Martini 3 Coarse-Grained Force Field for Cholesterol

GōMartini 3: From large conformational changes in proteins to environmental bias corrections

Coarse-grained modeling has become an important tool to supplement experimental measurements, allowing access to spatio-temporal scales beyond all-atom based approaches. The GōMartini model combines structure- and physics-based coarse-grained approaches, balancing computational efficiency and accurate representation of protein dynamics with the capabilities of studying proteins in different biological environments. This paper introduces an enhanced GōMartini model, which combines a virtual-site implementation of Gō models with Martini 3. The implementation has been extensively tested by the community since the release of the reparametrized version of Martini. This work demonstrates the capabilities of the model in diverse case studies, ranging from protein-membrane binding to protein-ligand interactions and AFM force profile calculations. The model is also versatile, as it can address recent inaccuracies reported in the Martini protein model. Lastly, the paper discusses the advantages, limitations, and future perspectives of the Martini 3 protein model and its combination with Gō models.

Structural insights into terminal arabinosylation of mycobacterial cell wall arabinan

The global challenge of tuberculosis, caused by Mycobacterium tuberculosis (Mtb), is compounded by the emergence of drug-resistant strains. A critical factor in Mtb's pathogenicity is its intricate cell envelope, which acts as a formidable barrier against immune defences and pharmacological interventions. Central to this envelope are arabinogalactan (AG) and lipoarabinomannan (LAM), two complex polysaccharides containing arabinan domains essential for maintaining cell wall structure and function. The arabinofuranosyltransferase AftB plays a pivotal role in the biosynthesis of these arabinan domains by catalyzing the addition of β-(1 → 2)-linked terminal arabinofuranose residues. Here, we present the cryo-EM structures of Mycobacterium chubuense AftB in both its apo form and bound to a donor substrate analog, resolved at 2.9 Å and 3.4 Å resolution, respectively. These structures reveal that AftB has a GT-C fold, with a transmembrane (TM) domain comprised of eleven TM helices and a periplasmic cap domain. AftB has a distinctive irregular, tube-shaped cavity that connects two proposed substrate binding sites. Through an integrated approach combining structural analysis, biochemical assays, and molecular dynamics simulations, we delineate the molecular basis of AftB's reaction mechanism and propose a model for its catalytic function.

Mechanistic studies of mycobacterial glycolipid biosynthesis by the mannosyltransferase PimE

Tuberculosis (TB), a leading cause of death among infectious diseases globally, is caused by Mycobacterium tuberculosis (Mtb). The pathogenicity of Mtb is largely attributed to its complex cell envelope, which includes a class of glycolipids called phosphatidyl-myo-inositol mannosides (PIMs). These glycolipids maintain the integrity of the cell envelope, regulate permeability, and mediate host-pathogen interactions. PIMs comprise a phosphatidyl-myo-inositol core decorated with one to six mannose residues and up to four acyl chains. The mannosyltransferase PimE catalyzes the transfer of the fifth PIM mannose residue from a polyprenyl phosphate-mannose (PPM) donor. This step contributes to the proper assembly and function of the mycobacterial cell envelope; however, the structural basis for substrate recognition and the catalytic mechanism of PimE remain poorly understood. Here, we present the cryo-electron microscopy (cryo-EM) structures of PimE from Mycobacterium abscessus in its apo and product-bound form. The structures reveal a distinctive binding cavity that accommodates both donor and acceptor substrates/products. Key residues involved in substrate coordination and catalysis were identified and validated via in vitro assays and in vivo complementation, while molecular dynamics simulations delineated access pathways and binding dynamics. Our integrated approach provides comprehensive insights into PimE function and informs potential strategies for anti-TB therapeutics.

Molecular mapping and functional validation of GLP-1R cholesterol binding sites in pancreatic beta cells

G protein-coupled receptors (GPCRs) are integral membrane proteins which closely interact with their plasma membrane lipid microenvironment. Cholesterol is a lipid enriched at the plasma membrane with pivotal roles in the control of membrane fluidity and maintenance of membrane microarchitecture, directly impacting on GPCR stability, dynamics, and function. Cholesterol extraction from pancreatic beta cells has previously been shown to disrupt the internalisation, clustering, and cAMP responses of the glucagon-like peptide-1 receptor (GLP-1R), a class B1 GPCR with key roles in the control of blood glucose levels via the potentiation of insulin secretion in beta cells and weight reduction via the modulation of brain appetite control centres. Here, we unveil the detrimental effect of a high cholesterol diet on GLP-1R-dependent glucoregulation in vivo, and the improvement in GLP-1R function that a reduction in cholesterol synthesis using simvastatin exerts in pancreatic islets. We next identify and map sites of cholesterol high occupancy and residence time on active vs inactive GLP-1Rs using coarse-grained molecular dynamics (cgMD) simulations, followed by a screen of key residues selected from these sites and detailed analyses of the effects of mutating one of these, Val229, to alanine on GLP-1R-cholesterol interactions, plasma membrane behaviours, clustering, trafficking and signalling in INS-1 832/3 rat pancreatic beta cells and primary mouse islets, unveiling an improved insulin secretion profile for the V229A mutant receptor. This study (1) highlights the role of cholesterol in regulating GLP-1R responses in vivo; (2) provides a detailed map of GLP-1R - cholesterol binding sites in model membranes; (3) validates their functional relevance in beta cells; and (4) highlights their potential as locations for the rational design of novel allosteric modulators with the capacity to fine-tune GLP-1R responses.

