All-atom Model Research Articles

An all-atom model of the human cardiac sodium channel in a lipid bilayer

Voltage-gated sodium channels (NaV) are complex macromolecular proteins that are responsible for the initial upstroke of an action potential in excitable cells. Appropriate function is necessary for many physiological processes such as heartbeat, voluntary muscle contraction, nerve conduction, and neurological function. Dysfunction can have life-threatening consequences. During the past decade, there have been significant advancements with ion channel structural characterization by CryoEM, yet descriptions of cytosolic components are often lacking. Many investigations have biophysically characterized reconstituted cytosolic components and their interactions. However, extrapolating the structural alterations and allosteric communication within an intact ion channel can be challenging. To address this, we have developed an all-atom model of the human cardiac sodium channel (NaV1.5) in a lipid bilayer with explicit salt and water. Our simulations contain descriptions of cytosolic components that are poorly predicted by AlphaFold and lacking in many CryoEM structures. Leveraging the latest advancements of the Amber force fields (ff19sb and Lipid21) and water model (OPC), our simulations improved protein backbone torsion angles and generated structural information across time (four independent one-microsecond simulations). Our analysis provided descriptions of lipid and solvent contacts and insight into the C-Terminal Domain - inactivation gate and inactivation gate - latch receptor interactions.

Exploring Free Energy Landscapes for Protein Partitioning into Membrane Domains in All-Atom and Coarse-Grained Simulations.

It is known that membrane environment can impact the structure and function of integral membrane proteins. As such, elucidation of the thermodynamic driving forces governing protein partitioning between membrane domains of varying lipid composition is a fundamental topic in membrane biophysics. Molecular dynamics simulations provide valuable tools for quantitatively characterizing the free energy landscapes governing protein partitioning at the molecular level. In this study, we propose an efficient simulation methodology for the calculation of free energies for the partitioning of transmembrane proteins between liquid-disorder (Ld) and liquid-ordered (Lo) domains in all-atom (AA) phase-separated lipid bilayers. The computed potential of mean force defining the equilibrium partition coefficients is compared for AA and coarse-grained systems. Energy decomposition is used to identify differences in the underlying thermodynamics. Our findings highlight the importance of employing AA models to accurately estimate relevant free energy changes during protein translation between membrane domains.

Recent developments on multiscale simulations for rheology and complex flow of polymers

AbstractThis review summarized the multiscale simulation (MSS) methods for polymeric liquids. Since polymeric liquids have multiscale characteristics of monomeric, mesoscopic, and macroscopic flow scales, MSSs that relate different hierarchical levels are adequate to reproduce flow properties accurately. Our review includes pioneering studies to the most advanced MSS studies on rheology predictions and flow simulations of polymeric liquids. We discuss two major types of MSS methods: the bottom-up and model-embedded MSS methods. The former method mainly connects all-atom molecular dynamics models and mesoscopic models to predict rheological properties. In contrast, the latter method, where a microscopic or mesoscopic model is embedded in a macroscopic computational domain, is designed to predict macroscopic flow properties. Finally, we also discuss MSS methods using machine learning techniques. Graphical abstract

Anomalous behaviour of transport properties in a supercooled water–glycerol mixture

Glycerol acts as a natural cryoprotectant by depressing the temperature of ice nucleation and slowing down the dynamics of water mixtures. In this work, we characterise dynamics – diffusion, viscosity and hydrogen bond dynamics – as well as density anomaly and structure of water mixtures with 1%–50% (w/w) glycerol at low temperatures via molecular dynamics simulations using all-atom and coarse-grained models. Simulations reveal distinct violations of the Stokes–Einstein relation in the low-temperature regime for water and glycerol. Deviations are positive for water at all concentrations, and positive for glycerol in very dilute solutions but turning negative in concentrated ones. The all-atom and coarse-grained models reveal an unexpected crossover in the dynamics of the 1% and 10 % (w/w) glycerol at the lowest simulated temperatures. This crossover manifests in the diffusion coefficients of water and glycerol, as well as in the viscosity and lifetime of hydrogen bonds in water. We interpret that the crossover originates on the opposing dependence with glycerol concentration of the two factors controlling the solutions' slow down: the increase in tetrahedrally coordinated water and the dynamics and clustering of the glycerol molecules. We anticipate that this dynamic crossover will also occur for solution of water with other polyols.

