Coarse-grained Molecular Dynamics Research Articles

Simulation-based survey of TMEM16 family reveals that robust lipid scrambling requires an open groove.

Biological membranes are complex and dynamic structures with different populations of lipids in their inner and outer leaflets. The Ca 2+ -activated TMEM16 family of membrane proteins plays an important role in collapsing this asymmetric lipid distribution by spontaneously, and bidirectionally, scrambling phospholipids between the two leaflets, which can initiate signaling and alter the physical properties of the membrane. While evidence shows that lipid scrambling can occur via an open hydrophilic pathway ("groove") that spans the membrane, it remains unclear if all family members facilitate lipid movement in this manner. Here we present a comprehensive computational study of lipid scrambling by all TMEM16 members with experimentally solved structures. We performed coarse-grained molecular dynamics (MD) simulations of 27 structures from five different family members solved under activating and non-activating conditions, and we captured over 700 scrambling events in aggregate. This enabled us to directly compare scrambling rates, mechanisms, and protein-lipid interactions for fungal and mammalian TMEM16s, in both open (Ca 2+ -bound) and closed (Ca 2+ -free) conformations with statistical rigor. We show that all TMEM16 structures thin the membrane and that the majority of (>90%) scrambling occurs at the groove only when TM4 and TM6 have sufficiently separated. Surprisingly, we also observed 60 scrambling events that occurred outside the canonical groove, over 90% of which took place at the dimer-dimer interface in mammalian TMEM16s. This new site suggests an alternative mechanism for lipid scrambling in the absence of an open groove.

Effect of Pregenomic RNA on the Mechanical Stability of HBV Capsid by Coarse-Grained Molecular Simulations.

Hepatitis B virus (HBV) is a double-stranded DNA virus, but its life cycle involves an intermediate stage, during which pregenomic RNA (pgRNA) is encapsulated in the capsid and then reverse-transcribed into the minus DNA strand. These immature HBV virions are the key target for antiviral drug discovery. In this study, we investigate the flexibility and mechanical stability of the HBV capsid containing pgRNA by employing residue-resolved coarse-grained molecular dynamics simulations. The results showed that the presence of pgRNA tends to decrease the overall flexibility of the capsid. In addition, the symmetrically arranged subunits of the capsid show asymmetry in the dominant modes of the conformational fluctuations with or without the presence of pgRNA. Furthermore, the simulations revealed that the presence of pgRNA enhances the overall mechanical stability of the virion particle. Electrostatic interactions between the disordered CTD of capsid and pgRNA were found to play a crucial role in modulating viral mechanical stability. Decreasing the electrostatic interactions by CTD phosphorylation or high salt concentration significantly reduces the mechanical stability of the HBV capsid containing pgRNA. Finally, the 2-fold symmetric sites have been proposed to be the most vulnerable to rupture during the initial stages of capsid disassembly. These findings could enhance our understanding of the physical basis of viral invasion and provide valuable insights into the development of antiviral drugs.

Dynamics of Polymer Chains in Disperse Melts: Insights from Coarse-Grained Molecular Dynamics Simulations.

Synthetic polymers have a distribution of chain lengths which can be characterized by dispersity, Đ. Their macroscopic properties are influenced by chain mobility in the melt, and controlling Đ can significantly impact these properties. In this work, we present a detailed study of the static and dynamic behavior of fully flexible polymer chains that follow the Schulz-Zimm molecular weight distribution up to Đ = 2.0 using coarse-grained molecular dynamics simulations. We analyze the behavior of test chains with molecular weights that are equal to, above, or below the molecular weight (Mw) of the melt. Static analysis shows that the conformation of these test chains remains unaffected by the heterogeneity of the surrounding chains. To study the dynamics, we computed the mean-squared displacement of test chains in melts of the same Mw and different dispersities. The mobility of test chains with N > Mw steadily increases with dispersity, due to the shorter chains contributing to early onset of disentanglement of the long chains. However, the dynamics of test chains of length N < Mw is nonmonotonic with respect to dispersity; this behavior arises from a trade-off between the increased mobility of shorter chains and the corresponding slowdown caused by the presence of longer chains. We examine the dynamic structure factor and find a weakening of tube confinement, with the effects becoming less pronounced with increasing dispersity and Mw. These findings provide insights into the rich dynamic heterogeneity of disperse polymer melts.