Cholesterol-dependent dimerization and conformational dynamics of EphA2 receptors from coarse-grained and all-atom simulations

The EphA2 transmembrane receptor regulates cellular growth, differentiation, and motility, and its overexpression in various cancers makes it a potential biomarker for clinical cancer management. EphA2 signaling occurs through ligand-induced dimerization where the transmembrane (TM) and juxtamembrane (JM) domains play crucial roles in stabilizing the dimer conformations, thereby facilitating signal transduction. Electrostatic interactions between basic JM residues and signaling lipids (PIP2 and PIP3) regulate phosphorylation while cholesterol's potential role in modulating EphA2 activation remains unclear. To investigate this, we modeled the TM-full JM peptide of EphA2 and employed coarse-grain and all-atom simulations to investigate its dimerization in cholesterol-rich and cholesterol-deficient membranes. Our findings reveal that cholesterol stabilizes specific TM dimers and TM-JM interactions with PIP2, highlighting the importance of membrane composition in EphA2 dimerization, oligomerization, and clustering. These insights enhance our understanding of lipid-mediated regulation of EphA2 and its implications in receptor signaling and cancer progression.

MartiniGlass: a Tool for Enabling Visualization of Coarse-Grained Martini Topologies

As molecular modeling gains ever more prominence in understanding cellular processes, high quality visualization of models and dynamics has never been more important. Naturally, much molecular visualization software is written to enable the visualization of atomic level details in structures. While necessary, this means that visualization of increasingly popular coarse-grained (CG) models remains a challenge. Here, we present a Python package, MartiniGlass, that facilitates the visualization of systems simulated with the widely used CG Martini force field using the popular visualization package VMD. MartiniGlass rapidly processes molecular topologies and accounts for important topological features at CG resolution, such as secondary structure restraints, preparing them for easy visualization of simulated trajectories.

Cholesterol-Dependent Dimerization and Conformational Dynamics of EphA2 Receptors: Insights from Coarse-Grained and All-Atom Simulations

The EphA2 transmembrane receptor regulates cellular growth, differentiation, and motility, and its overexpression in various cancers makes it a potential biomarker for clinical cancer management. EphA2 signaling occurs through ligand-induced dimerization, where the transmembrane (TM) and juxtamembrane (JM) domains play crucial roles in stabilizing the dimer conformations and thereby facilitating signal transduction. Electrostatic interactions between basic JM residues and signaling lipids (PIP2 and PIP3) regulate phosphorylation while Cholesterol's potential role in modulating EphA2 activation remains unclear. To investigate this, we modeled the TM-full JM peptide of EphA2 and employed coarse-grain and all-atom simulations to investigate its dimerization in cholesterol-rich and cholesterol-deficient membranes. Our findings reveal that cholesterol stabilizes specific TM dimers and TM-JM interactions with PIP2, highlighting the importance of membrane composition in EphA2 dimerization, oligomerization, and clustering. These insights enhance our understanding of lipid-mediated regulation of EphA2 and its implications in receptor signaling and cancer progression.

Extending the Martini 3 Coarse-Grained Force Field to Hyaluronic Acid

Hyaluronan, also known as hyaluronic acid, is a large glycosaminoglycan composed of repeating disaccharide units. It plays a crucial role in providing structural support, hydration, and facilitating cellular processes in connective tissue, skin, and the extracellular matrix in biological systems. We present a coarse-grained (CG) model of hyaluronic acid (HA) and its constituent residues, N-acetyl-d-glucosamine (GlcNAc) and glucuronic acid (GlcA), designed to be compatible with the Martini 3 force field. The model was validated against atomistic molecular dynamics simulations following standard procedures to ensure the accuracy of bonded interactions and, in the case of GlcNAc, the free energies of transfer between octanol and water. For the final HA model, we investigated its properties by simulating the self-assembly of HA chains at varying ion concentrations in solution and comparing the persistence length of single-chain HA with experimental data. We also studied the interactions of HA with lipid bilayers and various HA-binding proteins, demonstrating the ability of the model to accurately reproduce interactions with other biomolecules characteristic of natural biological systems. This extension of the carbohydrate-dedicated branch of the CG Martini 3 force field enables large-scale molecular dynamics simulations of HA-containing systems and contributes to a better understanding of the roles and functions of hyaluronan in natural biomolecular systems.