Probing the Molecular Interactions of A22 with Prokaryotic Actin MreB and Eukaryotic Actin: A Computational and Experimental Study.

Actin is a major cytoskeletal system that mediates the intricate organization of macromolecules within cells. The bacterial cytoskeletal protein MreB is a prokaryotic actin-like protein governing the cell shape and intracellular organization in many rod-shaped bacteria, including pathogens. MreB stands as a target for antibiotic development, and compounds like A22 and its analogue, MP265, are identified as potent inhibitors of MreB. The bacterial actin MreB shares structural homology with eukaryotic actin despite lacking sequence similarity. It is currently not clear whether small molecules that inhibit MreB can act on eukaryotic actin due to their structural similarity. In this study, we investigate the molecular interactions between A22 and its analogue MP265 with MreB and eukaryotic actin through a molecular dynamics approach. Employing MD simulations and free energy calculations with an all-atom model, we unveil the robust interaction of A22 and MP265 with MreB, and substantial binding affinity is observed for A22 and MP265 with eukaryotic actin. Experimental assays reveal A22's toxicity to eukaryotic cells, including yeast and human glioblastoma cells. Microscopy analysis demonstrates the profound effects of A22 on actin organization in human glioblastoma cells. This integrative computational and experimental study provides new insights into A22's mode of action, highlighting its potential as a versatile tool for probing the dynamics of both prokaryotic and eukaryotic actins.

Impact of N-Terminal Domain Conformation and Domain Interactions on RfaH Fold Switching.

RfaH is a two-domain metamorphic protein involved in transcription regulation and translation initiation. To carry out its dual functions, RfaH relies on two coupled structural changes: Domain dissociation and fold switching. In the free state, the C-terminal domain (CTD) of RfaH adopts an all-α fold and is tightly associated with the N-terminal domain (NTD). Upon binding to RNA polymerase (RNAP), the domains dissociate and the CTD transforms into an all-β fold while the NTD remains largely, but not entirely, unchanged. We test the idea that a change in the conformation of an extended β-hairpin (β3-β4) located on the NTD, helps trigger domain dissociation. To this end, we use homology modeling to construct a structure, H1, which is similar to free RfaH but with a remodeled β3-β4 hairpin. We then use an all-atom physics-based model enhanced with a dual basin structure-based potential to simulate domain separation driven by the thermal unfolding of the CTD with NTD in a fixed, folded conformation. We apply our model to both free RfaH and H1. For H1 we find, in line with our hypothesis, that the CTD exhibits lower stability and the domains dissociate at a lower temperature T, as compared to free RfaH. We do not, however, observe complete refolding to the all-β state in these simulations, suggesting that a change in β3-β4 orientation aids in, but is not sufficient for, domain dissociation. In addition, we study the reverse fold switch in which RfaH returns from a domain-open all-β state to its domain-closed all-α state. We observe a T-dependent transition rate; fold switching is slow at low T, where the CTD tends to be kinetically trapped in its all-β state, and at high-T, where the all-α state becomes unstable. Consequently, our simulations suggest an optimal T at which fold switching is most rapid. At this T, the stabilities of both folds are reduced. Overall, our study suggests that both inter-domain interactions and conformational changes within NTD may be important for the proper functioning of RfaH.

Nucleation of multi-species crystals: methane cleatrate hydrates, a playground for classical force models

Nucleation and growth of methane clathrate hydrates is an exceptional playground to study crystallisation of multi-component, host-guest crystallites when one of the species forming the crystal, the guest, has a higher concentration in the solid than in the liquid phase. This adds problems related to the transport of the low concentration species, here methane. A key aspect in the modelling of clathrates is the water model employed in the simulation. In previous articles, we compared an all-atom force model, TIP4P/Ewald, with a coarse grain one, which is highly appreciated for its computational efficiency. Here, we perform a complementary analysis considering three all-atoms water models: TIP4P/Ewald, TIP4P/ice and TIP5P. A key difference between these models is that the former predicts a much lower freezing temperature. Intuitively, one expects that to lower freezing temperatures of water correspond to lower water/methane–methane gas–clathrate coexistence ones, which determines the degree of supercooling and the degree of supersaturation. Hence, in the simulation conditions, 250 K (500 atm, and fixed methane molar fraction), one expects computational samples made of TIP4P-ice and TIP5P, with a similar freezing temperature ( T f ∼ 273 K), to be more supersaturated with respect to the case of TIP4P-Ew ( T f ∼ 245 K), and crystallisation to be faster. Surprisingly, we find that while the nucleation rate is consistent with this prediction, growth rate with TIP4P-ice and TIP5P is much slower than with TIP4P-Ew. The latter was attributed to the slower reorientation of water molecules in strong supercooled conditions, resulting in a lower growth rate. This suggests that the freezing temperature is not a suitable parameter to evaluate the adequacy of a water model.

