Coarse-grained Molecular Dynamics Simulations Research Articles

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.

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.

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.

Progress in Multiscale Modeling of Silk Materials.

As a result of their hierarchical structure and biological processing, silk fibers rank among nature's most remarkable materials. The biocompatibility of silk-based materials and the exceptional mechanical properties of certain fibers has inspired the use of silk in numerous technical and medical applications. In recent years, computational modeling has clarified the relationship between the molecular architecture and emergent properties of silk fibers and has demonstrated predictive power in studies on novel biomaterials. Here, we review advances in modeling the structure and properties of natural and synthetic silk-based materials, from early structural studies of silkworm cocoon fibers to cutting-edge atomistic simulations of spider silk nanofibrils and the recent use of machine learning models. We explore applications of modeling across length scales: from quantum mechanical studies on model peptides, to atomistic and coarse-grained molecular dynamics simulations of silk proteins, to finite element analysis of spider webs. As computational power and algorithmic efficiency continue to advance, we expect multiscale modeling to become an indispensable tool for understanding nature's most impressive fibers and developing bioinspired functional materials.

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.

Pulling Forces Differentially Affect Refolding Pathways Due to Entangled Misfolded States in SARS-CoV-1 and SARS-CoV-2 Receptor Binding Domain

Single-molecule force spectroscopy (SMFS) experiments can monitor protein refolding by applying a small force of a few piconewtons (pN) and slowing down the folding process. Bell theory predicts that in the narrow force regime where refolding can occur, the folding time should increase exponentially with increased external force. In this work, using coarse-grained molecular dynamics simulations, we compared the refolding pathways of SARS-CoV-1 RBD and SARS-CoV-2 RBD (RBD refers to the receptor binding domain) starting from unfolded conformations with and without a force applied to the protein termini. For SARS-CoV-2 RBD, the number of trajectories that fold is significantly reduced with the application of a 5 pN force, indicating that, qualitatively consistent with Bell theory, refolding is slowed down when a pulling force is applied to the termini. In contrast, the refolding times of SARS-CoV-1 RBD do not change meaningfully when a force of 5 pN is applied. How this lack of a Bell response could arise at the molecular level is unknown. Analysis of the entanglement changes of the folded conformations revealed that in the case of SARS-CoV-1 RBD, an external force minimizes misfolding into kinetically trapped states, thereby promoting efficient folding and offsetting any potential slowdown due to the external force. These misfolded states contain non-native entanglements that do not exist in the native state of either SARS-CoV-1-RBD or SARS-CoV-2-RBD. These results indicate that non-Bell behavior can arise from this class of misfolding and, hence, may be a means of experimentally detecting these elusive, theoretically predicted states.

A Nonfouling Electrochemical Biosensor for Protein Analysis in Complex Body Fluids Based on Multifunctional Peptide Conjugated with PEGlyated Phospholipid.

Developing antifouling biosensors capable of performing robustly in complex human body fluids is crucial for biomarker diagnosis and health monitoring. Herein, an antifouling and highly sensitive and stable biosensor was constructed through the self-assembly of the designed conjugates composed of a multifunctional peptide (MP) and PEGylated distearoylphosphatidylethanolamine (DSPE-PEG). The self-assembly capability of the DSPE-PEG-MP was demonstrated clearly through coarse-grained molecular dynamics simulation and transmission electron microscopy, and it can be effectively self-assembled onto the electrode surface modified with gold nanoparticles. The MP was designed to be antifouling and contained a peptide sequence that can specifically bind the target protein Annexin A1 (ANXA1), and the D-type amino acid composition of MP can enhance its resistance to enzymatic hydrolysis. The unique design of MP, in conjugation with the self-assembly capability of the PEGylated phospholipid DSPE-PEG, enabled the biosensor to exhibit excellent antifouling capability and stability in various complex human body fluids. The biosensor was capable of sensitively and selectively quantifying ANXA1 and achieved a limit of detection down to 0.12 pg mL-1. More importantly, the biosensor demonstrated satisfactory accuracy for ANXA1 detection in clinical serum samples, as verified by the enzyme linked immunosorbent assay (ELISA) kits. It is expected that various antifouling biosensors suitable for application in complex biological environments can be constructed by utilizing the strategy of designing similar DSPE-PEG-MP conjugates.

