Martini 3 Coarse‐Grained Force Field: Small Molecules

An integrated multiscale computational framework deciphers SARS-CoV-2 resistance to sotrovimab

The emergence of resistance mutations in the SARS-CoV-2 spike (S) protein presents a challenge for monoclonal antibody treatments like sotrovimab. Understanding the structural, dynamic, and molecular features of these mutations is essential for therapeutic advancements. However, the intricate landscape of potential mutations and critical residues conferring resistance to mAbs like sotrovimab remains elusive. This study introduces an integrated framework that combines interface protein design, machine learning, hybrid quantum mechanics/molecular mechanics methodologies, all-atom and coarse-grained molecular dynamics simulations, and correlation analysis. Beginning with the interface-based design and analysis, this framework elucidates the interaction between sotrovimab and the S-protein, identifying pivotal residues and plausible resistance mutations. Machine learning algorithms then facilitate the identification of potential resistance mutations using structural-sequence-binding affinity-energetics features. The hybrid quantum mechanics/molecular mechanics approach subsequently evaluates the role of mutational residues as quantum regions, assessing their impact on stabilizing the macromolecular complex. To gain a deeper understanding of the dynamic behavior of these mutations, multiscale simulations comprising all-atom and coarse-grained molecular dynamics simulations were performed, revealing their structural, biophysical and energetic impacts. These simulations complemented the static predictions, capturing the conformational dynamics and stability of the mutants in presence of glycan in the S-protein. The accuracy of the predictions is validated by correlating identified resistance mutations with clinical-sequencing data and empirical evidence from sotrovimab-treated patients. Notably, two residues, E340 at the S-protein-sotrovimab interface and Y508 distal from it, and their designs, align with clinically observed resistance mutations. Furthermore, machine learning approaches predict novel S-protein sequences with enhanced/reduced affinity for sotrovimab, validated structurally using AlphaFold. This integrated framework showcases its effectiveness in identifying potential resistance mutations, corroborated with clinical insights and offering a multidimensional strategy for decoding resistance mutations in SARS-CoV-2. Its translational relevance extends to understanding resistance mechanisms and designing novel antibody therapeutics in other systems.

Temperature-Dependent Coarse-Grained Model for Simulations of Intrinsically Disordered Protein LCST and UCST Liquid-Liquid Phase Separations

Many intrinsically disordered proteins (IDPs) can undergo a liquid-liquid phase separation (LLPS) in water, depending on solution conditions (temperature, pH, and ionic strength). There are two types of LLPS that are controlled by temperature: those occurring above a lower critical solution temperature (LCST) and those occurring below an upper critical solution temperature (UCST). IDP coarse-grained (CG) models are particularly appropriate for investigating the physical and chemical factors that govern their LLPS and supramolecular organization. However, the development of CG models allowing simulations of both LCST and UCST behavior of temperature-sensitive IDPs is still in its infancy. In this context, we present here a novel temperature-dependent (TD) CG model for IDP simulations based on the MARTINI 3 force field. The model was developed by modifying the Lennard-Jones potentials between apolar or charged solute beads and water with a TD rescaling factor. It was parametrized to fit the TD potentials of mean force (PMF) between two apolar or two charged molecules computed using all-atom (AA) simulations. We show that the TD CG model is able to reproduce the experimentally known LLPS of both LCST and UCST low-complexity sequences and to estimate phase transition temperatures comparable to experimental measurements.

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.

Hydrophobic Ion Pairing of Polymyxin B with Oleic Acid: A Dissipative Particle Dynamics Simulation Study

Background: Hydrophobic ion pairing is a technique for reducing the hydrophilicity of charged molecules (drugs) by pairing them with oppositely charged hydrophobic counterions. This method is used to control the solubility of charged molecules in a solvent and is of particular importance in drug delivery. Methods: Dissipative particle dynamics simulations were performed to provide a microscopic understanding of hydrophobic ion pairing in polymyxin B (PMB) and oleate (OA) ions. Solvents and ions were explicitly included in the simulations. Results: We investigated the effects of relative concentrations of PMB and OA (the charge ratio), solvent philicity, and the concentrations of PMB and OA at a fixed composition on the structural stability and the hydrophobicity of the ion paired cluster, as well as the kinetics of assembly. The maximum hydrophobicity belongs to PMB:OA charge ratio 1:1. The clustering efficiency in mixed ethanol-water solutions decreases with the increasing ethanol content of water. The dynamics of PMB/OA exchange between hydrophobic cluster and the surrounding solution reveal two distinct relaxation processes, whose relaxation times differ by two orders of magnitude. Conclusions: The hydrophobicity of the cluster is controlled by the charge ratio. The core of the ion paired cluster acts as the primary barrier and its surface layer acts as the secondary barrier against alcohol permeation into it. The exchange of surface PMB/OA ions with the surrounding is a much faster dynamic process than the establishment of equilibrium between the PMB/OA ions in the cluster and the solution. The time scale for the slower process provides useful information on the rate of drug release from the hydrophobic ion paired complex.

