How Anionic Lipids Affect Spatiotemporal Properties of KRAS4B on Model Membranes

Pioneer in Molecular Biology: Conformational Ensembles in Molecular Recognition, Allostery, and Cell Function

In 1978, for my PhD, I developed the efficient O(n3) dynamic programming algorithm for the-then open problem of RNA secondary structure prediction. This algorithm, now dubbed the "Nussinov algorithm", "Nussinov plots", and "Nussinov diagrams", is still taught across Europe and the U.S. As sequences started coming out in the 1980s, I started seeking genome-encoded functional signals, later becoming a bioinformatics trend. In the early 1990s I transited to proteins, co-developing a powerful computer vision-based docking algorithm. In the late 1990s, I proposed the foundational role of conformational ensembles in molecular recognition and allostery. At the time, conformational ensembles and free energy landscapes were viewed as physical properties of proteins but were not associated with function. The classical view of molecular recognition and binding was based on only two conformations captured by crystallography: open and closed. I proposed that all conformational states preexist. Proteins always have not one folded form-nor two-but many folded forms. Thus, rather than inducing fit, binding can work by shifting the ensembles between states, and this shifting, or redistributing the ensembles to maintain equilibrium, is the origin of the allosteric effect and protein, thus cell, function. This transformative paradigm impacted community views in allosteric drug design, catalysis, and regulation. Dynamic conformational ensemble shifts are now acknowledged as the origin of recognition, allostery, and signaling, underscoring that conformational ensembles-not proteins-are the workhorses of the cell, pioneering the fundamental idea that dynamic ensembles are the driving force behind cellular processes. Nussinov was recognized as pioneer in molecular biology by JMB.

Molecular dynamics simulations for the structure-based drug design: targeting small-GTPases proteins

INTRODUCTION

Molecular Dynamics (MD) simulations can support mechanism-based drug design. Indeed, MD simulations by capturing biomolecule motions at finite temperatures can reveal hidden binding sites, accurately predict drug-binding poses, and estimate the thermodynamics and kinetics, crucial information for drug discovery campaigns. Small-Guanosine Triphosphate Phosphohydrolases (GTPases) regulate a cascade of signaling events, that affect most cellular processes. Their deregulation is linked to several diseases, making them appealing drug targets. The broad roles of small-GTPases in cellular processes and the recent approval of a covalent KRas inhibitor as an anticancer agent renewed the interest in targeting small-GTPase with small molecules.

AREA COVERED

This review emphasizes the role of MD simulations in elucidating small-GTPase mechanisms, assessing the impact of cancer-related variants, and discovering novel inhibitors.

EXPERT OPINION

The application of MD simulations to small-GTPases exemplifies the role of MD simulations in the structure-based drug design process for challenging biomolecular targets. Furthermore, AI and machine learning-enhanced MD simulations, coupled with the upcoming power of quantum computing, are promising instruments to target elusive small-GTPases mutations and splice variants. This powerful synergy will aid in developing innovative therapeutic strategies associated to small-GTPases deregulation, which could potentially be used for personalized therapies and in a tissue-agnostic manner to treat tumors with mutations in small-GTPases.

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.

Quantitative Paramagnetic NMR-Based Analysis of Protein Orientational Dynamics on Membranes: Dissecting the KRas4B-Membrane Interactions

Peripheral membrane proteins can adopt distinct orientations on the surfaces of lipid bilayers that are often short-lived and challenging to characterize by conventional experimental methods. Here we describe a robust approach for mapping protein orientational landscapes through quantitative interpretation of paramagnetic relaxation enhancement (PRE) data arising from membrane mimetics with spin-labeled lipids. Theoretical analysis, followed by experimental verification, reveals insights into the distinct properties of the PRE observables that are generally distorted in the case of stably membrane-anchored proteins. To suppress the artifacts, we demonstrate that undistorted Γ₂ values can be obtained via transient membrane anchoring, based on which a computational framework is established for deriving accurate orientational ensembles obeying Boltzmann statistics. Application of the approach to KRas4B, a classical peripheral membrane protein whose orientations are critical for its functions and drug design, reveals four distinct orientational states that are close but not identical to those reported previously. Similar orientations are also found for a truncated KRas4B without the hypervariable region (HVR) that can sample a broader range of orientations, suggesting a confinement role of the HVR geometrically prohibiting severe tilting. Comparison of the KRas4B Γ₂ rates measured using nanodiscs containing different types of anionic lipids reveals identical Γ₂ patterns for the G-domain but different ones for the HVR, indicating only the latter is able to selectively interact with anionic lipids.