Dispersion of Hydrophilic Nanoparticles in Natural Rubber with Phospholipids

Coarse-grained molecular dynamics (CGMD) simulations were employed to investigate the effects of phospholipids on the aggregation of hydrophilic, modified carbon-nanoparticle fillers in cis-polyisoprene (cis-PI) composites. The MARTINI force field was applied to model dipalmitoylphosphatidylcholine (DPPC) lipids and hydrophilic modified fullerenes (HMFs). The simulations of DPPC in cis-PI composites show that the DPPC lipids self-assemble to form a reverse micelle in a rubber matrix. Moreover, HMF molecules readily aggregate into a cluster, in agreement with the previous studies. Interestingly, the mixture of the DPPC and HMF in the rubber matrix shows a cluster of HMF is encapsulated inside the DPPC reverse micelle. The HMF encapsulated micelles disperse well in the rubber matrix, and their sizes are dependent on the lipid concentration. Mechanical and thermal properties of the composites were analyzed by calculating the diffusion coefficients (D), bulk modulus (κ), and glass transition temperatures (Tg). The results suggest that DPPC acts as a plasticizer and enhances the flexibility of the HMF-DPPC rubber composites. These findings provide valuable insights into the design and process of high-performance rubber composites, offering improved mechanical and thermal properties for various applications.

Spontaneous Dimerization and Distinct Packing Modes of Transmembrane Domains in Receptor Tyrosine Kinases.

The insulin receptor (IR) and the insulin-like growth factor-1 receptor (IGF1R) are homodimeric transmembrane glycoproteins that transduce signals across the membrane on binding of extracellular peptide ligands. The structures of IR/IGF1R fragments in apo and liganded states have revealed that the extracellular subunits of these receptors adopt Λ-shaped configurations to which are connected the intracellular tyrosine kinase (TK) domains. The binding of peptide ligands induces structural transitions in the extracellular subunits leading to potential dimerization of transmembrane domains (TMDs) and autophosphorylation in TKs. However, the activation mechanisms of IR/IGF1R, especially the role of TMDs in coordinating signal-inducing structural transitions, remain poorly understood, in part due to the lack of structures of full-length receptors in apo or liganded states. While atomistic simulations of IR/IGF1R TMDs showed that these domains can dimerize in single component membranes, spontaneous unbiased dimerization in a plasma membrane having a physiologically representative lipid composition has not been observed. We address this limitation by employing coarse-grained (CG) molecular dynamics simulations to probe the dimerization propensity of IR/IGF1R TMDs. We observed that TMDs in both receptors spontaneously dimerized independent of their initial orientations in their dissociated states, signifying their natural propensity for dimerization. In the dimeric state, IR TMDs predominantly adopted X-shaped configurations with asymmetric helical packing and significant tilt relative to the membrane normal, while IGF1R TMDs adopted symmetric V-shaped or parallel configurations with either no tilt or a small tilt relative to the membrane normal. Our results suggest that IR/IGF1R TMDs spontaneously dimerize and adopt distinct dimerized configurations.

Generating Protein Structures for Pathway Discovery Using Deep Learning.

Resolving the intricate details of biological phenomena at the molecular level is fundamentally limited by both length- and time scales that can be probed experimentally. Molecular dynamics (MD) simulations at various scales are powerful tools frequently employed to offer valuable biological insights beyond experimental resolution. However, while it is relatively simple to observe long-lived, stable configurations of, for example, proteins, at the required spatial resolution, simulating the more interesting rare transitions between such states often takes orders of magnitude longer than what is feasible even on the largest supercomputers available today. One common aspect of this challenge is pathway discovery, where the start and end states of a scientific phenomenon are known or can be approximated, but the mechanistic details in between are unknown. Here, we propose a representation-learning-based solution that uses interpolation and extrapolation in an abstract representation space to synthesize potential transition states, which are automatically validated using MD simulations. The new simulations of the synthesized transition states are subsequently incorporated into the representation learning, leading to an iterative framework for targeted path sampling. Our approach is demonstrated by recovering the transition of a RAS-RAF protein domain (CRD) from membrane-free to interacting with the membrane using coarse-grain MD simulations.

Stick, Slide, or Bounce: Charge Density Controls Nanoparticle Diffusion.

