MARTINI Model Research Articles

Martini 3D-OZ: A Theoretical Investigation of Solvation Shell Structures and Solvation Free Energies of Martini Coarse-Grained Proteins.

We investigate the properties of aqueous solutions using integral equation theories and molecular dynamics (MD) simulations within the framework of the MARTINI coarse-grained force field. The integral equation theory used in the present work is based on the Ornstein-Zernike equation coupled with the hypernetted chain (HNC) and Kovalenko-Hirata (KH) closures. Overall, the solvation shell structures and solvation thermodynamics in the HNC approximation are shown to be in better agreement with those from the MD simulation than the KH results. Especially, through the analysis of spatial distribution functions of water around a protein, we have demonstrated that the HNC approximation can provide the highly anisotropic structure of the solvation shell of the protein. On the other hand, the KH approximation works well for simple particle solutes, but the results for highly hydrated proteins deviate quite significantly from the MD results. We further explore in detail the reason underlying the deviation caused by the KH approximation. Lastly, a potential application of the integral equation theory with the MARTINI model is outlined.

Molecular Dynamics Simulations Show That Short Peptides Can Drive Synthetic Cell Division by Binding to the Inner Membrane Leaflet.

An important functionality of lifelike "synthetic cells" is to mimic cell division. Currently, specialized proteins that induce membrane fission in living cells are the primary candidates for dividing synthetic cells. However, interactions between lipid membranes and proteins that are not found in living cells may also be suitable. Here, we discuss the potential of short membrane-anchored peptides to induce cell division. Specifically, we used the coarse-grained MARTINI model to investigate the interaction between short membrane-anchored peptides and a lipid bilayer patch. The simulation revealed that the anchored peptide induces significant spontaneous curvature and suggests that the lipid-peptide complex can be considered as a conically shaped "bulky headgroup" lipid. By systematically increasing the electrostatic charge of the peptide, we find that membrane-anchored peptides may generate sufficiently large constriction forces even at dilute coverages. Finally, we show that when the peptide has an opposite charge to the membrane, the peptide may induce division by binding the inner membrane leaflet of a synthetic cell, that is, cell division from within.

The surface tension of Martini 3 water mixtures.

The Martini model, a coarse-grained forcefield for biomolecular simulations, has experienced a vast increase in popularity in the past decade. Its building-block approach balances computational efficiency with high chemical specificity, enabling the simulation of organic and inorganic molecules. The modeling of coarse-grained beads as Lennard-Jones particles poses challenges for the accurate reproduction of liquid-vapor interfacial properties, which are crucial in various applications, especially in the case of water. The latest version of the forcefield introduces refined interaction parameters for water beads, tackling the well-known artifact of Martini water freezing at room temperature. In addition, multiple sizes of water beads are available for simulating the solvation of small cavities, including the smallest pockets of proteins. This work focuses on studying the interfacial properties of Martini water, including surface tension and surface thickness. Employing the test-area method, we systematically compute the liquid-vapor surface tension across various combinations of water bead sizes and for temperatures from 300 to 350K. These findings are of interest to the Martini community as they allow users to account for the low interfacial tension of Martini water by properly adjusting observables computed via coarse-grained simulations to allow for accurate matching against all-atom or experimental results. Surface tension data are also interpreted in terms of local enrichment of the various mixture components at the liquid-vapor interface by means of Gibbs' adsorption formalism. Finally, the critical scaling of the Martini surface tension with temperature is reported to be consistent with the critical exponent of the 3D Ising universality class.

Softening in Two-Component Lipid Mixtures by Spontaneous Curvature Variance.

The bending modulus of a lipid bilayer quantifies its mechanical resistance to curvature. It is typically understood in terms of thickness; e.g., thicker bilayers are usually stiffer. Here, we describe an additional and powerful molecular determinant of stiffness─the variance in the distribution of curvature sensitivity of lipids and lipid conformations. Zwitterionic choline and ethanolamine headgroups of glycerophospholipids dynamically explore inter- and intraspecies interactions, leading to transient clustering. We demonstrate that these clusters couple strongly to negative curvature, exciting undulatory membrane modes and reducing the apparent bending modulus. Three force fields (Martini 2, Martini 3, and all-atom CHARMM C36) each show the effect to a different extent, with the coarse-grained Martini models showing the most clustering and thus the most softening. The theory is a guide to understanding the stiffness of biological membranes with their complex composition, as well as how choices of force field parameterization are translated into mechanical stiffness.