Lifetime of actin-dependent protein nanoclusters

Protein nanoclusters (PNCs) are dynamic collections of a few proteins that spatially organize in nanometer-length clusters. PNCs are one of the principal forms of spatial organization of membrane proteins, and they have been shown or hypothesized to be important in various cellular processes, including cell signaling. PNCs show remarkable diversity in size, shape, and lifetime. In particular, the lifetime of PNCs can vary over a wide range of timescales. The diversity in size and shape can be explained by the interaction of the clustering proteins with the actin cytoskeleton or the lipid membrane, but very little is known about the processes that determine the lifetime of the nanoclusters. In this paper, using mathematical modeling of the cluster dynamics, we model the biophysical processes that determine the lifetime of actin-dependent PNCs. In particular, we investigated the role of actin aster fragmentation, which had been suggested to be a key determinant of the PNC lifetime, and we found that it is important only for a small class of PNCs. A simple extension of our model allowed us to investigate the kinetics of protein-ligand interaction near PNCs. We found an anomalous increase in the lifetime of ligands near PNCs, which agrees remarkably well with experimental data on RAS-RAF kinetics. In particular, analysis of the RAS-RAF data through our model provides falsifiable predictions and novel hypotheses that will not only shed light on the role of RAS-RAF kinetics in various cancers, but also will be useful in studying membrane protein clustering in general.

Millisecond molecular dynamics simulations of KRas-dimer formation and interfaces

Ras dimers have been proposed as building blocks for initiating the extracellular signal-regulated kinase (ERK)/mitogen-activated protein kinase (MAPK) cellular signaling pathway. To better examine the structure of possible dimer interfaces, the dynamics of Ras dimerization, and its potential signaling consequences, we performed molecular dynamics simulations totaling 1 ms of sampling, using an all-atom model of two full-length, farnesylated, guanosine triphosphate (GTP)-bound, wild-type KRas4b proteins diffusing on 29%POPS (1-palmitoyl-2-oleoyl-sn-glycero-3-phospho-L-serine)-mixed POPC (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine) membranes. Our simulations unveil an ensemble of thermodynamically weak KRas dimers spanning multiple conformations. The most stable conformations, having the largest interface areas, involve helix α2 and a hypervariable region (HVR). Among the dimer conformations, we found that the HVR of each KRas has frequent interactions with various parts of the dimer, thus potentially mediating the dimerization. Some dimer configurations have one KRas G-domain elevated above the lipid bilayer surface by residing on top of the other G-domain, thus likely contributing to the recruitment of cytosolic Raf kinases in the context of a stably formed multi-protein complex. We identified a variant of the α4-α5 KRas-dimer interface that is similar to the interfaces obtained with fluorescence resonance energy transfer (FRET) data of HRas on lipid bilayers. Interestingly, we found two arginine fingers, R68 and R149, that directly interact with the beta-phosphate of the GTP bound in KRas, in a manner similar to what is observed in a crystal structure of GAP-HRas complex, which can facilitate the GTP hydrolysis via the arginine finger of GTPase-activating protein (GAP).

Nanoscopic Spatial Association between Ras and Phosphatidylserine on the Cell Membrane Studied with Multicolor Super Resolution Microscopy

Recent work suggests that Ras small GTPases interact with the anionic lipid phosphatidylserine (PS) in an isoform-specific manner, with direct implications for their biological functions. Studies on PS-Ras associations in cells, however, have relied on immuno-EM imaging of membrane sheets. To study their spatial relationships in intact cells, we have combined the use of Lact-C2-GFP, a biosensor for PS, with multicolor super resolution imaging based on DNA-PAINT. At ~20 nm spatial resolution, the resulting super resolution images clearly show the nonuniform molecular distribution of PS on the cell membrane and its co-enrichment with caveolae, as well as with unidentified membrane structures. Two-color imaging followed by spatial analysis shows that KRas-G12D and HRas-G12V both co-enrich with PS in model U2OS cells, confirming previous observations, yet exhibit clear differences in their association patterns. Whereas HRas-G12V is almost always co-enriched with PS, KRas-G12D is strongly co-enriched with PS in about half of the cells, with the other half exhibiting a more moderate association. In addition, perturbations to the actin cytoskeleton differentially impact PS association with the two Ras isoforms. These results suggest that PS-Ras association is context-dependent and demonstrate the utility of multiplexed super resolution imaging in defining the complex interplay between Ras and the membrane.