Interaction of complex particles: A framework for the rapid and accurate approximation of pair potentials using neural networks

Motivated by the limitations of conventional coarse-grained molecular dynamics for simulation of large systems of nanoparticles and the challenges in efficiently representing general pair potentials for rigid bodies, we present a method for approximating general rigid body pair potentials based on a specialized type of deep neural network that maintains essential properties, such as conservation of energy and invariance to the chosen origins of the particles. The network uses a specialized geometric abstraction layer to convert the relative coordinates of the rigid bodies to input more suitable to a conventional artificial neural network, which is trained together with the specialized layer. This results in geometric representations of the particles optimized for the specific potential. The network can be trained directly on scalar values to fit a model without explicit gradient and then used to efficiently evaluate the force and torque on the particles resulting from the potential. The concept is demonstrated with an atomistic interaction model for carbon nanotubes and the resulting model is compared with a common type of coarse-grained model optimized for the same potential, with even very small networks comparing favourably and larger networks achieving up to two orders of magnitude lower cost. The sensitivity to noise in the training data is investigated and the model is found to strongly reject noise up to 12.5% given a dataset of 107 samples. The performance of a proof-of-concept implementation is demonstrated on a variety of hardware, showing the models viability for large-scale simulations. Furthermore, generalization to soft bodies and potentials for polydisperse systems are discussed. Published by the American Physical Society 2024

A Coarse-Grained Molecular Dynamics Perspective on the Release of 5-Fluorouracil from Liposomes.

Liposomes, small bilayer phospholipid-containing vesicles, are frequently used to ensure slow drug release for a prolonged and improved therapeutic effect. Nevertheless, current findings on the membrane affinity and permeability of the anticancer agent 5-fluorouracil (5-FU) are confounding, which leads to a lack of a clear understanding of how lipid composition impacts the distribution of 5-FU within liposomal structures and its delivery. In the current work, we report a comprehensive coarse-grained molecular dynamics (CGMD) investigation on the influence of cholesterol (CHOL) and the cationic lipid 1,2-dioleoyl-3-trimethylammonium-propane (DOTAP) on the partitioning of 5-FU in 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC) double-bilayer systems, as well as its in vitro release from liposomes with identical lipid compositions. Our results show that 5-FU tends to accumulate at the water-lipid interface, in the vicinity of polar headgroups, without partitioning in the hydrophobic tail region. At the same time, the presence of CHOL proportionally increases the distribution of this drug in the interbilayer aqueous space, decreasing the drug's affinity toward the membrane polar head region, while DOTAP has only a slight effect on drug distribution. Thus, it is expected that 5-FU will be released slower from CHOL-containing DPPC liposomes but not DOTAP-containing vesicles. However, in vitro release studies showed that the release kinetics of 5-FU from DPPC vesicles is not influenced by the presence of CHOL and that the incorporation of 10 mol % DOTAP leads to the best release profile for 5-FU, highlighting the complexity of nanocarrier drug release kinetics. We hypothesize that the initial rapid release seen in dialysis experiments is not related to drug membrane permeability but rather to 5-FU adsorbed on the outer surface of liposomes.

