Molecular simulations of glycolipids: Towards mammalian cell membrane models

Tailoring tumor-recognizable hyaluronic acid-lipid conjugates to enhance anticancer efficacies of surface-engineered natural killer cells

Natural killer (NK) cells have clinical advantages in adoptive cell therapy owing to their inherent anticancer efficacy and their ability to identify and eliminate malignant tumors. However, insufficient cancer-targeting ligands on NK cell surfaces often inhibit their immunotherapeutic performance, especially in immunosuppressive tumor microenvironment. To facilitate tumor recognition and subsequent anticancer function of NK cells, we developed hyaluronic acid (HA, ligands to target CD44 overexpressed onto cancer cells)-poly(ethylene glycol) (PEG, cytoplasmic penetration blocker)-Lipid (molecular anchor for NK cell membrane decoration through hydrophobic interaction) conjugates for biomaterial-mediated ex vivo NK cell surface engineering. Among these major compartments (i.e., Lipid, PEG and HA), optimization of lipid anchors (in terms of chemical structure and intrinsic amphiphilicity) is the most important design parameter to modulate hydrophobic interaction with dynamic NK cell membranes. Here, three different lipid types including 1,2-dimyristoyl-sn-glycero-3-phosphati-dylethanolamine (C14:0), 1,2-distearoyl-sn-glycero-3-phosphatidylethanolamine (DSPE, C18:0), and cholesterol were evaluated to maximize membrane coating efficacy and associated anticancer performance of surface-engineered NK cells (HALipid-NK cells). Our results demonstrated that NK cells coated with HA-PEG-DSPE conjugates exhibited significantly enhanced anticancer efficacies toward MDA-MB-231 breast cancer cells without an off-target effect on human fibroblasts specifically via increased NK cell membrane coating efficacy and prolonged surface duration of HA onto NK cell surfaces, thereby improving HA-CD44 recognition. These results suggest that our HALipid-NK cells with tumor-recognizable HA-PEG-DSPE conjugates could be further utilized in various cancer immunotherapies.

Cellular Lipids Regulate the Conformational Ensembles of the Disordered Intracellular Loop 3 in β2 Adrenergic Receptor

The structurally disordered intracellular loops (ICLs) of G protein-coupled receptors (GPCRs) play a critical role in G protein coupling. In our previous work, we used a combination of FRET-based and computational methodologies to show that the third intracellular loop (ICL3) modulates the activity and G protein coupling selectivity in GPCRs. In the current study, we have uncovered the role of several lipid components in modulating the conformational ensemble of ICL3 of the β2-adrenergic receptor (β2AR). Our findings indicate that phosphatidylinositol 4,5-bisphosphate (PIP2) in the inner leaflet of the membrane bilayer acts as a stabilizing anchor for ICL3, opening the intracellular cavity to facilitate G protein coupling. This interaction between PIP2 and ICL3 causes tilting of β2AR within the cellular membrane. Notably, this tilting of the receptor is supported by ganglioside GM3 stabilizing the extracellular loops on the outer leaflet of the bilayer, thereby exerting an allosteric effect on the orthosteric ligand binding pocket. Our results underscore the significance of lipids in modulating GPCR activity, proposing an allosteric mechanism that occurs through the receptor's orientation within the membrane.

GBA inhibition suppresses ovarian cancer growth, survival and receptor tyrosine kinase AXL-mediated signaling pathways

The poor outcome of advanced ovarian cancer under conventional therapy necessitates new strategies to improve therapeutic efficacy. β-glucosidase (encoded by GBA) is a lysosomal enzyme and is involved in sphingolipids metabolism. Recent studies revealed that β-glucosidase plays a role in cancer development and chemoresistance. In this work, we systematically evaluated the expression and role of GBA in ovarian cancer. Our work demonstrates that inhibition of β-glucosidase has therapeutic potential for ovarian cancer. Gene Expression Profiling Interactive Analysis database, western blot and immunohistochemistry analyses of patient samples demonstrated that GBA mRNA and protein expression levels were significantly increased in ovarian cancer compared to normal tissues. Functional studies using gain-of- function and loss-of-function approaches demonstrated that GBA overexpression did not affect growth and migration but alleviated cisplatin's efficacy in ovarian cancer cells. In addition, GBA depletion resulted in growth inhibition, apoptosis induction, and enhancement of cisplatin's efficacy. Of note, we found that GBA inhibition specifically decreased receptor tyrosine kinase AXL level, leading to the suppression of AXL-mediated signaling pathways. Our data suggest that GBA represents a promising target to inhibit AXL signaling and overcome cisplatin resistance in ovarian cancer.

