Membrane lipids drive formation of KRAS4b-RAF1 RBDCRD nanoclusters on the membrane.

The oncogene RAS, extensively studied for decades, presents persistent gaps in understanding, hindering the development of effective therapeutic strategies due to a lack of precise details on how RAS initiates MAPK signaling with RAF effector proteins at the plasma membrane. Recent advances in X-ray crystallography, cryo-EM, and super-resolution fluorescence microscopy offer structural and spatial insights, yet the molecular mechanisms involving protein-protein and protein-lipid interactions in RAS-mediated signaling require further characterization. This study utilizes single-molecule experimental techniques, nuclear magnetic resonance spectroscopy, and the computational Machine-Learned Modeling Infrastructure (MuMMI) to examine KRAS4b and RAF1 on a biologically relevant lipid bilayer. MuMMI captures long-timescale events while preserving detailed atomic descriptions, providing testable models for experimental validation. Both in vitro and computational studies reveal that RBDCRD binding alters KRAS lateral diffusion on the lipid bilayer, increasing cluster size and decreasing diffusion. RAS and membrane binding cause hydrophobic residues in the CRD region to penetrate the bilayer, stabilizing complexes through ß-strand elongation. These cooperative interactions among lipids, KRAS4b, and RAF1 are proposed as essential for forming nanoclusters, potentially a critical step in MAP kinase signal activation.

Advances in Computational Approaches for Estimating Passive Permeability in Drug Discovery.

Passive permeation of cellular membranes is a key feature of many therapeutics. The relevance of passive permeability spans all biological systems as they all employ biomembranes for compartmentalization. A variety of computational techniques are currently utilized and under active development to facilitate the characterization of passive permeability. These methods include lipophilicity relations, molecular dynamics simulations, and machine learning, which vary in accuracy, complexity, and computational cost. This review briefly introduces the underlying theories, such as the prominent inhomogeneous solubility diffusion model, and covers a number of recent applications. Various machine-learning applications, which have demonstrated good potential for high-volume, data-driven permeability predictions, are also discussed. Due to the confluence of novel computational methods and next-generation exascale computers, we anticipate an exciting future for computationally driven permeability predictions.

Martini 3 Coarse-Grained Force Field for Cholesterol.

Cholesterol plays a crucial role in biomembranes by regulating various properties, such as fluidity, rigidity, permeability, and organization of lipid bilayers. The latest version of the Martini model, Martini 3, offers significant improvements in interaction balance, molecular packing, and inclusion of new bead types and sizes. However, the release of the new model resulted in the need to reparameterize many core molecules, including cholesterol. Here, we describe the development and validation of a Martini 3 cholesterol model, addressing issues related to its bonded setup, shape, volume, and hydrophobicity. The proposed model mitigates some limitations of its Martini 2 predecessor while maintaining or improving the overall behavior.

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.

The confluence of machine learning and multiscale simulations.

Multiscale modeling has a long history of use in structural biology, as computational biologists strive to overcome the time- and length-scale limits of atomistic molecular dynamics. Contemporary machine learning techniques, such as deep learning, have promoted advances in virtually every field of science and engineering and are revitalizing the traditional notions of multiscale modeling. Deep learning has found success in various approaches for distilling information from fine-scale models, such as building surrogate models and guiding the development of coarse-grained potentials. However, perhaps its most powerful use in multiscale modeling is in defining latent spaces that enable efficient exploration of conformational space. This confluence of machine learning and multiscale simulation with modern high-performance computing promises a new era of discovery and innovation in structural biology.

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.

Exploring CRD mobility during RAS/RAF engagement at the membrane.