Computational Methods for Modeling Lipid-Mediated Active Pharmaceutical Ingredient Delivery

Lipid-mediated delivery of active pharmaceutical ingredients (API) opened new possibilities in advanced therapies. By encapsulating an API into a lipid nanocarrier (LNC), one can safely deliver APIs not soluble in water, those with otherwise strong adverse effects, or very fragile ones such as nucleic acids. However, for the rational design of LNCs, a detailed understanding of the composition-structure-function relationships is missing. This review presents currently available computational methods for LNC investigation, screening, and design. The state-of-the-art physics-based approaches are described, with the focus on molecular dynamics simulations in all-atom and coarse-grained resolution. Their strengths and weaknesses are discussed, highlighting the aspects necessary for obtaining reliable results in the simulations. Furthermore, a machine learning, i.e., data-based learning, approach to the design of lipid-mediated API delivery is introduced. The data produced by the experimental and theoretical approaches provide valuable insights. Processing these data can help optimize the design of LNCs for better performance. In the final section of this Review, state-of-the-art of computer simulations of LNCs are reviewed, specifically addressing the compatibility of experimental and computational insights.

Plasmodesmata act as unconventional membrane contact sites regulating intercellular molecular exchange in plants

Membrane contact sites (MCSs) are fundamental for intracellular communication, but their role in intercellular communication remains unexplored. We show that in plants, plasmodesmata communication bridges function as atypical endoplasmic reticulum (ER)-plasma membrane (PM) tubular MCSs, operating at cell-cell interfaces. Similar to other MCSs, ER-PM apposition is controlled by a protein-lipid tethering complex, but uniquely, this serves intercellular communication. Combining high-resolution microscopy, molecular dynamics, and pharmacological and genetic approaches, we show that cell-cell trafficking is modulated through the combined action of multiple C2 domains transmembrane domain proteins (MCTPs) 3, 4, and 6 ER-PM tethers and phosphatidylinositol-4-phosphate (PI4P) lipid. Graded PI4P amounts regulate MCTP docking to the PM, their plasmodesmata localization, and cell-cell permeability. SAC7, an ER-localized PI4P-phosphatase, regulates MCTP4 accumulation at plasmodesmata and modulates cell-cell trafficking capacity in a cell-type-specific manner. Our findings expand MCS functions in information transmission from intracellular to intercellular cellular activities.

Understanding the coarse-grained free energy landscape of phospholipids and their phase separation

The cell membrane exhibits lateral heterogeneity due to the preferential association among the large number of lipid species that constitute the membrane. In particular, the preferential association of cholesterol (CHOL) with saturated lipids into ordered domains has been an area of intense investigation. The large spatiotemporal scales that comprise spontaneous domain formation largely precludes computational investigation via conventional all-atom molecular dynamics. We demonstrate here that molecular coarse-grained (CG) models, obtained from the bottom-up, i.e., via statistical mechanical renormalization of atomistic models, are capable of spontaneous assembly and phase separation for two model raft-like systems, DLiPC/DPPC/CHOL and DOPC/DPPC/CHOL. The resulting bottom-up CG models exhibit spontaneous self-assembly and phase separation and recapitulate the structural correlations of the underlying atomistic models. The accuracy and fast dynamics of these CG models constitute an effective means of bypassing the limited spatiotemporal scales of atomistic simulations. As the first bottom-up CG models of lipid phase separation, the CG models in this work provide an informative analysis for further construction of bottom-up CG models transferable across a range of lipid compositions.

How well do empirical molecular mechanics force fields model the cholesterol condensing effect?

Membrane properties are determined in part by lipid composition, and cholesterol plays a large role in determining these properties. Cellular membranes show a diverse range of cholesterol compositions, the effects of which include alterations to cellular biomechanics, lipid raft formation, membrane fusion, signaling pathways, metabolism, pharmaceutical therapeutic efficacy, and disease onset. In addition, cholesterol plays an important role in non-cellular membranes, with its concentration in the skin lipid matrix being implicated in several skin diseases. In phospholipid membranes, cholesterol increases the tail ordering of neighboring lipids, decreasing the membrane lateral area and increasing the thickness. This reduction in the lateral area, known as the cholesterol condensing effect, results from cholesterol-lipid mixtures deviating from ideal mixing. Capturing the cholesterol condensing effect is crucial for molecular dynamics simulations as it directly affects the accuracy of predicted membrane properties, which are essential for understanding membrane function. We present a comparative analysis of cholesterol models across several popular force fields: CHARMM36, Slipids, Lipid17, GROMOS 53A6L, GROMOS-CKP, MARTINI 2, MARTINI 3, and ELBA. The simulations of 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) and 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC) membranes with varying cholesterol concentrations were conducted to calculate the partial-molecular areas of cholesterol and other condensing parameters, which are compared to the experimental data for validation. While all tested force fields predict small negative deviations from ideal mixing in cholesterol-DOPC membranes, only all-atom force fields capture the larger deviations expected in DMPC membranes. United-atom and coarse-grained models under-predict this effect, condensing fewer neighboring lipids by smaller magnitudes, resulting in too small deviations from ideal mixing. These results suggest that all-atom force fields, particularly CHARMM36 or Slipids, should be used for accurate simulations of cholesterol-containing membranes.