Multifaceted Computational Modeling in Glycoscience

Glycoscience assembles all the scientific disciplines involved in studying various molecules and macromolecules containing carbohydrates and complex glycans. Such an ensemble involves one of the most extensive sets of molecules in quantity and occurrence since they occur in all microorganisms and higher organisms. Once the compositions and sequences of these molecules are established, the determination of their three-dimensional structural and dynamical features is a step toward understanding the molecular basis underlying their properties and functions. The range of the relevant computational methods capable of addressing such issues is anchored by the specificity of stereoelectronic effects from quantum chemistry to mesoscale modeling throughout molecular dynamics and mechanics and coarse-grained and docking calculations. The Review leads the reader through the detailed presentations of the applications of computational modeling. The illustrations cover carbohydrate-carbohydrate interactions, glycolipids, and N- and O-linked glycans, emphasizing their role in SARS-CoV-2. The presentation continues with the structure of polysaccharides in solution and solid-state and lipopolysaccharides in membranes. The full range of protein-carbohydrate interactions is presented, as exemplified by carbohydrate-active enzymes, transporters, lectins, antibodies, and glycosaminoglycan binding proteins. A final section features a list of 150 tools and databases to help address the many issues of structural glycobioinformatics.

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.

Lipid-specific interactions determine the organization and dynamics of membrane-active peptide melittin

The cell membranes of different cells deviate significantly in lipid compositions and thus provide varying biological environments to modulate the diffusion, organization and the resultant function of biomacromolecules. However, the detailed modulation mechanism remains elusive especially in consideration of the current overuse of the simplified membrane models such as the pure phosphatidylcholine (PC) membrane. In this work, with the typical membrane-active peptide melittin, we demonstrated that a more complicated membrane environment, such as the bacterial (IME) or plasma membrane (PM), would significantly change the organization and dynamics of melittin, by using molecular dynamics simulations as a "computational microscope". It was found that in these membrane systems, adding melittin would cause a varying degree of reduction in the lateral diffusion of lipids due to the different assembly states of peptides. Melittin tended to aggregate to oligomers in the pure PC membrane, mostly as a tetramer or trimer, while in IME or PM, its degree of oligomerization was significantly reduced. More surprisingly, melittin displayed a strong affinity with ganglioside GM3 in PM, leading to the formation of melittin-GM3 nanoclusters, which hindered its diffusion and further oligomerization. Additionally, small changes in the residue sequence of melittin could modulate the degree or structure of the peptide oligomer. Our work provides a typical example of a study on the organization and dynamics of pore-forming peptides in specific membrane environments and has great significance on the optimization of peptide sequences and the design of helix bundles in the membrane for target biological function.

Computational Nanoscopy of Tight Junctions at the Blood-Brain Barrier Interface

The selectivity of the blood-brain barrier (BBB) is primarily maintained by tight junctions (TJs), which act as gatekeepers of the paracellular space by blocking blood-borne toxins, drugs, and pathogens from entering the brain. The BBB presents a significant challenge in designing neurotherapeutics, so a comprehensive understanding of the TJ architecture can aid in the design of novel therapeutics. Unraveling the intricacies of TJs with conventional experimental techniques alone is challenging, but recently developed computational tools can provide a valuable molecular-level understanding of TJ architecture. We employed the computational methods toolkit to investigate claudin-5, a highly expressed TJ protein at the BBB interface. Our approach started with the prediction of claudin-5 structure, evaluation of stable dimer conformations and nanoscale assemblies, followed by the impact of lipid environments, and posttranslational modifications on these claudin-5 assemblies. These led to the study of TJ pores and barriers and finally understanding of ion and small molecule transport through the TJs. Some of these in silico, molecular-level findings, will need to be corroborated by future experiments. The resulting understanding can be advantageous towards the eventual goal of drug delivery across the BBB. This review provides key insights gleaned from a series of state-of-the-art nanoscale simulations (or computational nanoscopy studies) performed on the TJ architecture.