Recent Advances in Simulation Studies on the Protein Corona

When flowing through the blood stream, drug carriers such as nanoparticles encounter hundreds of plasma proteins, forming a protein layer on the nanoparticle surface, known as the “protein corona”. Since the protein corona influences the size, shape, and surface properties of nanoparticles, it can modulate their circulating lifetime, cytotoxicity, and targeting efficiency. Therefore, understanding the mechanism of protein corona formation at the atomic scale is crucial, which has become possible due to advances in computer power and simulation methodologies. This review covers the following topics: (1) the structure, dynamics, and composition of protein corona on nanoparticles; (2) the effects of protein concentration and ionic strength on protein corona formation; (3) the effects of particle size, morphology, and surface properties on corona formation; (4) the interactions among lipids, membranes, and nanoparticles with the protein corona. For each topic, mesoscale, coarse-grained, and all-atom molecular dynamics simulations since 2020 are discussed. These simulations not only successfully reproduce experimental observations but also provide physical insights into the protein corona formation. In particular, these simulation findings can be applied to manipulate the formation of a protein corona that can target specific cells, aiding in the rational design of nanomedicines for drug delivery applications.

Systematic Evaluation of Macromolecular Carbohydrate-Lectin Recognition Using Precision Glycopolymers.

The precise modulation of protein-carbohydrate interactions is critical in glycobiology, where multivalent binding governs key cellular processes. As such, synthetic glycopolymers are useful for probing these interactions. Herein, we developed precision glycopolymers (PGPs) with unambiguous local chemical composition and well-defined global structure and systematically evaluated the effect of polymer length, hydrophobicity, and backbone hybridization as well as glycan density and identity on the binding to both mammalian and plant lectins. Our studies identified glycan density as a critical factor, with PGPs below 50% grafting density showing significantly weaker lectin interactions. Coarse-grained molecular dynamics simulations suggest that the observed phenomena may be due to a decrease in carbohydrate-carbohydrate interactions in fully grafted PGPs, leading to improved solvent accessibility. In functional assays, these PGPs reduced the cell viability and migration in 4T1 breast cancer cells. Our findings establish a structure-activity relationship in glycopolymers, providing new strategies for designing synthetic glycomacromolecules for a myriad of applications.

The Role of Cholesterol in M2 Clustering and Viral Budding Explained.

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

The nanoscale tensile behavior of polyaspartate polyurea: a coarse-grained molecular dynamics simulation study

The nanoscale tensile behavior of polyaspartate polyurea: a coarse-grained molecular dynamics simulation study

Cryptochrome magnetoreception: time course of photoactivation from non-equilibrium coarse-grained molecular dynamics

Magnetoreception, the ability to sense magnetic fields, is widespread in animals but remains poorly understood. The leading model links this ability in migratory birds to the photo-activation of the protein cryptochrome. Magnetic information is thought to induce structural changes in cryptochrome via a transient radical pair intermediate. This signal transduction pathway has been the subject of previous all-atom molecular dynamics (MD) simulations, but insights were limited to short timescales and equilibrium structures. To address this, we developed a non-equilibrium coarse-grained MD simulation approach, exploring cryptochrome’s photo-reduction over 20 replicates of 20 µs each. Our results revealed significant structural changes across the protein, with an overall time constant of 3 µs. The C-terminal (CT) region responded on a timescale of 4.7 µs, followed by the EEE-motif, while the phosphate binding loop (PBL) showed slower dynamics (9 µs). Network analysis highlighted direct pathways connecting the tryptophan tetrad to the CT, and distant pathways involving the EEE and PBL regions. The CT-dynamics are significantly impacted by a rearrangement of tryptophan residues in the central electron transfer chain. Our findings underscore the importance of considering longer timescales when studying cryptochrome magnetoreception and highlight the potential of non-equilibrium coarse-grained MD simulations as a powerful tool to unravel protein photoactivation reactions.

Toward understanding lipid reorganization in RNA lipid nanoparticles in acidic environments

The use of lipid nanoparticles (LNPs) for therapeutic RNA delivery has gained significant interest, particularly highlighted by recent milestones such as the approval of Onpattro and two mRNA-based SARS-CoV-2 vaccines. However, despite substantial advancements in this field, our understanding of the structure and internal organization of RNA-LNPs -and their relationship to efficacy, both in vitro and in vivo- remains limited. In this study, we present a coarse-grained molecular dynamics (MD) approach that allows for the simulations of full-size LNPs. By analyzing MD-derived structural characteristics in conjunction with cellular experiments, we investigate the effect of critical parameters, such as pH and composition, on LNP structure and potency. Additionally, we examine the mobility and chemical environment within LNPs at a molecular level. Our findings highlight the significant impact that LNP composition and internal molecular mobility can have on key stages of LNP-based intracellular RNA delivery.