The Role of Cholesterol in M2 Clustering and Viral Budding Explained

The influenza A M2 homotetrameric channel consists of four transmembrane (TM) and four amphipathic helices (AHs). This viral proton channel is suggested to form clusters in the catenoid budding neck areas in raft-like domains of the plasma membrane, resulting in cell membrane scission and viral release. The channel clustering environment is rich in cholesterol. Previous experiments have shown that cholesterol significantly contributes to lipid bilayer undulations in viral buds. However, a clear explanation of membrane curvature from the distribution of cholesterol around the M2TM-AH clusters is lacking. Using coarse-grained molecular dynamics simulations of M2TM-AH in bilayers, we observed that M2 channels form specific, C2-symmetric, clusters with conical shapes driven by the attraction of their AHs. We showed that cholesterol stabilized the formation of M2 channel clusters by filling and bridging the conical gap between M2 channels at specific sites in the N-termini of adjacent channels or via the C-terminal region of TM and AHs, with the latter sites displaying a longer interaction time and higher stability. The potential of mean force calculations showed that when cholesterols occupy the identified interfacial binding sites between two M2 channels, the dimer is stabilized by 11 kJ/mol. This translates to the cholesterol-bound dimer being populated by almost 2 orders of magnitude compared to a dimer lacking cholesterol. We demonstrated that the cholesterol-bridged M2 channels can exert a lateral force on the surrounding membrane to induce the necessary negative Gaussian curvature profile, which permits spontaneous scission of the catenoid membrane neck and leads to viral buds and scission.

Validation of a Coarse-Grained Martini 3 Model for Molecular Oxygen

Molecular oxygen (O2) is essential for life, and continuous effort has been made to understand its pathways in cellular respiration with all-atom (AA) molecular dynamics (MD) simulations of, e.g., membrane permeation or binding to proteins. To reach larger length scales with models, such as curved membranes in mitochondria or caveolae, coarse-grained (CG) simulations could be used at much lower computational cost than AA simulations. Yet a CG model for O2 is lacking. In this work, a CG model for O2 is therefore carefully selected from the Martini 3 force field based on criteria including size, zero charge, nonpolarity, solubility in nonpolar organic solvents, and partitioning in a phospholipid membrane. This chosen CG model for O2 (TC3 bead) is then further evaluated through the calculation of its diffusion constant in water and hexadecane, its permeability rate across pure phospholipid- and cholesterol-containing membranes, and its binding to the T4 lysozyme L99A protein. Our CG model shows semiquantitative agreement between CG diffusivity and permeation rates with the corresponding AA values and available experimental data. Additionally, it captures the binding to hydrophobic cavities of the protein, aligning well with the AA simulation of the same system. Thus, the results show that our O2 model approximates the behavior observed in the AA simulations. The CG O2 model is compatible with the widely used multifunctional Martini 3 force field for biological simulations, which will allow for the simulation of large biomolecular systems involved in O2's transport in the body.

G-Protein-Coupled Receptor Surface Creates a Favorable Pathway for Membrane Permeation of Drug Molecules

G-protein-coupled receptors (GPCRs) play a crucial role in modulating physiological responses and serve as the main drug target. Specifically, salmeterol and salbutamol, which are used for the treatment of pulmonary diseases, exert their effects by activating the GPCR β2-adrenergic receptor (β2AR). In our study, we employed coarse-grained molecular dynamics simulations with the Martini 3 force field to investigate the dynamics of drug molecules in membranes in the presence and absence of β2AR. Our simulations reveal that, in more than 50% of the flip-flop events, the drug molecules use the β2AR surface to permeate the membrane. The pathway along the GPCR surface is significantly more energetically favorable for the drug molecules, which was revealed by umbrella sampling simulations along spontaneous flip-flop pathways. Furthermore, we assessed the behavior of drugs with intracellular targets, such as kinase inhibitors, whose therapeutic efficacy could benefit from this observation. In summary, our results show that β2AR surface interactions can significantly enhance the membrane permeation of drugs, emphasizing their potential for consideration in future drug development strategies.