Asynchronous Reciprocal Coupling of Martini 2.2 Coarse-Grained and CHARMM36 All-Atom Simulations in an Automated Multiscale Framework

The appeal of multiscale modeling approaches is predicated on the promise of combinatorial synergy. However, this promise can only be realized when distinct scales are combined with reciprocal consistency. Here, we consider multiscale molecular dynamics (MD) simulations that combine the accuracy and macromolecular flexibility accessible to fixed-charge all-atom (AA) representations with the sampling speed accessible to reductive, coarse-grained (CG) representations. AA-to-CG conversions are relatively straightforward because deterministic routines with unique outcomes are achievable. Conversely, CG-to-AA conversions have many solutions due to a surge in the number of degrees of freedom. While automated tools for biomolecular CG-to-AA transformation exist, we find that one popular option, called Backward, is prone to stochastic failure and the AA models that it does generate frequently have compromised protein structure and incorrect stereochemistry. Although these shortcomings can likely be circumvented by human intervention in isolated instances, automated multiscale coupling requires reliable and robust scale conversion. Here, we detail an extension to Multiscale Machine-learned Modeling Infrastructure (MuMMI), including an improved CG-to-AA conversion tool called sinceCG. This tool is reliable (∼98% weakly correlated repeat success rate), automatable (no unrecoverable hangs), and yields AA models that generally preserve protein secondary structure and maintain correct stereochemistry. We describe how the MuMMI framework identifies CG system configurations of interest, converts them to AA representations, and simulates them at the AA scale while on-the-fly analyses provide feedback to update CG parameters. Application to systems containing the peripheral membrane protein RAS and proximal components of RAF kinase on complex eight-component lipid bilayers with ∼1.5 million atoms is discussed in the context of MuMMI.

Membrane Composition and Raf[CRD]-Membrane Attachment Are Driving Forces for K-Ras4B Dimer Stability

Ras proteins are membrane-anchored GTPases that regulate key cellular signaling networks. It has been recently shown that different anionic lipid types can affect the properties of Ras in terms of dimerization/clustering on the cell membrane. To understand the effects of anionic lipids on key spatiotemporal properties of dimeric K-Ras4B, we perform all-atom molecular dynamics simulations of the dimer K-Ras4B in the presence and absence of Raf[RBD/CRD] effectors on two model anionic lipid membranes: one containing 78% mol DOPC, 20% mol DOPS, and 2% mol PIP2 and another one with enhanced concentration of anionic lipids containing 50% mol DOPC, 40% mol DOPS, and 10% mol PIP2. Analysis of our results unveils the orientational space of dimeric K-Ras4B and shows that the stability of the dimer is enhanced on the membrane containing a high concentration of anionic lipids in the absence of Raf effectors. This enhanced stability is also observed in the presence of Raf[RBD/CRD] effectors although it is not influenced by the concentration of anionic lipids in the membrane, but rather on the ability of Raf[CRD] to anchor to the membrane. We generate dominant K-Ras4B conformations by Markov state modeling and yield the population of states according to the K-Ras4B orientation on the membrane. For the membrane containing anionic lipids, we observe correlations between the diffusion of K-Ras4B and PIP2 and anchoring of anionic lipids to the Raf[CRD] domain. We conclude that the presence of effectors with the Raf[CRD] domain anchoring on the membrane as well as the membrane composition both influence the conformational stability of the K-Ras4B dimer, enabling the preservation of crucial interface interactions.