CGsmiles: A Versatile Line Notation for Molecular Representations across Multiple Resolutions

Coarse-grained (CG) models simplify molecular representations by grouping multiple atoms into effective particles, enabling faster simulations and reducing the chemical compound space compared to atomistic methods. Additionally, models with chemical specificity, such as Martini, may extrapolate to cases where experimental data is scarce, making CG methods highly promising for high-throughput (HT) screenings and chemical space exploration. Yet no rigorous data formats exist for the crucial aspect of describing how the atoms are grouped (i.e., the mapping). As CG models advance toward true HT capabilities, the lack of mappings and indexing capabilities for the growing number of CG molecules poses a significant barrier. To address this, we introduce CGsmiles, a versatile line notation inspired by the popular Simplified Molecular Input Line Entry System (SMILES) and BigSMILES. CGsmiles encodes the molecular graph and particle (atom) properties independent of their resolution and incorporates a framework that allows seamless conversion between coarse- and fine-grained resolutions. By specifying fragments that describe how each particle is represented at the next finer resolution (e.g., CG particles to atoms), CGsmiles can represent multiple resolutions and their hierarchical relationships in a single string. In this paper, we present the CGSmiles syntax and analyze a benchmark set of 407 molecules from the Martini force field. We highlight key features missing in existing notations that are essential for accurately describing CG models. To demonstrate the utility of CGsmiles beyond simulations, we construct two simple machine-learning models for predicting partition coefficients, both trained on CGsmiles-indexed data and leveraging information from both CG and atomistic resolutions. Finally, we briefly discuss the applicability of CGsmiles to polymers, which particularly benefit from the multiresolution nature of the notation.

Can Coarse-Grained Molecular Dynamics Simulations Predict Pharmaceutical Crystal Growth?

To investigate the ability of coarse-grained molecular dynamics simulations to predict the relative growth rates of crystal facets of pharmaceutical molecules, we apply two coarse-graining strategies to two drug molecules, phenytoin and carbamazepine. In the first method, we map an atomistic model to a MARTINI-level coarse-grained (CG) force field that uses 2 or 3 heavy atoms per bead. This is followed by applying Particle Swarm Optimization (PSO), a global optimum searching algorithm, to the CG Lennard-Jones intermolecular potentials to fit the radial distribution functions of both the crystalline and melt structures. In the second, a coarser-grained method, we map 5 or more heavy atoms into one bead with the help of the Iterative Boltzmann Inversion (IBI) method to derive a tabulated longer-range force field (FF). Simulations using the FF's derived from both strategies were able to stabilize the crystal in the correct structure and to predict crystal growth from the melt with modest computational resources. We evaluate the advantages and limitations of both methods and compare the relative growth rates of various facets of both drug crystals with those predicted by the Bravais-Friedel-Donnay-Harker (BFDH) and attachment energy (AE) theories. While all methods, except for the simulations conducted with the coarser-grained IBI-generated model, produced similarly good results for phenytoin, the finer-grained PSO-generated FF using MARTINI mapping rules outperformed the other methods in its prediction of the facet growth rates and resulting crystalline morphology for carbamazepine.

Charge regulated pH/NIR dual responsive nanoplatforms centered on cuproptosis for enhanced cancer theranostics

Multifunctional nanoplatforms capable of simultaneously executing multimodal therapy and imaging functions are of great potentials for cancer theranostics. We present an elegantly designed, easy-to-fabricate poly(acrylic acid)/mesoporous calcium phosphate/mesoporous copper phosphate nanosphere (PAA/mCaP/mCuP NS) with outstanding pH/NIR-sensitive multimodal-synergic anti-tumor effects. Optimal porous PAA NS scaffolds were prepared at room temperature by balancing the intra-PAA polymer and polymer-solvents Lennard-Jones potentials in a water:isopropyl alcohol (IPA) mix-solvent. Subsequent sponging of Ca2+ and Cu2+, and adsorption of PO43- to the PAA template were achieved through exquisite electrostatic interactions among ions and the ionizable PAA side-chain in an aqueous environment. This forms the basis for the tumor microenvironment pH-triggered release of Cu2+ to induce cuproptosis, as well as the photothermal effect originating from CuP, while Ca2+ can enhance the nanoplatform's biocompatibility and can damage mitochondria when overloaded. Lastly, PAA/mCaP/mCuP NSs still exhibit high drug loading efficiency for doxorubicin (DOX), enabling chemotherapy. Satisfactory anti-tumor effects of these modalities, along with their synergistic effects, were verified both in vitro and in vivo, with the NSs demonstrating good biodegradation in the latter. The fabricated NS itself holds great promise as an anti-tumor nanomedicine, and the thorough mechanical insights into NS formation may inspire the design of next-generation multifunctional nanoplatforms.

Benchmark of Coacervate Formation and Mechanism Exploration Using the Martini Force Field

Peptide-based coacervates are crucial for drug delivery due to their biocompatibility, versatility, high drug loading capacity, and cell penetration rates; however, their stability mechanism and phase behavior are not fully understood. Additionally, although Martini is one of the most famous force fields capable of describing coacervate formation with molecular details, a comprehensive benchmark of its accuracy has not been conducted. This research utilized the Martini 3.0 force field and machine learning algorithms to explore representative peptide-based coacervates, including those composed of polyaspartate (PAsp)/polyarginine (PArg), rmfp-1, sticker-and-spacer small molecules, and HBpep molecules. We identified key coacervate formation driving forces such as Coulomb, cation-π, and π-π interactions and established three criteria for determining coacervate formation in simulations. The results also indicate that while Martini 3.0 accurately captures coacervate formation trends, it tends to underestimate Coulomb interactions and overestimate π-π interactions. What is more, our study on drug encapsulation of HBpep and its derivative coacervates suggested that most loaded drugs were distributed on surfaces of HBpep clusters, awaiting experimental validation. This study employs simulation to enhance understanding of peptide-based coacervate phase behavior and stability mechanisms while also benchmarking Martini 3.0, thereby providing fundamental insights for future experimental and simulation investigations.