Insertion and Anchoring of the HIV-1 Fusion Peptide into a Complex Membrane Mimicking the Human T-Cell

A fundamental understanding of how the HIV-1 envelope (Env) protein facilitates fusion is still lacking. The HIV-1 fusion peptide, consisting of 15 to 22 residues, is the N-terminus of the gp41 subunit of the Env protein. Further, this peptide, a promising vaccine candidate, initiates viral entry into target cells by inserting and anchoring into human immune cells. The influence of membrane lipid reorganization and the conformational changes of the fusion peptide during the membrane insertion and anchoring processes, which can significantly affect HIV-1 cell entry, remains largely unexplored due to the limitations of experimental measurements. In this work, we investigate the insertion of the fusion peptide into an immune cell membrane mimic through multiscale molecular dynamics simulations. We mimic the native T-cell by constructing a nine-lipid asymmetric membrane, along with geometrical restraints accounting for insertion in the context of gp41. To account for the slow time scale of lipid mixing while enabling conformational changes, we implement a protocol to go back and forth between atomistic and coarse-grained simulations. Our study provides a molecular understanding of the interactions between the HIV-1 fusion peptide and the T-cell membrane, highlighting the importance of the conformational flexibility of fusion peptides and local lipid reorganization in stabilizing the anchoring of gp41 into the targeted host membrane during the early events of HIV-1 cell entry. Importantly, we identify a motif within the fusion peptide critical for fusion that can be further manipulated in future immunological studies.

Lipid organization by the Caveolin-1 complex

Caveolins are lipid-binding proteins that can organize membrane remodeling and oligomerize into the 8S complex. The CAV1-8S complex comprises a disk-like structure, about 15 nm in diameter, with a central beta barrel. Further oligomerization of 8S complexes remodels the membrane into caveolae vessels, with a dependence on cholesterol concentration. However, the molecular mechanisms behind membrane remodeling and cholesterol filtering are still not understood. Performing atomistic molecular dynamics simulations in combination with advanced sampling techniques, we describe how the CAV1-8S complex bends the membrane and accumulates cholesterol. Here, our simulations show an enhancing effect by the palmitoylations of CAV1, and we predict that the CAV1-8S complex can extract cholesterol molecules from the lipid bilayer and accommodate them in its beta barrel. Through backmapping to the all-atom level, we also conclude that the Martini v.2 coarse-grained force field overestimates membrane bending, as the atomistic simulations exhibit only very localized bending.

Dual regulation of IP3 receptors by IP3 and PIP2 controls the transition from local to global Ca2+ signals

The spatial organization of inositol 1,4,5-trisphosphate (IP3)-evoked Ca2+ signals underlies their versatility. Low stimulus intensities evoke Ca2+ puffs, localized Ca2+ signals arising from a few IP3 receptors (IP3Rs) within a cluster tethered beneath the plasma membrane. More intense stimulation evokes global Ca2+ signals. Ca2+ signals propagate regeneratively as the Ca2+ released stimulates more IP3Rs. How is this potentially explosive mechanism constrained to allow local Ca2+ signaling? We developed methods that allow IP3 produced after G-protein coupled receptor (GPCR) activation to be intercepted and replaced by flash photolysis of a caged analog of IP3. We find that phosphatidylinositol 4,5-bisphosphate (PIP2) primes IP3Rs to respond by partially occupying their IP3-binding sites. As GPCRs stimulate IP3 formation, they also deplete PIP2, relieving the priming stimulus. Loss of PIP2 resets IP3R sensitivity and delays the transition from local to global Ca2+ signals. Dual regulation of IP3Rs by PIP2 and IP3 through GPCRs controls the transition from local to global Ca2+ signals.

Depletion of membrane cholesterol modifies structure, dynamic and activation of Nav1.7

Cholesterol is a major component of plasma membranes and plays a significant role in actively regulating the functioning of several membrane proteins in humans. In this study, we focus on the role of cholesterol depletion on the voltage-gated sodium channel Nav1.7, which is primarily expressed in the peripheral sensory neurons and linked to various chronic inherited pain syndromes. Coarse-grained molecular dynamics simulations revealed key dynamic changes of Nav1.7 upon membrane cholesterol depletion: A loss of rigidity in the structural motifs linked to activation and fast-inactivation is observed, suggesting an easier transition of the channel between different gating states. In-vitro whole-cell patch clamp experiments on HEK293t cells expressing Nav1.7 validated these predictions at the functional level: Hyperpolarizing shifts in the voltage-dependence of activation and fast-inactivation were observed along with an acceleration of the time to peak and onset kinetics of fast inactivation. These results underline the critical role of membrane composition, and of cholesterol in particular, in influencing Nav1.7 gating characteristics. Furthermore, our results also point to cholesterol-driven changes of the geometry of drug-binding regions, hinting to a key role of the membrane environment in the regulation of drug effects.