During the activation of mitogen-activated protein kinase (MAPK) signaling, the RAS-binding domain (RBD) and cysteine-rich domain (CRD) of RAF bind to active RAS at the plasma membrane. The orientation of RAS at the membrane may be critical for formation of the RAS-RBDCRD complex and subsequent signaling. To explore how RAS membrane orientation relates to the protein dynamics within the RAS-RBDCRD complex, we perform multiscale coarse-grained and all-atom molecular dynamics (MD) simulations of KRAS4b bound to the RBD and CRD domains of RAF-1, both in solution and anchored to a model plasma membrane. Solution MD simulations describe dynamic KRAS4b-CRD conformations, suggesting that the CRD has sufficient flexibility in this environment to substantially change its binding interface with KRAS4b. In contrast, when the ternary complex is anchored to the membrane, the mobility of the CRD relative to KRAS4b is restricted, resulting in fewer distinct KRAS4b-CRD conformations. These simulations implicate membrane orientations of the ternary complex that are consistent with NMR measurements. While a crystal structure-like conformation is observed in both solution and membrane simulations, a particular intermolecular rearrangement of the ternary complex is observed only when it is anchored to the membrane. This configuration emerges when the CRD hydrophobic loops are inserted into the membrane and helices α3-5 of KRAS4b are solvent exposed. This membrane-specific configuration is stabilized by KRAS4b-CRD contacts that are not observed in the crystal structure. These results suggest modulatory interplay between the CRD and plasma membrane that correlate with RAS/RAF complex structure and dynamics, and potentially influence subsequent steps in the activation of MAPK signaling.

Biophysical Characterization of Membrane Proteins Embedded in Nanodiscs Using Fluorescence Correlation Spectroscopy.

Proteins embedded in biological membranes perform essential functions in all organisms, serving as receptors, transporters, channels, cell adhesion molecules, and other supporting cellular roles. These membrane proteins comprise ~30% of all human proteins and are the targets of ~60% of FDA-approved drugs, yet their extensive characterization using established biochemical and biophysical methods has continued to be elusive due to challenges associated with the purification of these insoluble proteins. In response, the development of nanodisc techniques, such as nanolipoprotein particles (NLPs) and styrene maleic acid polymers (SMALPs), allowed membrane proteins to be expressed and isolated in solution as part of lipid bilayer rafts with defined, consistent nanometer sizes and compositions, thus enabling solution-based measurements. Fluorescence correlation spectroscopy (FCS) is a relatively simple yet powerful optical microscopy-based technique that yields quantitative biophysical information, such as diffusion kinetics and concentrations, about individual or interacting species in solution. Here, we first summarize current nanodisc techniques and FCS fundamentals. We then provide a focused review of studies that employed FCS in combination with nanodisc technology to investigate a handful of membrane proteins, including bacteriorhodopsin, bacterial division protein ZipA, bacterial membrane insertases SecYEG and YidC, Yersinia pestis type III secretion protein YopB, yeast cell wall stress sensor Wsc1, epidermal growth factor receptor (EGFR), ABC transporters, and several G protein-coupled receptors (GPCRs).

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.

Development of a CNS-permeable reactivator for nerve agent exposure: an iterative, multi-disciplinary approach.

Nerve agents have experienced a resurgence in recent times with their use against civilian targets during the attacks in Syria (2012), the poisoning of Sergei and Yulia Skripal in the United Kingdom (2018) and Alexei Navalny in Russia (2020), strongly renewing the importance of antidote development against these lethal substances. The current standard treatment against their effects relies on the use of small molecule-based oximes that can efficiently restore acetylcholinesterase (AChE) activity. Despite their efficacy in reactivating AChE, the action of drugs like 2-pralidoxime (2-PAM) is primarily limited to the peripheral nervous system (PNS) and, thus, provides no significant protection to the central nervous system (CNS). This lack of action in the CNS stems from their ionic nature that, on one end makes them very powerful reactivators and on the other renders them ineffective at crossing the Blood Brain Barrier (BBB) to reach the CNS. In this report, we describe the use of an iterative approach composed of parallel chemical and in silico syntheses, computational modeling, and a battery of detailed in vitro and in vivo assays that resulted in the identification of a promising, novel CNS-permeable oxime reactivator. Additional experiments to determine acute and chronic toxicity are ongoing.