The diffusion and interaction dynamics of charged nanoparticles (NPs) within charged polymer networks are crucial for understanding various biological and biomedical applications. Using a combination of coarse-grained molecular dynamics simulations and experimental diffusion studies, we investigate the effects of the NP size, relative surface charge density (ζ), and concentration on the NP permeation length and time. We propose a scaling law for the relative diffusion of NPs with respect to concentration and ζ, highlighting how these factors influence the NP movement within the network. The analyses reveal that concentration and ζ significantly affect NP permeation length and time, with ζ being critical, as critical as concentration. This finding is corroborated by controlled release experiments. Further, we categorize NP dynamics into sticking, sliding, and bouncing regimes, demonstrating how variations in ζ, concentration, and NP size control these behaviors. Through normalized attachment time (NAT) analyses, we elucidate the roles of electrostatic interactions, steric hindrance, and hydrodynamic forces in governing NP dynamics. These insights provide guidance for optimizing NP design in targeted drug delivery and advanced material applications, enhancing our understanding of NP behavior in complex environments.

Coarse-Grained Simulations of Adeno-Associated Virus and Its Receptor Reveal Influences on Membrane Lipid Organization and Curvature.

Adeno-associated virus (AAV) is a well-known gene delivery tool with a wide range of applications, including as a vector for gene therapies. However, the molecular mechanism of its cell entry remains unknown. Here, we performed coarse-grained molecular dynamics simulations of the AAV serotype 2 (AAV2) capsid and the universal AAV receptor (AAVR) in a model plasma membrane environment. Our simulations show that binding of the AAV2 capsid to the membrane induces membrane curvature, along with the recruitment and clustering of GM3 lipids around the AAV2 capsid. We also found that the AAVR binds to the AAV2 capsid at the VR-I loops using its PKD2 and PKD3 domains, whose binding poses differs from previous structural studies. These first molecular-level insights into AAV2 membrane interactions suggest a complex process during the initial phase of AAV2 capsid internalization.

Study on preparation of “ping-pong” shaped chitosan oligosaccharide -based hollow mesoporous carbon as a carrier for efficient anthocyanin loading

Carriers for efficient loading and delivery of compounds are urgently needed. A multifunctional nanoplatform of ordered hollow mesoporous carbon (HMC) was developed to load anthocyanins (AN) efficiently. The morphology, specific surface area, binding mode, and biocompatibility of HMC were verified. HMCs were uniformly spherical with well-defined cavities and mesoporous shells, similar to a “ping-pong” ball shape, and this shape of HMC provided a more spatial location for the load of the AN. And the best loading result of AN was 33.39 % ± 3.00 %. Coarse-grained molecular dynamics (CGMD) simulations showed that HMC and AN may bind by electrostatic interaction and hydrogen bonding, the binding process indicated that HMC contributed to the loading of AN, and the cytotoxicity results showed no significant toxicity of the complex. The homogeneous morphology and good biocompatibility of HMC offer new probabilities for the high effectiveness of oral delivery of active substances.

Molecular dynamics simulations of lipid composition and its impact on structural and dynamic properties of skin membrane

The stratum corneum (SC) plays the most important role in the absorption of topical and transdermal drugs. In this study, we developed a multi-layered SC model using coarse-grained molecular dynamics (CGMD) simulations of ceramides, cholesterol, and fatty acids in equimolar proportions, starting from two different initial configurations. In the first approach, all ceramide molecules were initially in the hairpin conformation, and the membrane bilayers were pre-formed. In the second approach, ceramide molecules were introduced in either the hairpin or splayed conformation, with the lipid molecules randomly oriented at the start of the simulation. The aim was to evaluate the effects of lipid chain length on the structural and dynamic properties of SC. By incorporating ceramides and fatty acids of different chain lengths, we simulated the SC membrane in healthy and diseased states. We calculated key structural properties including the thickness, normalized lipid area, lipid tail order parameters, and spatial ordering of the lipids from each system. The results showed that systems with higher ordering and structural integrity contained an equimolar ratio of ceramides (chain length of 24 carbon atoms), fatty acids with chain lengths ≥ of 20 carbon atoms, and cholesterol. In these systems, strong apolar interactions between the ceramide and fatty acid long acyl chains restricted the mobility of the lipid molecules, thereby maintaining a compact lipid headgroup region and high order in the lipid tail region. The simulations also revealed distinct flip-flop mechanisms for cholesterol and fatty acid within the multi-layered membrane. Cholesterol is mostly diffused through the tail-tail interface region of the membrane and could flip-flop in the same bilayer. In contrast, fatty acids flip-flopped between adjacent leaflets of two bilayers in which the tails crossed the thinner headgroup region of the membrane. To conclude, our SC model provides mechanistic insights into lipid mobility and is flexible in its design and composition of different lipids, enabling studies of varying skin conditions.