Toward the Evolutionary Optimisation of Small Molecules Within Coarse-Grained Simulations: Training Molecules to Hide Behind Lipid Head Groups

Exploring the vast chemical space of small molecules poses a significant challenge. We develop a new strategy to efficiently explore this space using coarse-grained toy-like molecules utilizing the Martini3 force field and graph representations. This yields initial proof-of-concept results for the approach enabling the identification of optimal molecules with specific properties targeting lipid bilayers. By leveraging genetic algorithms and coarse-grained molecular dynamics simulations, we demonstrate the potential of our method in designing simple, linear molecules. Our findings show a good convergence toward molecules with weak amphiphilic properties, resembling known (general anesthetic) molecules. While this study demonstrates the feasibility of our method, further refinement is needed to fully realize its potential and explore more complex molecular topologies. Nevertheless, these encouraging results suggest a new path for future research in small molecule discovery and design without relying on extensive data sets.

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.

Development of a Multifunctional Antimicrobial Peptide for Marine Antifouling by Theoretical Calculations and Experimental Approaches

Marine biofouling poses significant challenges to the economy, safety, and reliability of marine infrastructure. While antimicrobial peptides (AMPs) have emerged as green and efficient antifouling agents, their single functionality and the complexity of preparing antifouling surfaces remain key challenges. This study introduces a multifunctional AMP with combined adhesion and antimicrobial properties, derived from 3,4-dihydroxy-l-phenylalanine (DOPA) and IP12 (sequence IRLRWRWKWPWP). The directional recombination of AMP was guided by theoretical calculations. Density functional theory (DFT) simulations identify that the hydroxyl groups of DOPA were the main activating groups that react with aluminum alloy. Coarse-grained molecular dynamics (CG MD) and all-atom molecular dynamics (AA MD) simulations revealed that amino acid residues near the N-terminal of the IP12 could induce cell membrane bending and rupture. The AMP surfaces were fabricated to validate the accurate calibration of the simulations and performance of multifunctional AMP. Atomic force microscopy, Fourier transform infrared, and X-ray photoelectron spectroscopy results confirm the successful construction of AMP surfaces through adhesion function. Antifouling evaluations demonstrated the antifouling properties of AMP surfaces against Escherichia coli (Gram-negative) and Bacillus sp. (Gram-positive), achieving antifouling rates of 85.8 and 82.4%, respectively. This study provides valuable insights into the design of multifunctional AMPs.

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.

Evaluation of All-Atom and Martini 3 Coarse-Grained Force Fields from the Structural Investigation of Nitroxide Spin Probes and Their Confinement in Beta-Cyclodextrin

Nitroxide radicals have found wide applications as spin labels or probes, and their guest-host interactions with cyclodextrins exhibit enhanced applications in electron spin resonance (ESR) spectroscopy and imaging due to improved biostability toward reducing agents. Although the computational prediction of the guest-host binding has become increasingly common for small ligands, molecular simulations regarding the conformational preferences of hosted spin probes have not been conducted. Here we present molecular dynamics simulations at an atomistic level for a set of four TEMPO (2,2,6,6-tetramethylpiperidine 1-oxyl) spin probes and thereafter develop coarse-grained models compatible with the recent version of the Martini force field (v 3.0) to tackle their encapsulation in the cavity of β-cyclodextrin (βCD) for which experimental ESR data are available. The results indicate that the atomistic descriptions perform well in relation to the structural parameters derived from X-ray diffraction as well as hydrogen bonding and hydrogen patterns and predict that the guest-host complexation is hydrophobically driven by the presence of a methyl group pair of the spin probe at the cavity center of βCD. The spin probe mobility at the binding site reveals the nitroxide group orientation toward the secondary rim of the cyclodextrin and the alternating presence of the two methyl group pairs inside the cavity, features in agreement with the experimental behavior of the ESR parameters. The coarse-grained parameterizations of TEMPO probes and βCD rely on optimizing the bonded and nonbonded parameters with references to the atomistic simulation results, and they are capable of recovering the orientation and location of the spin probe inside the cyclodextrin cavity predicted by the atomistic guest-host complexes. The results suggest the cyclodextrin host-guest system as a powerful validation suite to evaluate new coarse-grained parameterizations of small ligands and future extensions to functionalized cyclodextrins in inclusion complexes.

Chemically Transferable Electronic Coarse Graining for Polythiophenes

Recent advances in machine-learning-based electronic coarse graining (ECG) methods have demonstrated the potential to enable electronic predictions in soft materials at mesoscopic length scales. However, previous ECG models have yet to confront the issue of chemical transferability. In this study, we develop chemically transferable ECG models for polythiophenes using graph neural networks. Our models are trained on a data set that samples over the conformational space of random polythiophene sequences generated with 15 different monomer chemistries and three different degrees of polymerization. We systematically explore the impact of coarse-grained representation on ECG accuracy, highlighting the significance of preserving the C-β coordinates in thiophene. We also find that integrating unique polymer sequences into training enhances the model performance more efficiently than augmenting conformational sampling for sequences already in the training data set. Moreover, our ECG models, developed initially for one property and one level of quantum chemical theory, can be efficiently transferred to related properties and higher levels of theory with minimal additional data. The chemically transferable ECG model introduced in this work will serve as a foundation model for new classes of chemically transferable ECG predictions across chemical space.