Network Topology and Percolation in Model Covalent Adaptable Networks.

Incorporating dynamic covalent linkages into thermosets can endow previously unrecyclable materials with new functionality and reprocessing options. Recent work has shown that the properties of the resulting covalent adaptable networks (CANs) are highly dependent on network topology, specifically the phenomenon of percolation, when permanent linkages form a connected skeleton that spans the material. Here, we use a model glassy disulfide based CAN to assess the merits of mean-field percolation theory as a tool to describe the network topology of CANs. After challenging the theory with both experimental data and a coarse-grained molecular dynamics simulation, we find that the mean-field approach is surprisingly accurate, despite its simplifying assumptions. The theory is particularly well suited to the unique context of mixed-composition CANs and provides practical guidance on how to design for reprocessability.

IsRNAcirc: 3D structure prediction of circular RNAs based on coarse-grained molecular dynamics simulation.

As an emerging class of RNA molecules, circular RNAs play pivotal roles in various biological processes, thereby determining their three-dimensional (3D) structure is crucial for a deep understanding of their biological significances. Similar to linear RNAs, the development of computational methods for circular RNA 3D structure prediction is challenging, especially considering the inherent flexibility and potentially long length of circular RNAs. Here, we introduce an extension of our previous IsRNA2 model, named IsRNAcirc, to enable circular RNA 3D structure predictions through coarse-grained molecular dynamics simulations. The workflow of IsRNAcirc consists of four main steps, including input preparation, end closure, structure prediction, and model refinement. Our results demonstrate that IsRNAcirc can provide reasonable 3D structure predictions for circular RNAs, which significantly reduce the locally irrational elements contained in the initial input. Moreover, for a validation test set comprising 34 circular RNAs, our IsRNAcirc can generate 3D models with better scores than the template-based 3dRNA method. These findings demonstrate that our IsRNAcirc method is a promising tool to explore the structural details along with intricate interactions of circular RNAs.

Dependence of Thermally Activated Relaxation of Crystalline Stems on the Molecular Topology at Crystalline/Amorphous Interfaces in Polyethylene.

We investigate the relaxation dynamics of crystalline stems in relation to the molecular topology of the crystalline/amorphous interface, employing coarse-grained molecular dynamics. To efficiently generate model semicrystalline systems of linear polyethylene with a realistic interphase morphology, we simplified the Monte Carlo method by introducing molecular dynamics for faster relaxation. The structural properties of the generated systems are validated against experimental measurements, theoretical predictions, and existing simulation data. The models suggest that the probability distribution of loop-entry sites on the lamellar surface can be described by a power law in terms of the distance between the entry sites. By considering realistic interphase morphology, we are able to improve the prediction of the overall activation energy for the relaxation of crystalline stems, aligning it closely with experimental measurements. The largest model predicts that crystalline stems connected via large loops, i.e., those that exceed the entanglement length, and long tails are associated with increased activation energy; whereas stems connected to shorter tails show the lowest activation energy. These predictions can guide the future development of tougher semicrystalline polymers by providing insights into how amorphous chain morphology contributes to the activation energy and the relaxation dynamics of crystalline chains.

Coarse-Grained MD Simulations of the Capillary Interaction between a Sphere and a Binary Fluid with Truncated Lennard-Jones Potentials.