Computational Modeling of Realistic Cell Membranes

Cell membranes contain a large variety of lipid types and are crowded with proteins, endowing them with the plasticity needed to fulfill their key roles in cell functioning. The compositional complexity of cellular membranes gives rise to a heterogeneous lateral organization, which is still poorly understood. Computational models, in particular molecular dynamics simulations and related techniques, have provided important insight into the organizational principles of cell membranes over the past decades. Now, we are witnessing a transition from simulations of simpler membrane models to multicomponent systems, culminating in realistic models of an increasing variety of cell types and organelles. Here, we review the state of the art in the field of realistic membrane simulations and discuss the current limitations and challenges ahead.

CHARMM-GUI Membrane Builder for Complex Biological Membrane Simulations with Glycolipids and Lipoglycans

Glycolipids (such as glycoglycerolipids, glycosphingolipids, and glycosylphosphatidylinositol) and lipoglycans (such as lipopolysaccharides (LPS), lipooligosaccharides (LOS), mycobacterial lipoarabinomannan, and mycoplasma lipoglycans) are typically found on the surface of cell membranes and play crucial roles in various cellular functions. Characterizing their structure and dynamics at the molecular level is essential to understand their biological roles, but systematic generation of glycolipid and lipoglycan structures is challenging because of great variations in lipid structures and glycan sequences (i.e., carbohydrate types and their linkages). To facilitate the generation of all-atom glycolipid/LPS/LOS structures, we have developed Glycolipid Modeler and LPS Modeler in CHARMM-GUI ( http://www.charmm-gui.org ), a web-based interface that simplifies building of complex biological simulation systems. In addition, we have incorporated these modules into Membrane Builder so that users can readily build a complex symmetric or asymmetric biological membrane system with various glycolipids and LPS/LOS. These tools are expected to be useful in innovative and novel glycolipid/LPS/LOS modeling and simulation research by easing tedious and intricate steps in modeling complex biological systems and shall provide insight into structures, dynamics, and underlying mechanisms of complex glycolipid-/LPS-/LOS-containing biological membrane systems.

Molecular design of antimicrobial peptides based on hemagglutinin fusion domain to combat antibiotic resistance in bacterial infection

Antimicrobial peptides are derived from the viral fusion domain of influenza virus hemagglutinin based on rational analysis of the intermolecular interaction between peptides and bacterial outer membrane. It is revealed that the isolated viral fusion domain is a negatively charged peptide HAfp^1-23 that cannot effectively interact with the anionic membrane. Conversion of the native HAfp^1-23 to a positively charged peptide HAfp^1-23 _KK by E11K/D19K mutation can promote the peptide-membrane interaction substantially; this confers to the peptide a moderate antibacterial potency against antibiotic-resistant bacterial strains. Cyclization of the linear peptide HAfp^1-23 _KK results in a cyclic peptide cHAfp^1-23 _KK, which can largely minimize entropy penalty upon the peptide-membrane binding by pre-stabilizing peptide hairpin configuration in solvent, where the linear peptide would incur in a considerable conformational change/folding from intrinsic disorder (in water) to the structured hairpin conformation (in lipid). As might be expected, the cyclization considerably improves peptide antibacterial activity with minimum inhibitory concentration of 67 and 34 μg/mL against multidrug-resistant Pseudomonas aeruginosa and methicillin-resistant Staphylococcus aureus, respectively.

Lipid-Protein Interactions Are Unique Fingerprints for Membrane Proteins

Cell membranes contain hundreds of different proteins and lipids in an asymmetric arrangement. Our current understanding of the detailed organization of cell membranes remains rather elusive, because of the challenge to study fluctuating nanoscale assemblies of lipids and proteins with the required spatiotemporal resolution. Here, we use molecular dynamics simulations to characterize the lipid environment of 10 different membrane proteins. To provide a realistic lipid environment, the proteins are embedded in a model plasma membrane, where more than 60 lipid species are represented, asymmetrically distributed between the leaflets. The simulations detail how each protein modulates its local lipid environment in a unique way, through enrichment or depletion of specific lipid components, resulting in thickness and curvature gradients. Our results provide a molecular glimpse of the complexity of lipid-protein interactions, with potentially far-reaching implications for our understanding of the overall organization of real cell membranes.