A millisecond coarse-grained simulation approach to decipher allosteric cannabinoid binding at the glycine receptor α1

Glycine receptors (GlyR) are regulated by small-molecule binding at several allosteric sites. Cannabinoids like tetrahydrocannabinol (THC) and N-arachidonyl-ethanol-amide (AEA) potentiate the GlyR response but their mechanism of action is not fully established. By combining millisecond coarse-grained (CG) MD simulations powered by Martini 3 with backmapping to all-atom representations, we have characterized the cannabinoid-binding site(s) at the zebrafish GlyR-α1 active state with atomic resolution. Based on hundreds of thousand ligand-binding events, we find that cannabinoids bind to the transmembrane domain of the receptor at both intrasubunit and intersubunit sites. For THC, the intrasubunit binding mode predicted in simulation is in excellent agreement with recent cryo-EM structures, while intersubunit binding recapitulates in full previous mutagenesis experiments. Intriguingly, AEA is predicted to bind at the same intersubunit site despite the strikingly different chemistry. Statistical analyses of the ligand-receptor interactions highlight potentially relevant residues for GlyR potentiation, offering experimentally testable predictions. The predictions for AEA have been validated by electrophysiology recordings of rationally designed mutants. The results highlight the existence of multiple cannabinoid-binding sites for the allosteric regulation of GlyR and put forward an effective strategy for the identification and structural characterization of allosteric binding sites.

Coarse-Graining the Recognition of a Glycolipid by the C-Type Lectin Mincle Receptor

Macrophage inducible Ca2+-dependent lectin (Mincle) receptor recognizes Mycobacterium tuberculosis glycolipids to trigger an immune response. This host membrane receptor is thus a key player in the modulation of the immune response to infection by M. tuberculosis and has emerged as a promising target for the development of new vaccines against tuberculosis. The recent development of the Martini 3 force field for coarse-grained (CG) molecular modeling allows the study of interactions of soluble proteins with small ligands which was not typically modeled well with the previous Martini 2 model. Here, we present a refined approach detailing a protocol for modeling interactions between a glycolipid and its receptor at a CG level using the Martini 3 force field. Using this approach, we studied Mincle and identified critical parameters governing ligand recognition, such as loop flexibility and the regulation of hydrophobic groove formation by calcium ions. In addition, we assessed ligand affinity using free energy perturbation calculations. Our results offer mechanistic insight into the interactions between Mincle and glycolipids, providing a basis for the rational design of molecules targeting this type of membrane receptors.

Perpendicular crossing chains enable high mobility in a noncrystalline conjugated polymer

The nature of interchain π-system contacts, and their relationship to hole transport, are elucidated for the high-mobility, noncrystalline conjugated polymer C16-IDTBT by the application of scanning tunneling microscopy, molecular dynamics, and quantum chemical calculations. The microstructure is shown to favor an unusual packing motif in which paired chains cross-over one another at near-perpendicular angles. By linking to mesoscale microstructural features, revealed by coarse-grained molecular dynamics and previous studies, and performing simulations of charge transport, it is demonstrated that the high mobility of C16-IDTBT can be explained by the promotion of a highly interconnected transport network, stemming from the adoption of perpendicular contacts at the nanoscale, in combination with fast intrachain transport.

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.

Coarse-Grained Simulations of Columnar Ionic Liquid Crystals: Comparison with Experiments

We simulate a homologous series of guanidinium-based columnar ionic liquid crystals (ILCs) using coarse-grained molecular dynamics (MD) simulations with the Martini force field. We systematically vary the length of alkyl side chains, ILC-n (n = 8, 12, 16), and compare our results with previous experimental findings. Experimentally, ILC-8 exhibits a narrow mesophase window and weak columnar order, while ILC-12 and ILC-16 display a broad mesophase window and high columnar order. The MD simulations show that ILC-8 forms a percolated structure, whereas the longer chain analogues self-assemble into columns, with columnar assembly becoming more prominent as the side chain length increases, in qualitative agreement with the experiments. Furthermore, the intercolumnar distance increases monotonically with increasing side chain length and decreases with increasing temperature. Finally, we find that the diffusion coefficient and ionic conductivity decrease substantially with increasing chain length, consistent with experimental observations. We attribute this decrease in mobility to the formation of hexagonally ordered columns, which restrict transport more than percolated networks.

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.

Martini 3 Coarse-Grained Model for the Cofactors Involved in Photosynthesis

As a critical step in advancing the simulation of photosynthetic complexes, we present the Martini 3 coarse-grained (CG) models of key cofactors associated with light harvesting (LHCII) proteins and the photosystem II (PSII) core complex. Our work focuses on the parametrization of beta-carotene, plastoquinone/quinol, violaxanthin, lutein, neoxanthin, chlorophyll A, chlorophyll B, and heme. We derived the CG parameters to match the all-atom reference simulations, while structural and thermodynamic properties of the cofactors were compared to experimental values when available. To further assess the reliability of the parameterization, we tested the behavior of these cofactors within their physiological environments, specifically in a lipid bilayer and bound to photosynthetic complexes. The results demonstrate that our CG models maintain the essential features required for realistic simulations. This work lays the groundwork for detailed simulations of the PSII-LHCII super-complex, providing a robust parameter set for future studies.

