Direct measurement of membrane dipole field in complex model membranes via vibrational stark effect spectroscopy coupled with molecular dynamics simulations

The heterogeneous composition of a biological membrane creates a complex electrostatic environment that regulates membrane structure and function. In this work, we investigated the magnitude of the membrane dipole field, Fd, located entirely within the low dielectric membrane interior as a function of membrane composition complexity. We directly measured Fd in vesicle model membrane composed of 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) using vibrational Stark effect (VSE) shifts of nitrile oscillators systematically placed along the membrane interior coupled with extensive Molecular Dynamics (MD) simulations. We calculated the absolute magnitude of Fd in DMPC vesicles to be 8-11 MV/cm, at the high end of the range provided in literature. We increased the complexity of the membrane composition by intercalating cholesterol molecule at a wide range of concentration (0- 40 mol%) and found that cholesterol increased Fd at low concentration (~10 mol%), and decreased Fd at higher concentration (>10 mol%). This result, when compared to lipid bilayers containing a cholesterol derivative, 6-ketocholestanol (6-kc) that differs from cholesterol by only a ketone functional group, was strikingly different. Using the spectral line widths obtained from Fourier-transform infrared spectroscopy experiments and molecular dynamic simulations on model lipid-sterol bilayers, we propose that the membrane dipole field is greatly correlated to the local membrane structure and organization regulated by the sterols in the bilayer. We propose that at low concentrations, cholesterol increases dipole field by increasing packing density of disordered lipids, cholesterol and their associated hydrogen bonded water dipoles whereas at high concentrations, the sterol decreases the field by forming liquid ordered state enriched in cholesterol, thus spacing out phospholipids along with water dipoles. 6-kc, on the other hand, is homogeneously distributed and increases hydrogen bonding with water dipoles via two polar groups on its sterol ring, thus never promoting ordered domain and increasing the dipole field monotonously. We also investigated the translocation mechanism of positively, negatively and zwitterionic charged tryptophan molecules through a phospholipid bilayer using time dependent fluorescence spectroscopy and atomically detailed simulations. Both experiment and simulation reproduced the qualitative trend and suggested that the fastest permeation occurred for positively charged tryptophan. Molecular dynamics simulations revealed that the translocation mechanism was assisted by a local defect and the permeation process was insignificantly influenced by the long-range electrostatic interactions, such as the membrane dipole potential.Chemistr

Machine Learning-Driven Multiscale Modeling: Bridging the Scales with a Next-Generation Simulation Infrastructure

Interdependence across time and length scales is common in biology, where atomic interactions can impact larger-scale phenomenon. Such dependence is especially true for a well-known cancer signaling pathway, where the membrane-bound RAS protein binds an effector protein called RAF. To capture the driving forces that bring RAS and RAF (represented as two domains, RBD and CRD) together on the plasma membrane, simulations with the ability to calculate atomic detail while having long time and large length- scales are needed. The Multiscale Machine-Learned Modeling Infrastructure (MuMMI) is able to resolve RAS/RAF protein-membrane interactions that identify specific lipid-protein fingerprints that enhance protein orientations viable for effector binding. MuMMI is a fully automated, ensemble-based multiscale approach connecting three resolution scales: (1) the coarsest scale is a continuum model able to simulate milliseconds of time for a 1 μm2 membrane, (2) the middle scale is a coarse-grained (CG) Martini bead model to explore protein-lipid interactions, and (3) the finest scale is an all-atom (AA) model capturing specific interactions between lipids and proteins. MuMMI dynamically couples adjacent scales in a pairwise manner using machine learning (ML). The dynamic coupling allows for better sampling of the refined scale from the adjacent coarse scale (forward) and on-the-fly feedback to improve the fidelity of the coarser scale from the adjacent refined scale (backward). MuMMI operates efficiently at any scale, from a few compute nodes to the largest supercomputers in the world, and is generalizable to simulate different systems. As computing resources continue to increase and multiscale methods continue to advance, fully automated multiscale simulations (like MuMMI) will be commonly used to address complex science questions

Machine learning–driven multiscale modeling reveals lipid-dependent dynamics of RAS signaling proteins

RAS is a signaling protein associated with the cell membrane that is mutated in up to 30% of human cancers. RAS signaling has been proposed to be regulated by dynamic heterogeneity of the cell membrane. Investigating such a mechanism requires near-atomistic detail at macroscopic temporal and spatial scales, which is not possible with conventional computational or experimental techniques. We demonstrate here a multiscale simulation infrastructure that uses machine learning to create a scale-bridging ensemble of over 100,000 simulations of active wild-type KRAS on a complex, asymmetric membrane. Initialized and validated with experimental data (including a new structure of active wild-type KRAS), these simulations represent a substantial advance in the ability to characterize RAS-membrane biology. We report distinctive patterns of local lipid composition that correlate with interfacially promiscuous RAS multimerization. These lipid fingerprints are coupled to RAS dynamics, predicted to influence effector binding, and therefore may be a mechanism for regulating cell signaling cascades