Unveiling Interactions of Tumor-Targeting Nanoparticles with Lipid Bilayers Using a Titratable Martini Model.

pH-responsive nanoparticles are ideal vehicles for drug delivery and are widely used in cell imaging in targeted therapy of cancer, which usually has a weakly acidic microenvironment. In this work, we constructed a titratable molecular model for nanoparticles grafted with ligands of pH-sensitive carboxylic acids and investigated the interactions between the nanoparticles and the lipid bilayer in varying pH environments. We mainly examined the effect of the grafting density of the pH-sensitive ligands of the nanoparticles on the interactions of the nanoparticles with the lipid bilayer. The results show that the nanoparticles can penetrate the lipid bilayer only when the pH value is lower than a critical value, which can be readily modulated to the specific pH value of the tumor microenvironment by changing the ligand grafting density. This work provides some insights into modulating the interactions between the pH-sensitive nanoparticles and cellular membranes to realize targeted drug delivery to tumors based on their specific pH environment.

Boundary Lubrication Performance of Polyelectrolyte-Surfactant Complexes on Biomimetic Surfaces.

Aqueous mixtures of oppositely charged polyelectrolytes and surfactants are useful in many industrial applications, such as shampoos and hair conditioners. In this work, we investigate the friction between biomimetic hair surfaces in the presence of adsorbed complexes formed from cationic polyelectrolytes and anionic surfactants in an aqueous solution. We apply nonequilibrium molecular dynamics (NEMD) simulations using the coarse-grained MARTINI model. We first developed new MARTINI parameters for cationic guar gum (CGG), a functionalized, plant-derived polysaccharide. The complexation of CGG and the anionic surfactant sodium dodecyl sulfate (SDS) on virgin and chemically damaged biomimetic hair surfaces was studied using a sequential adsorption approach. We then carried out squeeze-out and sliding NEMD simulations to assess the boundary lubrication performance of the CGG-SDS complex compressed between two hair surfaces. At low pressure, we observe a synergistic friction behavior for the CGG-SDS complex, which gives lower shear stress than either pure CGG or SDS. Here, friction is dominated by viscous dissipation in an interfacial layer comprising SDS and water. At higher pressures, which are probably beyond those usually experienced during hair manipulation, SDS and water are squeezed out, and friction increases due to interdigitation. The outcomes of this work are expected to be beneficial to fine-tune and screen sustainable hair care formulations to provide low friction and therefore a smooth feel and reduced entanglement.

Construction of Multiscale Dissipative Particle Dynamics (DPD) Models from Other Coarse-Grained Models.

We present a general scheme for converting coarse-grained models into Dissipative Particle Dynamics (DPD) models. We build the corresponding DPD models by analogy with the de novo DPD coarse-graining scheme suggested by Groot and Warren (J. Chem. Phys., 1997). Electrostatic interactions between charged DPD particles are represented though the addition of a long-range Slater Coulomb potential as suggested by González-Melchor et al. (J. Chem. Phys., 2006). The construction is illustrated by converting MARTINI models for various proteins into a DPD representation, but it not restricted to the usual potential form in the MARTINI model-viz., Lennard-Jones potentials. We further extended the DPD scheme away from the typical use of homogeneous particle sizes, therefore faithfully representing the variations in the particle sizes seen in the underlying MARTINI model. The accuracy of the resulting construction of our generalized DPD models with respect to several structural observables has been benchmarked favorably against all-atom and MARTINI models for a selected set of peptides and proteins, and variations in the scales of the coarse-graining of the water solvent.