Bartender: Martini 3 Bonded Terms via Quantum Mechanics-Based Molecular Dynamics

Coarse-grained (CG) molecular dynamics (MD) simulations have grown in applicability over the years. The recently released version of the Martini CG force field (Martini 3) has been successfully applied to simulate many processes, including protein-ligand binding. However, the current ligand parametrization scheme is manual and requires an a priori reference all-atom (AA) simulation for benchmarking. For systems with suboptimal AA parameters, which are often unknown, this translates into a CG model that does not reproduce the true dynamical behavior of the underlying molecule. Here, we present Bartender, a quantum mechanics (QM)/MD-based parametrization tool written in Go. Bartender harnesses the power of QM simulations and produces reasonable bonded terms for Martini 3 CG models of small molecules in an efficient and user-friendly manner. For small, ring-like molecules, Bartender generates models whose properties are indistinguishable from the human-made models. For more complex, drug-like ligands, it is able to fit functional forms beyond simple harmonic dihedrals and thus better captures their dynamical behavior. Bartender has the power to both increase the efficiency and the accuracy of Martini 3-based high-throughput applications by producing numerically stable and physically realistic CG models.

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.

Microscopically segregated ligand distribution in co-assembled peptide-amphiphile nanofibers

Peptide amphiphiles (PAs) self-assemble into cylindrical nanofibers with applications in protein purification, tissue engineering, and regenerative medicine. For these applications, functionalized PAs are often co-assembled with oppositely charged filler PAs. Finding the conditions at which these fibers are homogeneously mixed or segregated is crucial for the required application. We co-assemble negative C12VVEE fillers and positive C12VVKK-OEG4-Z33 ligands, which are important for antibody purifications. Our results show that the ligands tend to cluster and locally segregate in the fiber surfaces. The Z33s are overall neutral and form large aggregates in bulk solution due to short range attractions. However, full segregation of the C12VVKK-OEG4-Z33 is not observed in the cylindrical surface due to the electrostatic penalty of forming large domains of similarly charged molecules. This is commensurate with previous theoretical predictions, showing that the competition between short-range attractive interactions and long-range electrostatic repulsions leads to pattern formation in cylindrical surfaces. This work offers valuable insight into the design of functionalized nanofibers for various biomedical and chemical applications.

Molecular modeling of the carbohydrate corona formation on a polyvinyl chloride nanoparticle and its impact on the adhesion to lipid bilayers

The pervasive presence of nanoplastics (NPs) in the environment has gained increasing attention due to their accumulation in living organisms. These emerging contaminants inevitably interact with extracellular polymeric substances along respiratory or gastrointestinal tracts, and diverse organic coating on the surface of NPs, known as bio- or eco-corona, is formed. Although its impact on altering the NP properties and potential cell internalization has been extensively examined, studies on its role in NP partitioning in the cell membrane are elusive yet. In this work, molecular dynamics is used to investigate the formation of chitosan (CT) corona centered on a polyvinyl chloride (PVC) nanoparticle and the uptake of the resulting complex onto lipid membranes. Coarse-grained models compatible with the newly developed Martini 3.0 force field are implemented for the two polymers employing the atomistic properties as targets in the parameterization. The reliability of the coarse-grained polymer models is demonstrated by reproducing the structural properties of the PVC melt and of solvated CT strands, as well as by determining the conformation adopted by the latter at the NP surface. Results show that the spontaneous binding of CT chains of high and intermediate protonation degrees led to the formation of soft and hard corona that modulates the interaction of PVC core with model membranes. The structural changes of the corona adsorbed at the lipid-water interface enable a subsequent transfer of the NP to the center of the saturated lipid membranes and a complete or partial transition to a snorkel conformation depending on the hydrophilic/hydrophobic balance in the CT-PVC complex. Overall, the computational investigation of the coarse-grained model system provides implications for understanding how the eco-corona development influences the uptake and implicit toxicology of NPs.

Exploring Free Energies of Specific Protein Conformations Using the Martini Force Field

Coarse-grained (CG) level molecular dynamics simulations are routinely used to study various biomolecular processes. The Martini force field is currently the most widely adopted parameter set for such simulations. The functional form of this and several other CG force fields enforces secondary protein structure support by employing a variety of harmonic potentials or restraints that favor the protein's native conformation. We propose a straightforward method to calculate the energetic consequences of transitions between predefined conformational states in systems in which multiple factors can affect protein conformational equilibria. This method is designed for use within the Martini force field and involves imposing conformational transitions by linking a Martini-inherent elastic network to the coupling parameter λ. We demonstrate the applicability of our method using the example of five biomolecular systems that undergo experimentally characterized conformational transitions between well-defined structures (Staphylococcal nuclease, C-terminal segment of surfactant protein B, LAH4 peptide, and β2-adrenergic receptor) as well as between folded and unfolded states (GCN4 leucine zipper protein). The results show that the relative free energy changes associated with protein conformational transitions, which are affected by various factors, such as pH, mutations, solvent, and lipid membrane composition, are correctly reproduced. The proposed method may be a valuable tool for understanding how different conditions and modifications affect conformational equilibria in proteins.

Mesoporous nanoperforators as membranolytic agents via nano- and molecular-scale multi-patterning

Plasma membrane lysis is an effective anticancer strategy, which mostly relying on soluble molecular membranolytic agents. However, nanomaterial-based membranolytic agents has been largely unexplored. Herein, we introduce a mesoporous membranolytic nanoperforators (MLNPs) via a nano- and molecular-scale multi-patterning strategy, featuring a spiky surface topography (nanoscale patterning) and molecular-level periodicity in the spikes with a benzene-bridged organosilica composition (molecular-scale patterning), which cooperatively endow an intrinsic membranolytic activity. Computational modelling reveals a nanospike-mediated multivalent perforation behaviour, i.e., multiple spikes induce nonlinearly enlarged membrane pores compared to a single spike, and that benzene groups aligned parallelly to a phospholipid molecule show considerably higher binding energy than other alignments, underpinning the importance of molecular ordering in phospholipid extraction for membranolysis. Finally, the antitumour activity of MLNPs is demonstrated in female Balb/c mouse models. This work demonstrates assembly of organosilica based bioactive nanostructures, enabling new understandings on nano-/molecular patterns co-governed nano-bio interaction.