Self-diffusion is temperature independent on active membranes

Molecular transport maintains cellular structures and functions. For example, lipid and protein diffusion sculpts the dynamic shapes and structures on the cell membrane that perform essential cellular functions, such as cell signaling. Temperature variations in thermal equilibrium rapidly change molecular transport properties. The coefficient of lipid self-diffusion increases exponentially with temperature in thermal equilibrium, for example. Hence, maintaining cellular homeostasis through molecular transport is hard in thermal equilibrium in the noisy cellular environment, where temperatures can fluctuate widely due to local heat generation. In this paper, using both molecular and lattice-based modeling of membrane transport, we show that the presence of active transport originating from the cell's cytoskeleton can make the self-diffusion of the molecules on the membrane robust to temperature fluctuations. The resultant temperature-independence of self-diffusion keeps the precision of cellular signaling invariant over a broad range of ambient temperatures, allowing cells to make robust decisions. We have also found that the Kawasaki algorithm, the widely used model of lipid transport on lattices, predicts incorrect temperature dependence of lipid self-diffusion in equilibrium. We propose a new algorithm that correctly captures the equilibrium properties of lipid self-diffusion and reproduces experimental observations.

Insertion and Anchoring of HIV-1 Fusion Peptide into Complex Membrane Mimicking Human T-cell

A fundamental understanding of how HIV-1 envelope (Env) protein facilitates fusion is still lacking. The HIV-1 fusion peptide, consisting of 15 to 22 residues, is the N-terminus of the gp41 subunit of the Env protein. Further, this peptide, a promising vaccine candidate, initiates viral entry into target cells by inserting and anchoring into human immune cells. The influence of membrane lipid reorganization and the conformational changes of the fusion peptide during the membrane insertion and anchoring processes, which can significantly affect HIV-1 cell entry, remains largely unexplored due to the limitations of experimental measurements. In this work, we investigate the insertion of the fusion peptide into an immune cell membrane mimic through multiscale molecular dynamics simulations. We mimic the native T-cell by constructing a 9-lipid asymmetric membrane, along with geometrical restraints accounting for insertion in the context of gp41. To account for the slow timescale of lipid mixing while enabling conformational changes, we implement a protocol to go back and forth between atomistic and coarse-grained simulations. Our study provides a molecular understanding of the interactions between the HIV-1 fusion peptide and the T-cell membrane, highlighting the importance of conformational flexibility of fusion peptides and local lipid reorganization in stabilizing the anchoring of gp41 into the targeted host membrane during the early events of HIV-1 cell entry. Importantly, we identify a motif within the fusion peptide critical for fusion that can be further manipulated in future immunological studies.

Exploring the Properties of Curved Lipid Membranes: Comparative Analysis of Atomistic and Coarse-Grained Force Fields

Curvature emerges as a fundamental membrane characteristic crucial for diverse biological processes, including vesicle formation, cell signaling, and membrane trafficking. Increasingly valuable insights into atomistic details governing curvature-dependent membrane properties are provided by computer simulations. Nevertheless, the underlying force field models are conventionally calibrated and tested in relation to experimentally derived parameters of planar bilayers, thereby leaving uncertainties concerning their consistency in reproducing curved lipid systems. In this study we compare the depiction of buckled phosphatidylcholine (POPC) and POPC-cholesterol membranes by four popular force field models. Aside from agreement with respect to general trends in curvature dependence of a number of parameters, we observe a few qualitative differences. Among the most prominent ones is the difference between atomistic and coarse grained force fields in their representation of relative compressibility of the polar headgroup region and hydrophobic lipid core. Through a number of downstream effects, this discrepancy can influence the way in which curvature modulates the behavior of membrane bound proteins depending on the adopted simulation model.

Lipid Organization by the Caveolin-1 Complex

Caveolins are lipid-binding proteins that can organize membrane remodeling and oligomerize into the 8S-complex. The CAV1 8S-complex comprises a disk-like structure, about 15nm in diameter, with a central beta barrel. Further oligomerization of 8S-complexes remodels the membrane into caveolae vessels, with a dependence on cholesterol concentration. However, the molecular mechanisms behind membrane remodeling and cholesterol filtering are still not understood. Performing atomistic Molecular Dynamics simulations in combination with advanced sampling techniques, we describe how the CAV1-8S complex bends the membrane and accumulates cholesterol. Here, our simulations show an enhancing effect by the palmitoylations of CAV1, and we predict that the CAV1-8S complex can extract cholesterol molecules from the lipid bilayer and accommodate them in its beta barrel. Through backmapping to the all-atom level we also conclude that the Martini v2 coarse-grained forcefield overestimates membrane bending, as the atomistic simulations exhibit only very localized bending.