In atomic force microscopy experiments on fluid samples, a capillary bridge forms between the tip and the fluid, causing an attractive capillary force. Here, we present a computational model of the capillary interaction between a solid sphere and a coarse-grained Lennard-Jones fluid containing 10% antifreeze particles with an enlarged van der Waals radius. The capillary force acting on the sphere is obtained from the displacement of the sphere in a trap potential as the sphere is incrementally approached and then retracted from the fluid. This yields force-distance data similar to that obtained in atomic force microscopy experiments. We use this methodology to study the influence of the cutoff radius of the truncated Lennard-Jones potentials on the capillary force and its temperature dependence. The latter is found to scale with the critical temperature of the system. With the presented approach, the tip-sample interaction can be studied for a wide range of complex fluids, particle shapes, and force-probing schemes.

Effect of Nanostructure and Crosslinks on Impact Resistance of Carbon Nanotube Films Under Micro-Ballistic Impact.

Carbon nanotube (CNT) films show great promise as an advanced bulletproof materials due to their excellent energy dissipation ability under impact loadings. However, it is challenging to determine the optimized architecture structure of CNTs to enhance the impact resistance of CNT films. In this study, the impact behavior of CNT films with various architecture structures werestudied by micro-ballistic impact experiments and coarse-grained molecular dynamics (CGMD) simulations. The micro-ballistic impact experimental results showed that the cross-ply laminated (CPL) structure enhances significantly the specific energy absorption (SEA) of CNT films compared to that with disordered structure due to the synergistic interactions between covalent bonds in CNT chains. On this basis, four CPL-CNT (CCNT) films with the same areal density (ρ2D) but different single-layer areal density ( ) and one disordered CNT (DCNT) film with the same ρ2D as the CCNT films wereconstructed in CGMD models. The simulation results showed that the SEAs of all the four CCNT films are higher than DCNT film, which is consistent with experiments. In addition, the SEAs of CCNT films increase with decreasing . However, too small can lead to local plugging failure of the CNT film and therefore decrease SEA of the CNT film. Moreover, adding crosslinks could further increase the SEAs of both the DCNT and the CCNT films due to the strengthened interactions of adjacent CNTs. The crosslinked CCNT films with appropriate ρ2D is still much higher than the crosslinked DCNT films. Furthermore, it wasfurther found that when the strength of the crosslinks aligns with that of the CNT beads, the CNT film achieves preeminent impact resistance. This study provides a pathway for enhancing the impact resistance of CNT films by optimizing the microstructure and introducing crosslinks betweenCNTs.

Effect of Surface-Immobilized States of Antimicrobial Peptides on Their Ability to Disrupt Bacterial Cell Membrane Structure

Antimicrobial peptide (AMP) surfaces are widely used to inhibit biofilm formation and bacterial infection. However, endpoint-immobilized AMPs on surfaces are totally different from free-state AMPs due to the constraints of the surface. In this work, the interactions between AMPs and bacterial cell membranes were analyzed through coarse-grained molecular dynamics and all-atom molecular dynamics simulations. This AMP disrupted membrane structure by altering the thickness and curvature of the membrane. Furthermore, the effect of surface-immobilized states of AMPs on their ability to disrupt membrane structure was revealed. The immobilized AMPs in the freeze-N system could bind to the membrane and disrupt the membrane structure through electrostatic forces between positively charged N-terminal amino acid residues and the negatively charged membrane, while the immobilized AMPs in the freeze-C system were repelled. The results will aid in the rational design of new AMP surfaces with enhanced efficacy and stability.

Dual Role of Anionic Lipids in Amyloid Aggregation.