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.

Martini on the Rocks: Can a Coarse-Grained Force Field Model Crystals?

Computational chemistry is an important tool in numerous scientific disciplines, including drug discovery and structural biology. Coarse-grained models offer simple representations of molecular systems that enable simulations of large-scale systems. Because there has been an increase in the adoption of such models for simulations of biomolecular systems, critical evaluation is warranted. Here, the stability of the amyloid peptide and organic crystals is evaluated using the Martini 3 coarse-grained force field. The crystals change shape drastically during the simulations. Radial distribution functions show that the distance between backbone beads in β-sheets increases by ∼1 Å, breaking the crystals. The melting points of organic compounds are much too low in the Martini force field. This suggests that Martini 3 lacks the specific interactions needed to accurately simulate peptides or organic crystals without imposing artificial restraints. The problems may be exacerbated by the use of the 12-6 potential, suggesting that a softer potential could improve this model for crystal simulations.

Assessment of the MARTINI 3 Performance for Short Peptide Self-Assembly

The coarse-grained MARTINI force field, initially developed for membranes, has proven to be an exceptional tool for investigating supramolecular peptide assemblies. Over the years, the force field underwent refinements to enhance accuracy, enabling, for example, the reproduction of protein-ligand interactions and constant pH behavior. However, these protein-focused improvements seem to have compromised its ability to model short peptide self-assembly. In this study, we assess the performance of MARTINI 3 in reproducing peptide self-assembly using the well-established diphenylalanine (FF) as our test case. Unlike its success in version 2.1, FF does not even exhibit aggregation in version 3. By systematically exploring parameters for the aromatic side chains and charged backbone beads, we established a parameter set that effectively reproduces tube formation. Remarkably, these parameter adjustments also replicate the self-assembly of other di- and tripeptides and coassemblies. Furthermore, our analysis uncovers pivotal insights for enhancing the performance of MARTINI in modeling short peptide self-assembly. Specifically, we identify issues stemming from overestimated hydrophilicity arising from charged termini and disruptions in π-stacking interactions due to insufficient planarity in aromatic groups and a discrepancy in intermolecular distances between this and backbone-backbone interactions. This investigation demonstrates that strategic modifications can harness the advancements offered by MARTINI 3 for the realm of short peptide self-assembly.

Polyfluoroalkyl Chain-Based Assemblies for Biomimetic Catalysis

Amphiphobic fluoroalkyl chains are exploited for creating robust and diverse self-assembled biomimetic catalysts. Long terminal perfluoroalkyl chains (Cn F2n+1 with n=6, 8, and 10) linked with a short perhydroalkyl chains (Cm H2m with m=2 and 3) were used to synthesize several 1,4,7-triazacyclononane (TACN) derivatives, Cn F2n+1 -Cm H2m -TACN. In the presence of an equimolar amount of Zn2+ ions that coordinate the TACN moiety and drive the self-assembly into micelle-like aggregates, the critical aggregation concentration of polyfluorinated Cn F2n+1 -Cm H2m -TACN⋅Zn2+ was lowered by ∼1 order of magnitude compared to the traditional perhyroalkyl counterpart with identical carbon number of alkyl chain. When 2'-hydroxypropyl-4-nitrophenyl phosphate was used as the model phosphate substrate, polyfluorinated Cn F2n+1 -Cm H2m -TACN⋅Zn2+ assemblies showed higher affinity and catalytic activity, compared to its perhyroalkyl chain-based counterpart. Coarse-grained molecular dynamic simulations have been introduced to explore the supramolecular assembly of polyfluoroalkyl chains in the presence of Zn2+ ions and to better understand their enhanced catalytic activity.

Molecular Modeling and Simulation of the Mycobacterial Cell Envelope: From Individual Components to Cell Envelope Assemblies

Unlike typical Gram-positive bacteria, the cell envelope of mycobacteria is unique and composed of a mycobacterial outer membrane, also known as the mycomembrane, a peptidoglycan layer, and a mycobacterial inner membrane, which is analogous to that of Gram-negative bacteria. Despite its importance, however, our understanding of this complex cell envelope is rudimentary at best. Thus, molecular modeling and simulation of such an envelope can benefit the scientific community by proposing new hypotheses about the biophysical properties of its different layers. In this Perspective, we present recent advances in molecular modeling and simulation of the mycobacterial cell envelope from individual components to cell envelope assemblies. We also show how modeling other types of cell envelopes, such as that of Escherichia coli, may help modeling part of the mycobacterial envelopes. We hope that the studies presented here are just the beginning of the road and more and more new modeling and simulation studies help us to understand crucial questions related to mycobacteria such as antibiotic resistance or bacterial survival in the host.

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.

