molecular switch in signaling pathways. Herein, using data from a 20 ms-long atomistic molecular dynamics simulation of the oncogenic G12V-KRAS mutant in a phosphatidylcholine/phosphatidylserine bilayer, we first show that internal conformational fluctuations of flexible regions in KRAS result in three distinct membrane orientations. We then show, using single-molecule fluorescence resonance energy transfer measurements in native lipid nanodiscs derived from baby hamster kidney cells, that G12V-KRAS samples three conformational states that correspond to the predicted orientations. The combined results suggest that relatively small energy barriers separate orientation states and that signaling-competent conformations dominate the overall population.

Self-assembly of plasma membrane-associated Ras GTPases has major implications to the regulation of cell signaling. However, the structural basis of homo-oligomerization and the fractional distribution of oligomeric states remained undetermined. We have addressed these issues by deciphering the distribution of dimers and higher-order oligomers of K-Ras4B, the most frequently mutated Ras isoform in human cancers. We focused on the constitutively active G12V K-Ras and two of its variants, K101E and K101C/E107C, which respectively destabilize and stabilize oligomers. Using raster image correlation spectroscopy and number and brightness analysis combined with fluorescence recovery after photobleaching, fluorescence correlation spectroscopy and electron microscopy in live cells, we show that G12V K-Ras exists as a mixture of monomers, dimers and larger oligomers, while the K101E mutant is predominantly monomeric and K101C/E107C is dominated by oligomers. This observation demonstrates the ability of K-Ras to exist in multiple oligomeric states whose population can be altered by interfacial mutations. Using molecular modeling and simulations we further show that K-Ras uses two partially overlapping interfaces to form compositionally and topologically diverse oligomers. Our results thus provide the first detailed insight into the multiplicity, structure, and membrane organization of K-Ras homomers.

RAS mutations account for >15% of all human tumors, and of these ~85% are due to mutations in a particular RAS gene: KRAS. Recent studies revealed that KRAS harbors four druggable allosteric sites. Here, we have (a) used molecular simulations to generate ensembles of wild type and four major oncogenic KRAS mutants (G12V, G12D, G13D, and Q61H); (b) characterized the druggability of each allosteric pocket in each protein; (c) conducted extensive ensemble‐based virtual screening using pocket‐tailored ligand libraries; (d) prioritized hits through hierarchical postdocking analysis; and (e) validated predicted hits with NMR. Of the 785 diverse potential hits identified by our in silico analysis, we tested 90 for their ability to bind KRAS using NMR and found that nine cause backbone amide chemical shift perturbations of residues near the functionally responsive switch loops, suggesting potential binding. We conducted detailed biophysical analyses on a novel indole‐based compound to demonstrate the potential of our workflow to yield lead compounds. We believe the detailed information documented in this work regarding the druggability profile of each allosteric site and the chemical fingerprints of compounds that target them will serve as vital resources for future structure‐based drug design efforts against KRAS, a high‐value target for cancer therapy.

Achieving mechanistic understanding of transport in complex environments such as inside cells or at polymer interfaces is challenging. We need better ways to image transport in 3-D and better single particle tracking algorithms to determine transport that are not systemically biased toward any classical motion model. Here we present an unbiased single particle tracking algorithm: Knowing Nothing Outside Tracking (KNOT). KNOT uses point clouds provided by iterative deconvolution to educate individual particle localizations and link particle positions between frames to achieve 2-D and 3-D tracking. Information from prior point clouds fuels an independent adaptive motion model for each particle to avoid global models that could introduce biases. KNOT competes with or surpasses other 2-D methods from the 2012 particle tracking challenge while accurately tracking adsorption dynamics of proteins on polymer surfaces and early endosome transport in live cells in 3-D. We apply KNOT to study 3-D endosome transport to reveal new physical insight into locally directed and diffusive transport in live cells. Our analysis demonstrates better accuracy in classifying local motion and its direction compared to previous methods, revealing intricate intracellular transport heterogeneities.