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.

Magnesium Regulates RNA Ring Dynamics and Folding in Subgenomic Flaviviral RNA.

Mosquito-borne flaviviruses including dengue, Zika, yellow fever, and regional encephalitis produce a large amount of short subgenomic flaviviral RNAs during infection. A segment of these RNAs named as xrRNA1 features a multi-pseudoknot (PK)-associated structure, which resists the host cell enzyme (XRN1) from degrading the viral RNA. We investigate how this long-range RNA PK folds in the presence of counterions, specifically in a mix of monovalent (K+) and divalent (Mg2+) salts at physiological concentrations. In this study, we use extensive explicit solvent molecular dynamics (MD) simulations to characterize the RNA ion environment of the folded RNA conformation, as determined by the crystal structure. This allowed us to identify the precise locations of various coordinated RNA-Mg2+ interactions, including inner-sphere/chelated and outer-sphere coordinated Mg2+. Given that RNA folding involves large-scale conformational changes, making it challenging to explore through classical MD simulations, we investigate the folding mechanism of xrRNA1 using an all-atom structure-based RNA model with a hybrid implicit-explicit treatment of the ion environment via the dynamic counterion condensation model, both with and without physiological Mg2+ concentration. The study reveals potential folding pathways for this xrRNA1, which is consistent with the results obtained from optical tweezer experiments. The equilibrium and free energy simulations both capture a dynamic equilibrium between the ring-open and ring-close states of the RNA, driven by a long-range PK interaction. Free energy calculations reveal that with the addition of Mg2+ ions, the equilibrium shifts more toward the ring-close state. A detailed analysis of the free energy pathways and ion-mediated contact probability map highlights the critical role of Mg2+ in bridging G50 and A33. This Mg2+-mediated connection helps form the long-range PK which in turn controls the transition between the ring-open and ring-close states. The study underscores the critical role of Mg2+ in the RNA folding transition, highlighting specific locations of Mg2+ contributing to the stabilization of long-range PK connections likely to enhance the robustness of Xrn1 resistance of flaviviral xrRNAs.

Exploring protein structural ensembles: Integration of sparse experimental data from electron paramagnetic resonance spectroscopy with molecular modeling methods.

Under physiological conditions, proteins continuously undergo structural fluctuations on different timescales. Some conformations are only sparsely populated, but still play a key role in protein function. Thus, meaningful structure-function frameworks must include structural ensembles rather than only the most populated protein conformations. To detail protein plasticity, modern structural biology combines complementary experimental and computational approaches. In this review, we survey available computational approaches that integrate sparse experimental data from electron paramagnetic resonance spectroscopy with molecular modeling techniques to derive all-atom structural models of rare protein conformations. We also propose strategies to increase the reliability and improve efficiency using deep learning approaches, thus advancing the field of integrative structural biology.

Effect of hydrogen constraints on predicting thermal conductivity of hydrocarbons in molecular dynamics simulation

Large overestimation has been reported while predicting the thermal conductivity of hydrocarbons using an all-atom force field model in molecular dynamics (MD) simulations. Although it has been guessed as resulting from the high-frequency vibration of the hydrogen atoms and hydrogen constraints method is suggested to be employed to reduce the deviation, how hydrogen constraints affect the heat conduction and local structure of hydrocarbons in MD simulation is still not fully understood. In this work, the effect of hydrogen constraints on the prediction of thermal conductivity of n-decane in MD simulations with the all-atom force field is studied. The results show that the deviation of the thermal conductivity of n-decane can be narrowed down by 72.48 % in simulations if the hydrogen constraints with a SHAKE algorithm is employed. The analysis of heat flux decomposition indicates that employing hydrogen constraints can reduce the contribution of transport term and non-bonded interactions to the heat flux, which in turn can help improve the accuracy while predicting the thermal conductivity in MD simulations. Furthermore, results of the vibrational density of states show that hydrogen constraints can dismiss the high-frequency vibration mode of molecules, thus effectively reducing the overestimation of thermal conductivity of hydrocarbon systems in MD simulations. The findings of this work sheds light on the molecular mechanism of heat transfer in hydrocarbon systems.