Correlated conformational dynamics of the human GluN1-GluN2A type N-methyl-D-aspartate (NMDA) receptor.

N-Methyl-D-aspartate receptors (NMDARs) are glutamate-gated ion channels found in the nerve cell membranes. As a result of overexcitation of NMDARs, neuronal death occurs and may lead to diseases such as epilepsy, stroke, Alzheimer's disease, and Parkinson's disease. In this study, human GluN1- GluN2A type NMDAR structure is modeled based on the X-ray structure of the Xenopus laevis template and missing loops are added by ab-initio loop modeling. The final structure is chosen according to two different model assessment scores. To be able to observe the structural changes upon ligand binding, glycine and glutamate molecules are docked into the corresponding binding sites of the receptor. Subsequently, molecular dynamics simulations of 1.3 µs are performed for both apo and ligand-bound structures. Structural parameters, which have been considered to show functionally important changes in previous NMDAR studies, are monitored as conformational rulers to understand the dynamics of the conformational changes. Moreover, principal component analysis (PCA) is performed for the equilibrated part of the simulations. From these analyses, the differences in between apo and ligand-bound simulations can be summarized as the following: The girdle right at the beginning of the pore loop, which connects M2 and M3 helices of the ion channel, partially opens. Ligands act like an adhesive for the ligand-binding domain (LBD) by keeping the bi-lobed structure together and consequently this is reflected to the overall dynamics of the protein as an increased correlation of the LBD with especially the amino-terminal domain (ATD) of the protein.

ddcMD: A fully GPU-accelerated molecular dynamics program for the Martini force field.

We have implemented the Martini force field within Lawrence Livermore National Laboratory's molecular dynamics program, ddcMD. The program is extended to a heterogeneous programming model so that it can exploit graphics processing unit (GPU) accelerators. In addition to the Martini force field being ported to the GPU, the entire integration step, including thermostat, barostat, and constraint solver, is ported as well, which speeds up the simulations to 278-fold using one GPU vs one central processing unit (CPU) core. A benchmark study is performed with several test cases, comparing ddcMD and GROMACS Martini simulations. The average performance of ddcMD for a protein-lipid simulation system of 136k particles achieves 1.04 µs/day on one NVIDIA V100 GPU and aggregates 6.19 µs/day on one Summit node with six GPUs. The GPU implementation in ddcMD offloads all computations to the GPU and only requires one CPU core per simulation to manage the inputs and outputs, freeing up remaining CPU resources on the compute node for alternative tasks often required in complex simulation campaigns. The ddcMD code has been made open source and is available on GitHub at https://github.com/LLNL/ddcMD.

Capturing Biologically Complex Tissue-Specific Membranes at Different Levels of Compositional Complexity.

Plasma membranes (PMs) contain hundreds of different lipid species that contribute differently to overall bilayer properties. By modulation of these properties, membrane protein function can be affected. Furthermore, inhomogeneous lipid mixing and domains of lipid enrichment/depletion can sort proteins and provide optimal local environments. Recent coarse-grained (CG) Martini molecular dynamics efforts have provided glimpses into lipid organization of different PMs: an "Average" and a "Brain" PM. Their high complexity and large size require long simulations (∼80 µs) for proper sampling. Thus, these simulations are computationally taxing. This level of complexity is beyond the possibilities of all-atom simulations, raising the question-what complexity is needed for "realistic" bilayer properties? We constructed CG Martini PM models of varying complexity (63 down to 8 different lipids). Lipid tail saturations and headgroup combinations were kept as consistent as possible for the "tissues'" (Average/Brain) at three levels of compositional complexity. For each system, we analyzed membrane properties to evaluate which features can be retained at lower complexity and validate eight-component bilayers that can act as reliable mimetics for Average or Brain PMs. Systems of reduced complexity deliver a more robust and malleable tool for computational membrane studies and allow for equivalent all-atom simulations and experiments.

Simulations of Asymmetric Membranes Illustrate Cooperative Leaflet Coupling and Lipid Adaptability.