Kinetic Mechanism of Surfactant-Based Molecular Recognition: Selective Permeability across an Oil-Water Interface Regulated by Supramolecular Aggregates

Lipids are known to play a vital role in the molecular organization of all cellular life. Molecular recognition is another fundamental biological process that is generally attributed to biological polymers, such as proteins and nucleic acids. However, there is evidence that aggregates of lipids and lipid-like molecules are also capable of selectively binding to or regulating the partitioning of other molecules. We previously demonstrated that a model two-phase octanol/water system can selectively partition Red 40 and Blue 1 dyes added to an aqueous phase, with the selectivity depending on the surfactant (e.g., cetyltrimethylammonium bromide) dissolved in the organic phase. Here, we elucidate the mechanism of molecular recognition in this system by using quantitative partitioning experiments and molecular dynamics (MD) simulations. Our results indicate that the selectivity for the red dye is thermodynamically favored at all surfactant concentrations, while selectivity for the blue dye is kinetically favored at high surfactant concentrations. The kinetic selectivity for the blue dye correlates with the presence of molecular aggregation at the oil-water interface. Coarse-grained MD simulations elucidate nanoscale supramolecular structures that can preferentially bind one small molecule rather than another at an interface, providing a selectively permeable barrier in the absence of proteins. The results suggest a new supramolecular mechanism for molecular recognition with potential applications in drug delivery, drug discovery, and biosensing.

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.

Identification of inhibitors targeting the energy-coupling factor (ECF) transporters

The energy-coupling factor (ECF) transporters are a family of transmembrane proteins involved in the uptake of vitamins in a wide range of bacteria. Inhibition of the activity of these proteins could reduce the viability of pathogens that depend on vitamin uptake. The central role of vitamin transport in the metabolism of bacteria and absence from humans make the ECF transporters an attractive target for inhibition with selective chemical probes. Here, we report on the identification of a promising class of inhibitors of the ECF transporters. We used coarse-grained molecular dynamics simulations on Lactobacillus delbrueckii ECF-FolT2 and ECF-PanT to profile the binding mode and mechanism of inhibition of this novel chemotype. The results corroborate the postulated mechanism of transport and pave the way for further drug-discovery efforts.

Systematic exploration of accessible topologies of cage molecules via minimalistic models

Cages are macrocyclic structures with an intrinsic internal cavity that support applications in separations, sensing and catalysis. These materials can be synthesised via self-assembly of organic or metal-organic building blocks. Their bottom-up synthesis and the diversity in building block chemistry allows for fine-tuning of their shape and properties towards a target property. However, it is not straightforward to predict the outcome of self-assembly, and, thus, the structures that are practically accessible during synthesis. Indeed, such a prediction becomes more difficult as problems related to the flexibility of the building blocks or increased combinatorics lead to a higher level of complexity and increased computational costs. Molecular models, and their coarse-graining into simplified representations, may be very useful to this end. Here, we develop a minimalistic toy model of cage-like molecules to explore the stable space of different cage topologies based on a few fundamental geometric building block parameters. Our results capture, despite the simplifications of the model, known geometrical design rules in synthetic cage molecules and uncover the role of building block coordination number and flexibility on the stability of cage topologies. This leads to a large-scale and systematic exploration of design principles, generating data that we expect could be analysed through expandable approaches towards the rational design of self-assembled porous architectures.

Understanding the Mechanical Properties of Ultradeformable Liposomes Using Molecular Dynamics Simulations

Improving drug delivery efficiency to solid tumor sites is a central challenge in anticancer therapeutic research. Our previous experimental study (Guo et al., Nat. Commun. 2018, 9, 130) showed that soft, elastic liposomes had increased uptake and accumulation in cancer cells and tumors in vitro and in vivo respectively, relative to rigid particles. As a first step toward understanding how liposomes' molecular structure and composition modulates their elasticity, we performed all-atom and coarse-grained classical molecular dynamics (MD) simulations of lipid bilayers formed by mixing a long-tailed unsaturated phospholipid with a short-tailed saturated lipid with the same headgroup. The former types of phospholipids considered were 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC) and 1,2-dipalmitoleoyl-sn-glycero-3-phosphocholine (termed here DPMPC). The shorter saturated lipids examined were 1,2-diheptanoyl-sn-glycero-3-phosphocholine (DHPC), 1,2-didecanoyl-sn-glycero-3-phosphocholine (DDPC), 1,2-dilauroyl-sn-glycero-3-phosphocholine (DLPC), and 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC). Several lipid concentrations and surface tensions were considered. Our results show that DOPC or DPMPC systems having 25-35 mol % of the shortest lipids DHPC or DDPC are the least rigid, having area compressibility moduli KA that are ∼10% smaller than the values observed in pure DOPC or DPMPC bilayers. These results agree with experimental measurements of the stretching modulus and lysis tension in liposomes with the same compositions. These mixed systems also have lower areas per lipid and form more uneven x-y interfaces with water, the tails of both primary and secondary lipids are more disordered, and the terminal methyl groups in the tails of the long lipid DOPC or DPMPC wriggle more in the vertical direction, compared to pure DOPC or DPMPC bilayers or their mixtures with the longer saturated lipid DLPC or DMPC. These observations confirm our hypothesis that adding increasing concentrations of the short unsaturated lipid DHPC or DDPC to DOPC or DPMPC bilayers alters lipid packing and thus makes the resulting liposomes more elastic and less rigid. No formation of lipid nanodomains was noted in our simulations, and no clear trends were observed in the lateral diffusivities of the lipids as the concentration, type of secondary lipid, and surface tension were varied.

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.