CHARMM36 All-Atom Gas Model for Lipid Nanobubble Simulation.

Lipid nanobubbles with different gas cores may integrate the biocompatibility of lipids, powerful physicochemical properties of nanobubbles, and therapeutic effects of gas molecules, which thus promote enormous biomedical applications such as ultrasound molecular imaging, gene/drug delivery, and gas therapy. In order for further more precise applications, the exact molecular mechanisms for the interactions between lipid nanobubbles and biological systems should be studied. Molecular dynamics (MD) simulation provides a powerful computational tool for this purpose. However, previous state-of-the-art MD simulations of free gas nanobubble/lipid nanobubble employed the vacuum as their gas cores, which is not suitable for studying the interactions between functional lipid nanobubbles and biological systems and revealing the biological roles of gas molecules. Hence, in this work, we developed and optimized the CHARMM36 all-atom gas parameters for six gases including N2, O2, H2, CO, CO2, and SO2, which accurately reproduced the gas density at different pressures as well as the spontaneous formation of gas nanobubbles. Subsequent applications of these gas parameters for lipid nanobubble simulations also reproduced the self-assembly process of the lipid nanobubble. We further developed a Python script to generate all-atom lipid nanobubble simulation systems, which was proven to be efficient for all-atom MD simulations of lipid nanobubbles and to be able to capture the exact dynamics of gas molecules at the gas-lipid and lipid-water interfaces of the lipid nanobubble. In summary, the all-atom gas models proposed in this work are suitable for simulating free gas nanobubbles and lipid nanobubbles, which are supposed to overcome the shortcomings of previous state-of-the-art MD simulations with the vacuum replacing the gas core and play key roles in revealing the molecular-level interactions between lipid nanobubbles and biological systems.

Understanding the structure-property relationship of anhydride-cured epoxidized vegetable oils: Modeling and molecular dynamics simulation

The development of high-performance thermosetting resins derived from epoxidized vegetable oil (EVO) has received significant attention in terms of the development of low-carbon economies. In this work, the all-atomic cross-linked models of anhydride-cured EVO were constructed and five cross-linking pathways involving distinct cure reactions were considered in the modelling process. The evolution of the number of distinct cure reactions and reactive groups of the cross-linked network under each pathway were monitored. The effects of the type of cure reaction, cure reaction probability, epoxy functionality of monomer, molar ratio of anhydride to epoxy, and anhydride structure on mass density, gelation transition, bulk properties, thermal properties, and mechanical properties of EVO-based thermosets were investigated in detail. The results show that the network exhibited minor difference in cure reactions but apparent thermo-mechanical properties of the thermosets. Etherification of epoxy groups and dehydration condensation caused the unfavorable thermo-mechanical properties. The glass transition temperature (Tg) is most affected by the molar ratio of anhydride to epoxy; whereas the Young's modulus is sensitive to the epoxy functionality of monomer. It is expected that the current work provides deep insights into the network formation and performance development of anhydride-cured EVO thermosets.

Assessment of machine learning models trained by molecular dynamics simulations results for inferring ethanol adsorption on an aluminium surface

Molecular dynamics (MD) simulations can reduce our need for experimental tests and provide detailed insight into the chemical reactions and binding kinetics. There are two challenges while dealing with MD simulations: one is the time and length scale limitations, and the latter is efficiently processing the massive amount of data resulting from the MD simulations and generating the proper reaction rates. In this work, we evaluated the use of regression machine learning (ML) methods to solve these two challenges by developing a framework for ethanol adsorption on an Aluminium (Al) slab. This framework comprises three main stages: first, an all-atom molecular dynamics model; second, ML regression models; and third, validation and testing. In stage one, the adsorption of ethanol molecules on the Al surface for various temperatures, velocities and concentrations is simulated using the large-scale atomic/molecular massively parallel simulator (LAMMPS) and ReaxFF. The outcome of stage one is utilised for training, testing, and validating the predictive models in stages two and three. We developed and evaluated 28 different ML models for predicting the number of adsorbed molecules over time, including linear regression, support vector machine (SVM), decision trees, ensemble, Gaussian process regression (GPR), neural network (NN) and Bayesian hyper-parameter optimisation models. Based on the results, the Bayesian-based GPR showed the highest accuracy and the lowest training time. The developed model can predict the number of adsorbed molecules for new cases within seconds, while MD simulations take a few weeks. This adsorption rate can then be used in macroscale simulations to tackle the time and length scale limitations. The proposed numerical framework has the potential to be generalised and, therefore, contribute to future low-cost binding reaction estimations, providing a valuable tool for industry and experimentalists.