Biological membranes are composed of lipid bilayers that are often asymmetric with regards to the lipid composition and/or aqueous solvent they separate. Studying lipid asymmetry both experimentally and computationally is challenging. Molecular dynamics simulations of lipid bilayers with asymmetry are difficult due to finite system sizes and time scales accessible to simulations. Due to the very slow flip-flop rate for phospholipids, one must first choose how many lipids are on each side of the bilayer, but the resulting bilayer may be unstable (or metastable) due to differing tensile and compressive forces between leaflets. Here we use molecular dynamics simulations to investigate a number of different asymmetric membrane systems, both with atomistic and coarse-grained models. Asymmetries studied include differences in number of lipids, lipid composition (unsaturated and saturated tails and different headgroups), and chemical gradients between the aqueous phases. Extensive analysis of the bilayers' properties such as area per lipid, density, and lateral pressure profiles are used to characterize bilayer asymmetry. We also address how cholesterol (which flip-flops relatively quickly) influences membrane asymmetries. Our results show how each leaflet is influenced by the other and can mitigate the structural changes to the bilayer overall structure. Cholesterol can respond to changes in bilayer asymmetry to alleviate some of the effect on the bilayer structure, but that will alter its leaflet distribution, which in turn affects its chemical potential. Ionic imbalances are shown to have a modest change in bilayer structure, despite large changes in the electrostatic potential. Bilayer asymmetry can also induce a modest electrostatic potential across the membrane. Our results highlight the importance of membrane asymmetry on bilayer properties, the influence of lipid headgroups, tails and cholesterol on asymmetry, and the ability of lipids to adapt to different environments.

Encapsulin carrier proteins for enhanced expression of antimicrobial peptides.

Antimicrobial peptides (AMPs) are regarded as attractive alternatives to conventional antibiotics, but their production in microbes remains challenging due to their inherent bactericidal nature. To address these limitations, we have developed a novel AMP fusion protein system based on an encapsulin nanocompartment protein and have demonstrated its utility in enhancing expression of HBCM2, an AMP with activity against Gram-negative bacteria. Here, HBCM2 was fused to the N-terminus of several Encapsulin monomer (Enc) variants engineered with multiple TEV protease recognition site insertions to facilitate proteolytic release of the fused HBCM2. Fusion of HBCM2 to the Enc variants, but not other common carrier proteins, enabled robust overexpression in Escherichia coli C43(DE3) cells. Interestingly, variants with a TEV site insertion following residue K71 in Enc exhibited the highest overexpression and HBCM2 release efficiencies compared to other variants but were deficient in cage formation. HBCM2 was purified from the highest expressing variant following TEV protease digestion and was found to be highly active in inhibiting E. coli growth (MIC = 5 µg/ml). Our study demonstrates the potential use of the Enc system to enhance expression of AMPs for biomanufacturing and therapeutic applications.

MemSurfer: A Tool for Robust Computation and Characterization of Curved Membranes.

Advances in simulation methodologies, code efficiency, and computing power have enabled larger, longer, and more-complicated biological membrane simulations. The resulting membranes can be highly complex and have curved geometries that greatly deviate from a simple planar state. Studying these membranes requires appropriate characterization of geometric and topological properties of the membrane surface before any local lipid properties, such as areas and curvatures, can be computed. We present MemSurfer, an efficient and versatile tool to robustly compute membrane surfaces for a wide variety of large-scale molecular simulations. MemSurfer works independent of the type of simulation and directly on 3D point coordinates. As a result, MemSurfer can handle a variety of membranes. Using Delaunay triangulations and surface parameterizations, MemSurfer not only computes common lipid properties of interest but also provides direct access to the membrane surface itself, allowing the user to potentially conceive and compute a variety of nonstandard properties. The software provides a simple-to-use Python API and is released open-source under a GPL3 license.

Multiscale Modeling and Simulation Approaches to Lipid-Protein Interactions.