Sequence-dependent material properties of biomolecular condensates and their relation to dilute phase conformations

Material properties of phase-separated biomolecular condensates, enriched with disordered proteins, dictate many cellular functions. Contrary to the progress made in understanding the sequence-dependent phase separation of proteins, little is known about the sequence determinants of condensate material properties. Using the hydropathy scale and Martini models, we computationally decipher these relationships for charge-rich disordered protein condensates. Our computations yield dynamical, rheological, and interfacial properties of condensates that are quantitatively comparable with experimentally characterized condensates. Interestingly, we find that the material properties of model and natural proteins respond similarly to charge segregation, despite different sequence compositions. Molecular interactions within the condensates closely resemble those within the single-chain ensembles. Consequently, the material properties strongly correlate with molecular contact dynamics and single-chain structural properties. We demonstrate the potential to harness the sequence characteristics of disordered proteins for predicting and engineering the material properties of functional condensates, with insights from the dilute phase properties.

Improved Protein Dynamics and Hydration in the Martini3 Coarse-Grain Model.

The Martini coarse-grain force-field has emerged as an important framework to probe cellular processes at experimentally relevant time- and length-scales. However, the recently developed version, the Martini3 force-field with the implemented Go̅ model (Martini3Go̅), as well as previous variants of the Martini model have not been benchmarked and rigorously tested for globular proteins. In this study, we consider three globular proteins, ubiquitin, lysozyme, and cofilin, and compare protein dynamics and hydration with observables from experiments and all-atom simulations. We show that the Martini3Go̅ model is able to accurately model the structural and dynamic features of small globular proteins. Overall, the structural integrity of the proteins is maintained, as validated by contact maps, radii of gyration (Rg), and SAXS profiles. The chemical shifts predicted from the ensemble sampled in the simulations are consistent with the experimental data. Further, a good match is observed in the protein-water interaction energetics, and the hydration levels of the residues are similar to atomistic simulations. However, the protein-water interaction dynamics is not accurately represented and appears to depend on the protein structural complexity, residue specificity, and water dynamics. Our work is a step toward testing and assessing the Martini3Go̅ model and provides insights into future efforts to refine Martini models with improved solvation effects and better correspondence to the underlying all-atom systems.

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.

Unraveling mechanisms of protein encapsulation and release in coacervates via molecular dynamics and machine learning.

Coacervates play a pivotal role in protein-based drug delivery research, yet their drug encapsulation and release mechanisms remain poorly understood. Here, we utilized the Martini model to investigate bovine serum albumin (BSA) protein encapsulation and release within polylysine/polyglutamate (PLys/PGlu) coacervates. Our findings emphasize the importance of ingredient addition sequence in coacervate formation and encapsulation rates, attributed to preference contact between oppositely charged proteins and poly(amino acid)s. Notably, coacervates composed of β-sheet poly(amino acid)s demonstrate greater BSA encapsulation efficiency due to their reduced entropy and flexibility. Furthermore, we examined the pH responsiveness of coacervates, shedding light on the dissolution process driven by Coulomb forces. By leveraging machine learning algorithms to analyze simulation results, our research advances the understanding of coacervate-based drug delivery systems, with the ultimate goal of optimizing therapeutic outcomes.

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.

Multiscale simulations reveal TDP-43 molecular-level interactions driving condensation

The RNA-binding protein TDP-43 is associated with mRNA processing and transport from the nucleus to the cytoplasm. TDP-43 localizes in the nucleus as well as accumulating in cytoplasmic condensates such as stress granules. Aggregation and formation of amyloid-like fibrils of cytoplasmic TDP-43 are hallmarks of numerous neurodegenerative diseases, most strikingly present in >90% of amyotrophic lateral sclerosis (ALS) patients. If excessive accumulation of cytoplasmic TDP-43 causes, or is caused by, neurodegeneration is presently not known. In this work, we use molecular dynamics simulations at multiple resolutions to explore TDP-43 self- and cross-interaction dynamics. A full-length molecular model of TDP-43, all 414 amino acids, was constructed from select structures of the protein functional domains (N-terminal domain, and two RNA recognition motifs, RRM1 and RRM2) and modeling of disordered connecting loops and the low complexity glycine-rich C-terminus domain. All-atom CHARMM36m simulations of single TDP-43 proteins served as guides to construct a coarse-grained Martini 3 model of TDP-43. The Martini model and a coarser implicit solvent C⍺ model, optimized for disordered proteins, were subsequently used to probe TDP-43 interactions; self-interactions from single-chain full-length TDP-43 simulations, cross-interactions from simulations with two proteins and simulations with assemblies of dozens to hundreds of proteins. Our findings illustrate the utility of different modeling scales for accessing TDP-43 molecular-level interactions and suggest that TDP-43 has numerous interaction preferences or patterns, exhibiting an overall strong, but dynamic, association and driving the formation of biomolecular condensates.