Neurodegenerative diseases, such as Alzheimer's, Parkinson's, and Huntington's, affect millions worldwide and share a common feature: the aggregation of intrinsically disordered proteins into toxic oligomers that interact with cell membranes. In Alzheimer's disease (AD), amyloid-beta (Aβ) peptides accumulate and bind to plasma membranes, potentially disrupting cellular function. The complex interplay between amyloidogenic peptides and lipid membranes, particularly the role of anionic lipids, is crucial in disease pathogenesis but challenging to characterize experimentally. The literature presents conflicting results on the influence of anionic lipids on peptide aggregation kinetics, highlighting a knowledge gap. To address this, we used coarse-grained molecular dynamics (CG-MD) simulations to study interactions between a model amyloidogenic peptide, amyloid-β's K16LVFFAE22 fragment (Aβ16-22), and mixed lipid bilayers. We used phosphatidylserine (PS) and phosphatidylcholine (PC) as representative anionic and zwitterionic lipids, respectively, examining the mixed bilayer compositions of 0% PS-100% PC, 10% PS-90% PC, and 30% PS-70% PC. Our simulations revealed that membranes enriched in anionic lipids enhance peptide adsorption and interaction kinetics. The aggregation dynamics was modulated by two competing factors: increased local peptide concentration near negatively charged membranes, which promoted aggregation, and peptide-lipid interactions, which slowed it down. Higher percentages of anionic lipids led to smaller and more ordered aggregates and enhanced lipid demixing, leading to the formation of PS clusters. These findings contribute to understanding membrane-mediated peptide aggregation in neurodegenerative disorders, potentially guiding new therapeutic strategies targeting the early stages of protein aggregation in various neurodegenerative diseases.

Megamolecule Self-Assembly Networks: A Combined Computational and Experimental Design Strategy.

This work describes the use of computational strategies to design megamolecule building blocks for the self-assembly of lattice networks. The megamolecules are prepared by attaching four Cutinase-SnapTag fusion proteins (CS fusions) to a four-armed linker, followed by functionalizing each fusion with a terpyridine linker. This functionality is designed to participate in a metal-mediated self-assembly process to give networks. This article describes a simulation-guided strategy for the design of megamolecules to optimize the peptide linker in the fusion protein to give conformations that are best suited for self-assembly and therefore streamlines the typically time-consuming and labor-intensive experimental process. We designed 11 candidate megamolecules and identified the most promising linker, (EAAAK)2, along with the optimal experimental conditions through a combination of all-atom molecular dynamics, enhanced sampling, and larger-scale coarse-grained molecular dynamics simulations. Our simulation findings were validated and found to be consistent with the experimental results. Significantly, this study offers valuable insight into the self-assembly of megamolecule networks and provides a novel and general strategy for large biomolecular material designs by using systematic bottom-up coarse-grained simulations.

Understanding the role of chain stiffness in the mechanical response of cross-linked polymer: Flexible vs. semi-flexible chains

Cross-linked polymers are widely used in structural, engineering, and biomedical applications due to their lightweight and superior properties. Although chain bending stiffness has been recognized to play an essential role in their thermodynamical and mechanical properties, how it influences these properties of cross-linked polymers with flexible or semi-flexible chains remains under debate. Here, we systematically explore its influences utilizing coarse-grained (CG) molecular dynamics (MD) simulations based on a bead-spring CG model. It is found that with chain bending stiffness increasing, both density and elastic moduli (i.e., shear modulus and tensile modulus) of cross-linked polymers first decrease slightly and then decrease significantly followed by a gradual increase, along with the polymer transition from a dense cross-linked thermoset to a highly porous fibrous network. The moduli of cross-linked polymers with flexible and semi-flexible chains exhibit distinct scaling laws with the density. For cross-linked polymers with flexible chains, their moduli increase significantly with increasing strain rate, which correlates to the change in potential energy of interchain interaction during deformation. However, the moduli display slight dependence on strain rate for porous cross-linked polymers with sufficiently stiff chains, where the intrachain interactions (i.e., bond stretching and angle bending energies) become dominant and independent of strain rate. Moreover, the elastic moduli exhibit scaling laws with Debye-Waller factor for both dense cross-linked thermosets with flexible chains and highly porous networks with stiff backbones. Our work facilitates a better understanding for mechanical properties and deformation mechanism of cross-linked polymers with variable chain bending stiffness at molecular level, shedding light on tailoring mechanical properties of cross-linked polymers via chain engineering.