Examining the Effect of Charged Lipids on Mitochondrial Outer Membrane Dynamics Using Atomistic Simulations

The outer mitochondrial membrane (OMM) is involved in multiple cellular functions such as apoptosis, inflammation and signaling via its membrane-associated and -embedded proteins. Despite the central role of the OMM in these vital phenomena, the structure and dynamics of the membrane have regularly been investigated in silico using simple two-component models. Accordingly, the aim was to generate the realistic multi-component model of the OMM and inspect its properties using atomistic molecular dynamics (MD) simulations. All major lipid components, phosphatidylinositol (PI), phosphatidylcholine (PC), phosphatidylethanolamine (PE), and phosphatidylserine (PS), were included in the probed OMM models. Because increased levels of anionic PS lipids have potential effects on schizophrenia and, more specifically, on monoamine oxidase B enzyme activity, the effect of varying the PS concentration was explored. The MD simulations indicate that the complex membrane lipid composition (MLC) behavior is notably different from the two-component PC-PE model. The MLC changes caused relatively minor effects on the membrane structural properties such as membrane thickness or area per lipid; however, notable effects could be seen with the dynamical parameters at the water-membrane interface. Increase of PS levels appears to slow down lateral diffusion of all lipids and, in general, the presence of anionic lipids reduced hydration and slowed down the PE headgroup rotation. In addition, sodium ions could neutralize the membrane surface, when PI was the main anionic component; however, a similar effect was not seen for high PS levels. Based on these results, it is advisable for future studies on the OMM and its protein or ligand partners, especially when wanting to replicate the correct properties on the water-membrane interface, to use models that are sufficiently complex, containing anionic lipid types, PI in particular.

Modeling of full-length Piezo1 suggests importance of the proximal N-terminus for dome structure

Piezo1 forms a mechanically activated calcium-permeable nonselective cation channel that is functionally important in many cell types. Structural data exist for C-terminal regions, but we lack information about N-terminal regions and how the entire channel interacts with the lipid bilayer. Here, we use computational approaches to predict the three-dimensional structure of the full-length Piezo1 and simulate it in an asymmetric membrane. A number of novel insights are suggested by the model: 1) Piezo1 creates a trilobed dome in the membrane that extends beyond the radius of the protein, 2) Piezo1 changes the lipid environment in its vicinity via preferential interactions with cholesterol and phosphatidylinositol 4,5-bisphosphate (PIP₂) molecules, and 3) cholesterol changes the depth of the dome and PIP₂ binding preference. In vitro alteration of cholesterol concentration inhibits Piezo1 activity in a manner complementing some of our computational findings. The data suggest the importance of N-terminal regions of Piezo1 for dome structure and membrane cholesterol and PIP₂ interactions.

Membrane Tethering Potency of Rab-Family Small GTPases Is Defined by the C-Terminal Hypervariable Regions

Membrane tethering is a crucial step to determine the spatiotemporal specificity of secretory and endocytic trafficking pathways in all eukaryotic endomembrane systems. Recent biochemical studies by a chemically-defined reconstitution approach reveal that, in addition to the structurally-diverse classic tethering factors such as coiled-coil tethering proteins and multisubunit tethering complexes, Rab-family small GTPases also retain the inherent membrane tethering functions to directly and physically bridge two distinct lipid bilayers by themselves. Although Rab-mediated membrane tethering reactions are fairly efficient and specific in the physiological context, its mechanistic basis is yet to be understood. Here, to explore whether and how the intrinsic tethering potency of Rab GTPases is controlled by their C-terminal hypervariable region (HVR) domains that link the conserved small GTPase domains (G-domains) to membrane anchors at the C-terminus, we quantitatively compared tethering activities of two representative Rab isoforms in humans (Rab5a, Rab4a) and their HVR-deleted mutant forms. Strikingly, deletion of the HVR linker domains enabled both Rab5a and Rab4a isoforms to enhance their intrinsic tethering potency, exhibiting 5- to 50-fold higher initial velocities of tethering for the HVR-deleted mutants than those for the full-length, wild-type Rabs. Furthermore, we revealed that the tethering activity of full-length Rab5a was significantly reduced by the omission of anionic lipids and cholesterol from membrane lipids and, however, membrane tethering driven by HVR-deleted Rab5a mutant was completely insensitive to the headgroup composition of lipids. Reconstituted membrane tethering assays with the C-terminally-truncated mutants of Rab4a further uncovered that the N-terminal residues in the HVR linker, located adjacent to the G-domain, are critical for regulating the intrinsic tethering activity. In conclusion, our current findings establish that the non-conserved, flexible C-terminal HVR linker domains define membrane tethering potency of Rab-family small GTPases through controlling the close attachment of the globular G-domains to membrane surfaces, which confers the active tethering-competent state of the G-domains on lipid bilayers.