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.

Effect of Scaling the Electrostatic Interactions on the Free Energy of Transfer of Azurin from Water to Lipid Membrane Determined by Coarse-Grained Simulations

Azurin protein potentially plays an important role as an anti-cancer therapeutic agent, particularly in treating breast cancer in experiments and showing without having a negative effect on normal cells. Although the interaction mechanism between protein and lipid membrane is complicated, it can be modeled as protein-lipid interaction. Since the all-atom (AA) model simulation is cost computing, we apply a coarse-grained (CG-MARTINI) model to calculate the protein-lipid interaction. We investigate the binding free energy value dependency by varying the windows separation and electrostatic scale parameters. After scaling the electrostatic interactions by a factor of 0.04, the best result in terms of free energy is -140.831 kcal/mol, while after window-separation optimization, it reaches -71.859 kcal/mol. This scaling was necessary because the structures from the CG MARTINI model have a higher density than the corresponding all-atom structures. We thus postulate that electrostatic interactions should be scaled down in this case of CG-MARTINI simulations.

Reactive Martini: Chemical Reactions in Coarse-Grained Molecular Dynamics Simulations.

Chemical reactions are ubiquitous in both materials and the biophysical sciences. While coarse-grained (CG) molecular dynamics simulations are often needed to study the spatiotemporal scales present in these fields, chemical reactivity has not been explored thoroughly in CG models. In this work, a new approach to model chemical reactivity is presented for the widely used Martini CG Martini model. Employing tabulated potentials with a single extra particle for the angle dependence, the model provides a generic framework for capturing bonded topology changes using nonbonded interactions. As a first example application, the reactive model is used to study the macrocycle formation of benzene-1,3-dithiol molecules through the formation of disulfide bonds. We show that starting from monomers, macrocycles with sizes in agreement with experimental results are obtained using reactive Martini. Overall, our reactive Martini framework is general and can be easily extended to other systems. All of the required scripts and tutorials to explain its use are provided online.

Mesoscale computer modeling of asphaltene aggregation in liquid paraffin.

Asphaltenes represent a novel class of carbon nanofillers that are of potential interest for many applications, including polymer nanocomposites, solar cells, and domestic heat storage devices. In this work, we developed a realistic coarse-grained Martini model that was refined against the thermodynamic data extracted from atomistic simulations. This allowed us to explore the aggregation behavior of thousands of asphaltene molecules in liquid paraffin on a microsecond time scale. Our computational findings show that native asphaltenes with aliphatic side groups form small clusters that are uniformly distributed in paraffin. The chemical modification of asphaltenes via cutting off their aliphatic periphery changes their aggregation behavior: modified asphaltenes form extended stacks whose size increases with asphaltene concentration. At a certain large concentration (44 mol. %), the stacks of modified asphaltenes partly overlap, leading to the formation of large, disordered super-aggregates. Importantly, the size of such super-aggregates increases with the simulation box due to phase separation in the paraffin-asphaltene system. The mobility of native asphaltenes is systematically lower than that of their modified counterparts since the aliphatic side groups mix with paraffin chains, slowing down the diffusion of native asphaltenes. We also show that diffusion coefficients of asphaltenes are not very sensitive to the system size: enlarging the simulation box results in some increase in diffusion coefficients, with the effect being less pronounced at high asphaltene concentrations. Overall, our findings provide valuable insight into the aggregation behavior of asphaltenes on spatial and time scales that are normally beyond the scales accessible for atomistic simulations.