Molecular Dynamics Calculation of Interfacial Tension in a Two-Phase Liquid Hydrocarbon–Water–Surfactant System: From Rarefied to Superdense Surfactant Monolayer

A method is proposed for calculating low interfacial tension (IFT) based on molecular dynamics simulation of systems with superdense packing of surfactant molecules at the water–liquid hydrocarbon interface. The interfacial tension was calculated by the molecular dynamics method using the all-atom and coarse-grained models in water–alkane (decane, dodecane) two-phase systems in the presence of various individual surfactants. The following ionic and nonionic surfactants were considered: sodium dodecyl sulfate (SDS), cetyltrimethylammonium chloride (CTAC), sodium dodecylbenzenesulfonate (SDBS), sodium decet-6 sulfate C10E6SO4Na, hexaethylene glycol monodecyl ether (C10E6), triethylene glycol monononadecyl ether (C19E3), and octapropoxypentaethylene glycol monododecyl ether (C12P8E5). It was shown that the interfacial tension decreases to zero when surfactant adsorption increases to the limiting values.

Classical and reactive simulations of plastic co-pyrolysis: Effects of polystyrene and its fragments on product yields, aggregation, and reaction mechanisms

Mixtures of polyethylene (PE), polypropylene (PP), polystyrene (PS), and their fragments such as ethylbenzene (EB) and linear alpha olefin (LAO) are simulated at different ratios using classical and reactive force fields. Using all-atom models validated by calculating conformations and diffusivities of differently sized PE melts in agreement with experiments, simulations are performed at 450 or 513 K that mimics a low temperature under the thermal co-pyrolysis condition, showing that the decomposition of PE into LAO influences densities and diffusivities of PE, to an extent dependent on PE lengths. In PE/PP/PS simulations, PS polymers aggregate at PS concentrations of 20 wt% or higher, leading to the separation between PS and other polymers. With the inclusion of EB that are assumed to be decomposed from PS, polymers do not aggregate. To understand reaction mechanisms, reactive simulations of PE and PS are performed, showing more heavy oil and less gas products at higher PS concentrations, in agreement with experiments, because PS fragments such as benzene, EB, and methylbenzene bind to PE and end up in heavy or light oils. In particular, the transfer of H radicals between PE and PS not only prevents further decomposition of polymers but also yields free radicals of polymers that can expedite co-pyrolysis. These findings help explain experimental observations regarding the effect of PS on liquid and gas product yields and suggest the optimal PS concentration without aggregation under the thermal co-pyrolysis condition, interpreted by reaction mechanisms of the transfer of H radicals and benzene fragments.

Tubulin Vibration Modes Are in the Subterahertz Range, and Their Electromagnetic Absorption Is Affected by Water.

Many proteins are thought to coordinate distant sites in their structures through a concerted action of global structural vibrations. However, the direct experimental spectroscopic detection of these vibration modes is rather elusive. We used normal-mode analysis to explore the dominant vibration modes of an all-atom model of the tubulin protein and described their characteristics using a large ensemble of tubulin structures. We quantified the frequency range of the normal vibrational modes to be in the subterahertz band, specifically between ∼40 and ∼160 GHz. Adding water layers to the model increases the frequencies of the low-frequency modes and narrows the frequency variations of the modes among the protein ensemble. We also showed how the electromagnetic absorption of tubulin vibration modes is affected by vibrational damping. These results contribute to our understanding of tubulin's vibrational and electromagnetic properties and provide a foundation for future attempts to control protein behavior via external electromagnetic fields.

Multiscale Modeling of Vinyl-Addition Polynorbornenes: The Effect of Stereochemistry.