Assessing the Martini 3 protein model: A review of its path and potential

Coarse-grained (CG) protein models have become indispensable tools for studying many biological protein details, from conformational dynamics to the organization of protein macro-complexes, and even the interaction of proteins with other molecules. The Martini force field is one of the most widely used CG models for bio-molecular simulations, partly because of the enormous success of its protein model. With the recent release of a new and improved version of the Martini force field - Martini 3 - a new iteration of its protein model was also made available. The Martini 3 protein force field is an evolution of its Martini 2 counterpart, aimed at improving many of the shortcomings that had been previously identified. In this mini-review, we first provide a general overview of the model and then focus on the successful advances made in the short time since its release, many of which would not have been possible before. Furthermore, we discuss reported limitations, potential directions for model improvement and comment on what the likely future development and application avenues are.

Martini-Based Coarse-Grained Soil Organic Matter Model Derived from Atomistic Simulations

The significance of soil organic matter (SOM) in environmental contexts, particularly its role in pollutant adsorption, has prompted an increased utilization of molecular simulations to understand microscopic interactions. This study introduces a coarse-grained SOM model, parametrized within the framework of the versatile Martini 3 force field. Utilizing models generated by the Vienna Soil Organic Matter Modeler 2, which constructs humic substance systems from a fragment database, we employed Swarm-CG to parametrize the fragments and subsequently assembled them into macromolecules. Direct Boltzmann inversion (DBI) facilitated the determination of bonded parameters between fragments. The parametrization yielded favorable agreement in the radius of gyration and solvent-accessible surface area. Transfer free energies exhibited a strong correlation with hexadecane-water and chloroform-water values, albeit deviations were noted for octanol-water values. Comparing densities of modeled Leonardite humic acid systems at coarse-grained and atomistic levels revealed promising agreement, particularly at higher water concentrations. The DBI approach effectively reproduced average values of bonded interactions between fragments. Radial distribution functions between carboxylate groups and calcium ions showed partial agreement, however, reproducing certain peaks was challenging due to fixed bead sizes. Detailed analysis of atomistic systems revealed different configurations between the groups, explaining discrepancies. The present contribution provides a comprehensive insight into the properties, strengths, and weaknesses of the coarse-grained SOM model, serving as a foundation for future investigations encompassing pollutant interactions and varied SOM compositions.

The Secret Ballet Inside Multivesicular Bodies

Lipid bilayers possess the capacity for self-assembly due to the amphipathic nature of lipid molecules, which have both hydrophobic and hydrophilic regions. When confined, lipid bilayers exhibit astonishing versatility in their forms, adopting diverse shapes that are challenging to observe through experimental means. Exploiting this adaptability, lipid structures motivate the development of bio-inspired mechanomaterials and integrated nanobio-interfaces that could seamlessly merge with biological entities, ultimately bridging the gap between synthetic and biological systems. In this work, we demonstrate how, in numerical simulations of multivesicular bodies, a fascinating evolution unfolds from an initial semblance of order toward states of higher entropy over time. We observe dynamic rearrangements in confined vesicles that reveal unexpected limit shapes of distinct geometric patterns. We identify five structures as the basic building blocks that systematically repeat under various conditions of size and composition. Moreover, we observe more complex and less frequent shapes that emerge in confined spaces. Our results provide insights into the dynamics of multivesicular systems, offering a richer understanding of how confined lipid bodies spontaneously self-organize.

Building complex membranes with Martini 3

The Martini model is a popular force field for coarse-grained simulations. Membranes have always been at the center of its development, with the latest version, Martini 3, showing great promise in capturing more and more realistic behavior. In this chapter we provide a step-by-step tutorial on how to construct starting configurations, run initial simulations and perform dedicated analysis for membrane-based systems of increasing complexity, including leaflet asymmetry, curvature gradients and embedding of membrane proteins.

Characterization of domain formation in complex membranes

A widely known property of lipid membranes is their tendency to undergo a separation into disordered (Ld) and ordered (Lo) domains. This impacts the local structure of the membrane relevant for the physical (e.g., enhanced electroporation) and biological (e.g., protein sorting) significance of these regions. The increase in computing power, advancements in simulation software, and more detailed information about the composition of biological membranes shifts the study of these domains into the focus of classical molecular dynamics simulations. In this chapter, we present a versatile yet robust analysis pipeline that can be easily implemented and adapted for a wide range of lipid compositions. It employs Gaussian-based Hidden Markov Models to predict the hidden order states of individual lipids by describing their structure through the area per lipid and the average SCC order parameters per acyl chain. Regions of the membrane with a high correlation between ordered lipids are identified by employing the Getis-Ord local spatial autocorrelation statistic on a Voronoi tessellation of the lipids. As an example, the approach is applied to two distinct systems at a coarse-grained resolution, demonstrating either a strong tendency towards phase separation (1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC), 1,2-dilinoleoyl-sn-glycero-3-phosphocholine (DIPC), cholesterol) or a weak tendency toward phase separation (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC), 1-palmitoyl-2-docosahexaenoyl-sn-glycero-3-phosphocholine (PUPC), cholesterol). Explanations of the steps are complemented by coding examples written in Python, providing both a comprehensive understanding and practical guidance for a seamless integration of the workflow into individual projects.