Interplay of G Protein-Coupled Receptors with the Membrane: Insights from Supra-Atomic Coarse Grain Molecular Dynamics Simulations

G protein-coupled receptors (GPCRs) are central to many fundamental cellular signaling pathways. They transduce signals from the outside to the inside of cells in physiological processes ranging from vision to immune response. It is extremely challenging to look at them individually using conventional experimental techniques. Recently, a pseudo atomistic molecular model has emerged as a valuable tool to access information on GPCRs, more specifically on their interactions with their environment in their native cell membrane and the consequences on their supramolecular organization. This approach uses the Martini coarse grain (CG) model to describe the receptors, lipids, and solvent in molecular dynamics (MD) simulations and in enough detail to allow conserving the chemical specificity of the different molecules. The elimination of unnecessary degrees of freedom has opened up large-scale simulations of the lipid-mediated supramolecular organization of GPCRs. Here, after introducing the Martini CGMD method, we review these studies carried out on various members of the GPCR family, including rhodopsin (visual receptor), opioid receptors, adrenergic receptors, adenosine receptors, dopamine receptor, and sphingosine 1-phosphate receptor. These studies have brought to light an interesting set of novel biophysical principles. The insights range from revealing localized and heterogeneous deformations of the membrane bilayer at the surface of the protein, specific interactions of lipid molecules with individual GPCRs, to the effect of the membrane matrix on global GPCR self-assembly. The review ends with an overview of the lessons learned from the use of the CGMD method, the biophysical-chemical findings on lipid-protein interplay.

Ganglioside-Lipid and Ganglioside-Protein Interactions Revealed by Coarse-Grained and Atomistic Molecular Dynamics Simulations

Gangliosides are glycolipids in which an oligosaccharide headgroup containing one or more sialic acids is connected to a ceramide. Gangliosides reside in the outer leaflet of the plasma membrane and play a crucial role in various physiological processes such as cell signal transduction and neuronal differentiation by modulating structures and functions of membrane proteins. Because the detailed behavior of gangliosides and protein-ganglioside interactions are poorly known, we investigated the interactions between the gangliosides GM1 and GM3 and the proteins aquaporin (AQP1) and WALP23 using equilibrium molecular dynamics simulations and potential of mean force calculations at both coarse-grained (CG) and atomistic levels. In atomistic simulations, on the basis of the GROMOS force field, ganglioside aggregation appears to be a result of the balance between hydrogen bond interactions and steric hindrance of the headgroups. GM3 clusters are slightly larger and more ordered than GM1 clusters due to the smaller headgroup of GM3. The different structures of GM1 and GM3 clusters from atomistic simulations are not observed at the CG level based on the Martini model, implying a difference in driving forces for ganglioside interactions in atomistic and CG simulations. For protein-ganglioside interactions, in the atomistic simulations, GM1 lipids bind to specific sites on the AQP1 surface, whereas they are depleted from WALP23. In the CG simulations, the ganglioside binding sites on the AQP1 surface are similar, but ganglioside aggregation and protein-ganglioside interactions are more prevalent than in the atomistic simulations. Using the polarizable Martini water model, results were closer to the atomistic simulations. Although experimental data for validation is lacking, we proposed modified Martini parameters for gangliosides to more closely mimic the sizes and structures of ganglioside clusters observed at the atomistic level.

Computational virology: From the inside out

Viruses typically pack their genetic material within a protein capsid. Enveloped viruses also have an outer membrane made up of a lipid bilayer and membrane-spanning glycoproteins. X-ray diffraction and cryoelectron microscopy provide high resolution static views of viral structure. Molecular dynamics (MD) simulations may be used to provide dynamic insights into the structures of viruses and their components. There have been a number of simulations of viral capsids and (in some cases) of the inner core of RNA or DNA packaged within them. These simulations have generally focussed on the structural integrity and stability of the capsid and/or on the influence of the nucleic acid core on capsid stability. More recently there have been a number of simulation studies of enveloped viruses, including HIV-1, influenza A, and dengue virus. These have addressed the dynamic behaviour of the capsid, the matrix, and/or of the outer envelope. Analysis of the dynamics of the lipid bilayer components of the envelopes of influenza A and of dengue virus reveals a degree of biophysical robustness, which may contribute to the stability of virus particles in different environments. Significant computational challenges need to be addressed to aid simulation of complex viruses and their membranes, including the need to integrate structural data from a range of sources to enable us to move towards simulations of intact virions. This article is part of a Special Issue entitled: Membrane Proteins edited by J.C. Gumbart and Sergei Noskov.