Vinyl-addition polynorbornenes are candidates for designing high-performance polymers due to unique characteristics, which include a high glass transition temperature associated with a rigid backbone. Recent studies have established that the processability and properties of these polymers can be fine-tuned by using targeted substitutions. However, synthesis with different catalysts results in materials with distinct properties, potentially due to the presence of various stereoisomers that are difficult to quantify experimentally. Herein, we develop all-atom models of polynorbornene oligomers based on classical force fields and density functional theory. To establish the relationship between chemical architecture, chain conformations, and melt structure, we perform detailed molecular dynamics simulations with the fine-tuned atomistic force field and propose simpler coarse-grained descriptions to address the high molecular weight limit. All-atom simulations of oligomers suggest high glass transition temperatures in the range of 550-600 K. In the melt state (800 K), meso chains form highly rigid extended coils (C∞≈11) with amorphous structural characteristics similar to the X-ray diffraction data observed in the literature. In contrast, simulations with racemo chains predict highly helical tubular chain conformations that could promote assembly into crystalline structures.

Hydrodynamics and Aggregation of Nanoparticles with Protein Corona: Effects of Protein Concentration and Ionic Strength.

Multiple 10nm-sized anionic nanoparticles complexed with plasma proteins (human serum albumin (SA) or immunoglobulin gamma-1 (IgG)) at different ratios are simulated using all-atom and coarse-grained models. Coarse-grained simulations show much larger hydrodynamic radii of individual particles at a low protein concentration (a protein-to-particle ratio of 1) than at high protein concentrations or without proteins, indicating particle aggregation only at such a low protein concentration, in agreement with experiments. This particle aggregation is attributed to both electrostatic and hydrophobic particle-protein interactions, to an extent dependent on different proteins. In all-atom simulations, IgG proteins induce particle aggregation with and without salt, while SA proteins promote particle aggregation only in the presence of salt that can weaken the electrostatic repulsion between anionic particles closely linked via SA that is smaller than IgG, which also agree well with experiments. Besides charge interactions, hydrophobic interactions between particles and proteins are also important especially at the high salt concentration, leading to the increased particle-protein contact area. These findings help explain experimental observations regarding that the effects of protein concentration and ionic strength on particle aggregation depend on different plasma proteins, which are interpreted by binding free energies, electrostatic, and hydrophobic interactions between particles and proteins.

Wild-Type α-Synuclein Structure and Aggregation: A Comprehensive Coarse-Grained and All-Atom Molecular Dynamics Study.

α-Synuclein (α-syn) is a 140 amino acid intrinsically disordered protein (IDP) and the primary component of cytotoxic oligomers implicated in the etiology of Parkinson's disease (PD). While IDPs lack a stable three-dimensional structure, they sample a heterogeneous ensemble of conformations that can, in principle, be assessed through molecular dynamics simulations. However, describing the structure and aggregation of large IDPs is challenging due to force field (FF) accuracy and sampling limitations. To cope with the latter, coarse-grained (CG) FFs emerge as a potential alternative at the expense of atomic detail loss. Whereas CG models can accurately describe the structure of the monomer, less is known about aggregation. The latter is key for assessing aggregation pathways and designing aggregation inhibitor drugs. Herein, we investigate the structure and dynamics of α-syn using different resolution CG (Martini3 and Sirah2) and all-atom (Amber99sb and Charmm36m) FFs to gain insight into the differences and resemblances between these models. The dependence of the magnitude of protein-water interactions and the putative need for enhanced sampling (replica exchange) methods in CG simulations are analyzed to distinguish between force field accuracy and sampling limitations. The stability of the CG models of an α-syn fibril was also investigated. Additionally, α-syn aggregation was studied through umbrella sampling for the CG models and CG/all-atom models for an 11-mer peptide (NACore) from an amyloidogenic domain of α-syn. Our results show that despite the α-syn structures of Martini3 and Sirah2 with enhanced protein-water interactions being similar, major differences exist concerning aggregation. The Martini3 fibril is not stable, and the binding free energy of α-syn and NACore is positive, opposite to Sirah2. Sirah2 peptides in a zwitterionic form, in turn, display termini interactions that are too strong, resulting in end-to-end orientation. Sirah2, with enhanced protein-water interactions and neutral termini, provides, however, a peptide aggregation free energy profile similar to that found with all-atom models. Overall, we find that Sirah2 with enhanced protein-water interactions is suitable for studying protein-protein and protein-drug aggregation.