Shocker─A Molecular Dynamics Protocol and Tool for Accelerating and Analyzing the Effects of Osmotic Shocks

The process of osmosis, a fundamental phenomenon in life, drives water through a semipermeable membrane in response to a solute concentration gradient across this membrane. In vitro, osmotic shocks are often used to drive shape changes in lipid vesicles, for instance, to study fission events in the context of artificial cells. While experimental techniques provide a macroscopic picture of large-scale membrane remodeling processes, molecular dynamics (MD) simulations are a powerful tool to study membrane deformations at the molecular level. However, simulating an osmotic shock is a time-consuming process due to slow water diffusion across the membrane, making it practically impossible to examine its effects in classic MD simulations. In this article, we present Shocker, a Python-based MD tool for simulating the effects of an osmotic shock by selecting and relocating water particles across a membrane over the course of several pumping cycles. Although this method is primarily aimed at efficiently simulating volume changes in vesicles, it can also handle membrane tubes and double bilayer systems. Additionally, Shocker is force field-independent and compatible with both coarse-grained and all-atom systems. We demonstrate that our tool is applicable to simulate both hypertonic and hypotonic osmotic shocks for a range of vesicular and bilamellar setups, including complex multicomponent systems containing membrane proteins or crowded internal solutions.

Martini 3 Force Field Parameters for Protein Lipidation Post-Translational Modifications

Protein lipidations are vital co/post-translational modifications that tether lipid tails to specific protein amino acids, allowing them to anchor to biological membranes, switch their subcellular localization, and modulate association with other proteins. Such lipidations are thus crucial for multiple biological processes including signal transduction, protein trafficking, and membrane localization and are implicated in various diseases as well. Examples of lipid-anchored proteins include the Ras family of proteins that undergo farnesylation; actin and gelsolin that are myristoylated; phospholipase D that is palmitoylated; glycosylphosphatidylinositol-anchored proteins; and others. Here, we develop parameters for cysteine-targeting farnesylation, geranylgeranylation, and palmitoylation, as well as glycine-targeting myristoylation for the latest version of the Martini 3 coarse-grained force field. The parameters are developed using the CHARMM36m all-atom force field parameters as reference. The behavior of the coarse-grained models is consistent with that of the all-atom force field for all lipidations and reproduces key dynamical and structural features of lipid-anchored peptides, such as the solvent-accessible surface area, bilayer penetration depth, and representative conformations of the anchors. The parameters are also validated in simulations of the lipid-anchored peripheral membrane proteins Rheb and Arf1, after comparison with independent all-atom simulations. The parameters, along with mapping schemes for the popular martinize2 tool, are available for download at 10.5281/zenodo.7849262 and also as supporting information.

CGCompiler: Automated Coarse-Grained Molecule Parametrization via Noise-Resistant Mixed-Variable Optimization

Coarse-grained force fields (CG FFs) such as the Martini model entail a predefined, fixed set of Lennard-Jones parameters (building blocks) to model virtually all possible nonbonded interactions between chemically relevant molecules. Owing to its universality and transferability, the building-block coarse-grained approach has gained tremendous popularity over the past decade. The parametrization of molecules can be highly complex and often involves the selection and fine-tuning of a large number of parameters (e.g., bead types and bond lengths) to optimally match multiple relevant targets simultaneously. The parametrization of a molecule within the building-block CG approach is a mixed-variable optimization problem: the nonbonded interactions are discrete variables, whereas the bonded interactions are continuous variables. Here, we pioneer the utility of mixed-variable particle swarm optimization in automatically parametrizing molecules within the Martini 3 coarse-grained force field by matching both structural (e.g., RDFs) as well as thermodynamic data (phase-transition temperatures). For the sake of demonstration, we parametrize the linker of the lipid sphingomyelin. The important advantage of our approach is that both bonded and nonbonded interactions are simultaneously optimized while conserving the search efficiency of vector guided particle swarm optimization (PSO) methods over other metaheuristic search methods such as genetic algorithms. In addition, we explore noise-mitigation strategies in matching the phase-transition temperatures of lipid membranes, where nucleation and concomitant hysteresis introduce a dominant noise term within the objective function. We propose that noise-resistant mixed-variable PSO methods can both improve and automate parametrization of molecules within building-block CG FFs, such as Martini.