Structural basis of peptidoglycan synthesis by E. coli RodA-PBP2 complex

Peptidoglycan (PG) is an essential structural component of the bacterial cell wall that is synthetized during cell division and elongation. PG forms an extracellular polymer crucial for cellular viability, the synthesis of which is the target of many antibiotics. PG assembly requires a glycosyltransferase (GT) to generate a glycan polymer using a Lipid II substrate, which is then crosslinked to the existing PG via a transpeptidase (TP) reaction. A Shape, Elongation, Division and Sporulation (SEDS) GT enzyme and a Class B Penicillin Binding Protein (PBP) form the core of the multi-protein complex required for PG assembly. Here we used single particle cryo-electron microscopy to determine the structure of a cell elongation-specific E. coli RodA-PBP2 complex. We combine this information with biochemical, genetic, spectroscopic, and computational analyses to identify the Lipid II binding sites and propose a mechanism for Lipid II polymerization. Our data suggest a hypothesis for the movement of the glycan strand from the Lipid II polymerization site of RodA towards the TP site of PBP2, functionally linking these two central enzymatic activities required for cell wall peptidoglycan biosynthesis.

Protocol for the development of coarse-grained structures for macromolecular simulation using GROMACS

Coarse-grained simulations have emerged as a valuable tool in the study of large and complex biomolecular systems. These simulations, which use simplified models to represent complex biomolecules, reduce the computational cost of simulations and enable the study of larger systems for longer periods of time than traditional atomistic simulations. GROMACS is a widely used software package for performing coarse-grained simulations of biomolecules, and several force fields have been developed specifically for this purpose. In this protocol paper, we explore the advantages of using coarse-grained simulations in the study of biomolecular systems, focusing specifically on simulations performed using GROMACS. We discuss the force fields required for these simulations and the types of research questions that can be addressed using coarse-grained simulations. We also highlight the potential benefits of coarse-grained simulations for the development of new force fields and simulation methodologies. We then discuss the expected results from coarse-grained simulations using GROMACS and the various techniques that can be used to analyze these results. We explore the use of trajectory analysis tools, as well as thermodynamic and structural analysis techniques, to gain insight into the behavior of biomolecular systems.

Effect of Oil Polarity on the Protein Adsorption at Oil-Water Interfaces

Protein adsorption at oil-water interfaces has received much attention in applications of food emulsion and biocatalysis. The protein activity is influenced by the protein orientation and conformation. The oil polarity is expected to influence the orientation and conformation of adsorbed proteins by modulating intermolecular interactions. Hence, it is possible to tune the protein emulsion stability and activity by varying the oil polarity. Martini v3.0-based coarse-grained molecular dynamics (CGMD) simulations were employed to investigate the effect of oil polarity on the orientation and conformation of hydrophobin (HFBI) and Candida antarctica lipase B (CALB) adsorbed at triolein-water, hexadecane-water, and octanol-water interfaces for the first time. The protein adsorption orientation was predicted through the hydrophobic dipole, indicating that protein adsorption exists in preferred orientations at hydrophobic oil interfaces. The conformation of the adsorbed HFBI is well conserved, whereas relatively larger conformational changes occur during the CALB adsorption as the oil hydrophobicity increases. Comparisons on the adsorption interaction energy of proteins with oils confirm the relationship between the oil polarity and the interaction strength of proteins with oils. In addition, CGMD simulations allow longer time scale simulations of the behaviors of protein adsorption at oil-water interfaces.

An implementation of the Martini coarse-grained force field in OpenMM

We describe a complete implementation of Martini 2 and Martini 3 in the OpenMM molecular dynamics software package. Martini is a widely used coarse-grained force field with applications in biomolecular simulation, materials, and broader areas of chemistry. It is implemented as a force field but makes extensive use of facilities unique to the GROMACS software, including virtual sites and bonded terms that are not commonly used in standard atomistic force fields. OpenMM is a flexible molecular dynamics package widely used for methods development and is competitive in speed on GPUs with other commonly used packages. OpenMM has facilities to easily implement new force field terms, external forces and fields, and other nonstandard features, which we use to implement all force field terms used in Martini 2 and Martini 3. This allows Martini simulations, starting with GROMACS topology files that are processed by custom scripts, with all the added flexibility of OpenMM. We provide a GitHub repository with test cases, compare accuracy and performance between GROMACS and OpenMM, and discuss the limitations of our implementation in terms of direct comparison with GROMACS. We describe a use case that implements the Modeling Employing Limited Data method to apply experimental constraints in a Martini simulation to efficiently determine the structure of a protein complex. We also discuss issues and a potential solution with the Martini 2 topology for cholesterol.

pH-tunable membrane-active polymers, NCMNP2a-x, and their potential membrane protein applications

Accurate 3D structures of membrane proteins are essential for comprehending their mechanisms of action and designing specific ligands to modulate their activities. However, these structures are still uncommon due to the involvement of detergents in the sample preparation. Recently, membrane-active polymers have emerged as an alternative to detergents, but their incompatibility with low pH and divalent cations has hindered their efficacy. Herein, we describe the design, synthesis, characterization, and application of a new class of pH-tunable membrane-active polymers, NCMNP2a-x. The results demonstrated that NCMNP2a-x could be used for high-resolution single-particle cryo-EM structural analysis of AcrB in various pH conditions and can effectively solubilize BcTSPO with the function preserved. Molecular dynamic simulation is consistent with experimental data that shed great insights into the working mechanism of this class of polymers. These results demonstrated that NCMNP2a-x might have broad applications in membrane protein research.

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.