Lipid membranes play a crucial role in living systems by compartmentalizing biological processes and forming a barrier between these processes and the environment. Naturally, a large apparatus of biomolecules is responsible for construction, maintenance, transport, and degradation of these lipid barriers. Additional classes of biomolecules are tasked with transport of specific substances or transduction of signals from the environment across lipid membranes. In this article, we intend to describe a set of techniques that enable one to build accurate models of lipid systems and their associated proteins, and to simulate their dynamics over a variety of time and length scales. We discuss the methods and challenges that allow us to derive structural, mechanistic, and thermodynamic information from these models. We also show how these models have recently been applied in research to study some of the most complex lipid-protein systems to date, including bacterial and viral envelopes, neuronal membranes, and mammalian signaling systems.

Capturing Phase Behavior of Ternary Lipid Mixtures with a Refined Martini Coarse-Grained Force Field.

Whether lipid rafts are present in the membranes of living cells remains hotly disputed despite their incontrovertible existence in liposomes at 298 K. In attempts to resolve this debate, molecular dynamics (MD) simulations have been extensively used to study lipid phase separation at high resolution. However, computation has been of limited utility in this respect because the experimental distributions of phases in lamellar lipid mixtures are poorly reproduced by simulations. In particular, all-atom (AA) approaches suffer from restrictions on accessible time scales and system sizes whereas the more efficient coarse-grained (CG) force fields remain insufficiently accurate to achieve correspondence with experiment. In this work, we refine the CG Martini parameters for the high- and low-melting temperature ( Tm) lipids 1,2-dipalmitoyl- sn-glycero-3-phosphatidylcholine (DPPC) and 1,2-dioleoyl- sn-glycero-3-phosphatidylcholine (DOPC). Our approach involves the modification of bonded Martini parameters based on fitting to atomistic simulations conducted with the CHARMM36 lipid force field. The resulting CG parameters reproduce experimental structural and thermodynamic properties of homogeneous lipid membranes while concurrently improving simulation fidelity to experimental phase diagrams of DPPC, DOPC, and cholesterol lipid mixtures. Importantly, the refined parameters provide much better phase accuracy for regions near the critical point that mimic the lipid concentrations under physiological conditions.

Pharmacological characterization of the neurotrophic sesquiterpene jiadifenolide reveals a non-convulsant signature and potential for progression in neurodegenerative disease studies.

The 'neurotrophic sesquiterpenes' refer to a group of molecules derived from the Illicium genus of flowering plant. They display neurotrophic effects in cultured neuron preparations and have been suggested to be cognitive enhancers and potential therapeutics for neurodegenerative disorders and dementias. Recent synthetic advances generated sufficient quantities of jiadifenolide for in vivo investigation into its biological effects. Jiadifenolide did not induce convulsions in mice nor did it enhance or diminish convulsions induced by pentylenetetrazole. Other negative allosteric modulators of GABAA receptors, picrotoxin, tetramethylenedisulfotetramine (TETS), and bilobalide all induced convulsions. Either i.p. or i.c.v. dosing generated micromolar plasma and brain levels of jiadifenolide but only small effects on locomotion of mice. However, jiadifenolide decreased d-amphetamine-induced hyperlocomotion in mice, an antipsychotic-like drug effect. Jiadifenolide did not significantly alter body temperature or behavior in the forced-swim test in mice. Molecular simulation data suggested a potential site in the pore/M2 helix region that is at an overlapping, yet lower position than those observed for other 'cage convulsant' compounds such as TETS and picrotoxin. We hypothesize that a position nearer to the entrance of the pore channel may allow for easier displacement of jiadifenolide from its blocking location leading to lower potency and lower side-effect liability. Like jiadifenolide, memantine (Namenda), one of the few drugs used in the symptomatic treatment of dementias, occupies a unique site on the NMDA receptor complex that creates low binding affinity that is associated with its reduced side-effect profile. Given the potential therapeutic applications of jiadifenolide and its relatively inert effects on overt behavior, the possibility of clinical utility for jiadifenolide and related compounds becomes intriguing.