Coarse-grained molecular dynamic model and wettability simulation of graphite materials

Although progress has been made in high-performance computing, there are still limitations on temporal and spatial scales of molecular dynamic calculations. A major issue in molecular dynamic (MD) simulations is the computational cost, and coarse-grained methods can save computational costs and accelerate calculations by reducing the degrees of freedom in the system. This method takes a selected group of representative atoms in the atomic microstructure as a bead and uses the proportional relationship of atomic scale atomic potentials in MD simulation to define the interaction between beads and solve the motion equation of beads. This article proposed a coarse-grained potential for graphite based on the modification of Airebo potential, with an n3: 1 mapping, and established a corresponding n3: 1 coarse-grained model, where one bead represents the number of n3 atoms. The results indicated that the coarse-grained model well reflected the basic thermal and mechanical properties of graphite. In addition, this article also proposed a coarsening method for the Lennard–Jones (L–J) potential function parameters and established a coarse-grained wetting model with n = 2. The results indicate that the coarse-grained wetting model can effectively predict the wetting performance of copper droplets on graphite in only 60% of the time using the all-atom model. The coarsening potential function and model proposed in this article are also applicable to graphite with rough surfaces. It can be anticipated that coarse-grained molecular dynamics methods will have more applications in the future, as they can handle large-scale calculations more quickly.

Effects of the Absence of Friction in Coarse-Grained Molecular Dynamics Simulations of Clay

Effects of the Absence of Friction in Coarse-Grained Molecular Dynamics Simulations of Clay

Coarse-Grained Molecular Dynamics Simulation of Ionomer-Mediated Carbon Cluster Bonding in Polymer Electrolyte Fuel Cell Catalyst Layers

This study examines the impact of ionomer to carbon mass ratio and the number of platinum particle clusters on the structural dynamics and organization of Pt-C-Nafion catalyst layers during the evaporation process. Using Coarse-Grained Molecular Dynamics (CGMD) simulations, we analyzed the center of mass position and ionomer coverage on Pt surface. The results show that a higher ionic polymer to carbon mass ratio (0.6) increases lateral mobility and short-range ordering along with the coverage of the Pt surface with ionomers, whereas increasing the number of Pt particle clusters (from 1 to 4 ) enhances lateral diffusion as well as leading to more difficult adsorption of ionomers. These findings highlight the importance of optimizing these parameters to obtain a homogeneous and stable catalyst layer, which is critical for fuel cell performance. Future work will include tuning the Pt/C mass ratio, hydrophilicity, and IC ratio in conjunction with energy and force analyses to further understand the effect of Nafion on Pt/C clusters.

Robust lipid scrambling by TMEM16 proteins requires an open groove and thin membrane.

Biological membranes are complex and dynamic structures with different populations of lipids on their inner and outer leaflets. The Ca2+-activated TMEM16 family of membrane proteins play 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 occurs 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 (Ca2+-bound) and closed (Ca2+-free) conformations with statistical rigor. We show that nearly all (>90%) scrambling occurs in the dilated groove and the degree of induced membrane thinning positively correlates with the scrambling rate. 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.

Ebola Virus Matrix Protein VP40 Single Mutations G198R and G201R Significantly Enhance Plasma Membrane Localization.

Viral proteins frequently undergo single or multiple amino acid mutations during replication, which can significantly alter their functionality. The Ebola virus matrix protein VP40 is multifunctional but primarily responsible for creating the viral envelope by binding to the inner leaflet of the host cell plasma membrane (PM). Changes to the VP40 surface cationic charge via mutations can influence PM interactions, resulting in altered viral assembly and budding. A recent mutagenesis study evaluated the effects of several mutations and found that mutations G198R and G201R enhanced VP40 assembly at the PM and virus-like particle budding. These two mutations lie in the loop region of the C-terminal domain (CTD), which directly interacts with the PM. To understand the role of these mutations in PM localization at the molecular level, we performed both all-atom and coarse-grained molecular dynamics simulations using a dimer-dimer configuration of VP40, which contains the CTD-CTD interface. Our studies indicate that the location of mutations on the outer surface of the CTD regions can lead to changes in membrane binding orientation and degree of membrane penetration. Direct PI(4,5)P2 interactions with the mutated residues seem to further stabilize and pull VP40 into the PM, thereby enhancing interactions with numerous amino acids that were otherwise infrequently or completely inaccessible. These multiscale computational studies provide new insights at the atomic and molecular level as to how VP40-PM interactions are altered through single amino acid mutations. Given the high case fatality rates associated with Ebola virus disease in humans, it is essential to explore the mechanisms of viral assembly in the presence of mutations to mitigate the severity of the disease and understand the potential of future outbreaks.