Aggregation and partitioning of amyloid peptide fragments in the presence of a lipid bilayer: A coarse grained molecular dynamics study

Amyloidogenicity and toxicity of amyloid peptides have been linked to the peptide aggregation and interactions with lipid bilayers. In this work we used the coarse grained MARTINI model to study the aggregation and partitioning of amyloid peptide fragments Aβ(1–28) and Aβ(25–35) in the presence of a dipalmitoylphosphatidylcholine bilayer. We explored the peptide aggregation starting from three initial spatial arrangements where free monomers were placed in solution outside the membrane, at the membrane-solution interface, or in the membrane. We found that Aβ(1–28) and Aβ(25–35) interact with the bilayer quite differently. The Aβ(1–28) fragments show strong peptide-peptide and peptide-lipid interactions leading to irreversible aggregation where the aggregates stay confined to their initial spatial location. The Aβ(25–35) fragments show weaker peptide-peptide and peptide-lipid interaction leading to reversible aggregation and accumulation at the membrane-solution interface irrespective of their initial spatial arrangement. Those findings can be explained in terms of the shape of the potential of mean force for the single-peptide translocation across the membrane.

Automatic Optimization of Lipid Models in the Martini Force Field Using SwarmCG.

After two decades of continued development of the Martini coarse-grained force field (CG FF), further refinment of the already rather accurate Martini lipid models has become a demanding task that could benefit from integrative data-driven methods. Automatic approaches are increasingly used in the development of accurate molecular models, but they typically make use of specifically designed interaction potentials that transfer poorly to molecular systems or conditions different than those used for model calibration. As a proof of concept, here, we employ SwarmCG, an automatic multiobjective optimization approach facilitating the development of lipid force fields, to refine specifically the bonded interaction parameters in building blocks of lipid models within the framework of the general Martini CG FF. As targets of the optimization procedure, we employ both experimental observables (top-down references: area per lipid and bilayer thickness) and all-atom molecular dynamics simulations (bottom-up reference), which respectively inform on the supra-molecular structure of the lipid bilayer systems and on their submolecular dynamics. In our training sets, we simulate at different temperatures in the liquid and gel phases up to 11 homogeneous lamellar bilayers composed of phosphatidylcholine lipids spanning various tail lengths and degrees of (un)saturation. We explore different CG representations of the molecules and evaluate improvements a posteriori using additional simulation temperatures and a portion of the phase diagram of a DOPC/DPPC mixture. Successfully optimizing up to ∼80 model parameters within still limited computational budgets, we show that this protocol allows the obtainment of improved transferable Martini lipid models. In particular, the results of this study demonstrate how a fine-tuning of the representation and parameters of the models may improve their accuracy and how automatic approaches, such as SwarmCG, may be very useful to this end.

Effect of Force Field Resolution on Membrane Mechanical Response and Mechanoporation Damage under Deformation Simulations.

Damage induced by transient disruption and mechanoporation in an intact cell membrane is a vital nanoscale biomechanical mechanism that critically affects cell viability. To complement experimental studies of mechanical membrane damage and disruption, molecular dynamics (MD) simulations have been performed at different force field resolutions, each of which follows different parameterization strategies and thus may influence the properties and dynamics of membrane systems. Therefore, the current study performed tensile deformation MD simulations of bilayer membranes using all-atom (AA), united-atom (UA), and coarse-grained Martini (CG-M) models to investigate how the damage biomechanics differs across atomistic and coarse-grained (CG) simulations. The mechanical response and mechanoporation damage were qualitatively similar but quantitatively different in the three models, including some progressive changes based on the coarse-graining level. The membranes yielded and reached ultimate strength at similar strains; however, the coarser systems exhibited lower average yield stresses and failure strains. The average failure strain in the UA model was approximately 7% lower than the AA, and the CG-M was 20% lower than UA and 27% lower than AA. The CG systems also nucleated a higher number of pores and larger pores, which resulted in higher damage during the deformation process. Overall, the study provides insight on the impact of force field-a critical factor in modeling biomolecular systems and their interactions-in inspecting membrane mechanosensitive responses and serves as a reference for justifying the appropriate force field for future studies of more complex membranes and more diverse biomolecular assemblies.