RAS Nanoclusters: Dynamic Signaling Platforms Amenable to Therapeutic Intervention

RAS proteins are mutated in approximately 20% of all cancers and are generally associated with poor clinical outcomes. RAS proteins are localized to the plasma membrane and function as molecular switches, turned on by partners that receive extracellular mitogenic signals. In the on-state, they activate intracellular signal transduction cascades. Membrane-bound RAS molecules segregate into multimers, known as nanoclusters. These nanoclusters, held together through weak protein–protein and protein–lipid associations, are highly dynamic and respond to cellular input signals and fluctuations in the local lipid environment. Disruption of RAS nanoclusters results in downregulation of RAS-mediated mitogenic signaling. In this review, we discuss the propensity of RAS proteins to display clustering behavior and the interfaces that are associated with these assemblies. Strategies to therapeutically disrupt nanocluster formation or the stabilization of signaling incompetent RAS complexes are discussed

Direct Measurement of the Effect of Cholesterol and 6‑Ketocholestanol on the Membrane Dipole Electric Field Using Vibrational Stark Effect Spectroscopy Coupled with Molecular Dynamics Simulations

Biological membranes are heterogeneous structures with complex electrostatic profiles arising from lipids, sterols, membrane proteins, and water molecules. We investigated the effect of cholesterol and its derivative 6-ketocholestanol (6-kc) on membrane electrostatics by directly measuring the dipole electric field (<i>F</i>⃗_d) within lipid bilayers containing cholesterol or 6-kc at concentrations of 0–40 mol% through the vibrational Stark effect (VSE). We found that adding low concentrations of cholesterol, up to ∼10 mol %, increases <i>F</i>⃗_d, while adding more cholesterol up to 40 mol% lowers <i>F</i>⃗_d. In contrast, we measured a monotonic increase in <i>F</i>⃗_d as 6-kc concentration increased. We propose that this membrane electric field is affected by multiple factors: the polarity of the sterol molecules, the reorientation of the phospholipid dipole due to sterol, and the impact of the sterol on hydrogen bonding with surface water. We used molecular dynamics simulations to examine the distribution of phospholipids, sterol, and helix in bilayers containing these sterols. At low concentrations, we observed clustering of sterols near the vibrational probe whereas at high concentrations, we observed spatial correlation between the positions of the sterol molecules. This work demonstrates how a one-atom difference in a sterol changes the physicochemical and electric field properties of the bilayer

Direct Modulators of K-Ras-Membrane Interactions.

Protein-membrane interactions (PMIs) are ubiquitous in cellular signaling. Initial steps of signal transduction cascades often rely on transient and dynamic interactions with the inner plasma membrane leaflet to populate and regulate signaling hotspots. Methods to target and modulate these interactions could yield attractive tool compounds and drug candidates. Here, we demonstrate that the conjugation of a medium-chain lipid tail to the covalent K-Ras(G12C) binder MRTX849 at a solvent-exposed site enables such direct modulation of PMIs. The conjugated lipid tail interacts with the tethered membrane and changes the relative membrane orientation and conformation of K-Ras(G12C), as shown by molecular dynamics (MD) simulation-supported NMR studies. In cells, this PMI modulation restricts the lateral mobility of K-Ras(G12C) and disrupts nanoclusters. The described strategy could be broadly applicable to selectively modulate transient PMIs

Machine learning-driven multiscale modeling reveals lipid-dependent dynamics of RAS signaling proteins.

RAS is a signaling protein associated with the cell membrane that is mutated in up to 30% of human cancers. RAS signaling has been proposed to be regulated by dynamic heterogeneity of the cell membrane. Investigating such a mechanism requires near-atomistic detail at macroscopic temporal and spatial scales, which is not possible with conventional computational or experimental techniques. We demonstrate here a multiscale simulation infrastructure that uses machine learning to create a scale-bridging ensemble of over 100,000 simulations of active wild-type KRAS on a complex, asymmetric membrane. Initialized and validated with experimental data (including a new structure of active wild-type KRAS), these simulations represent a substantial advance in the ability to characterize RAS-membrane biology. We report distinctive patterns of local lipid composition that correlate with interfacially promiscuous RAS multimerization. These lipid fingerprints are coupled to RAS dynamics, predicted to influence effector binding, and therefore may be a mechanism for regulating cell signaling cascades