All-atom Molecular Simulations Research Articles

Combined Physics- and Machine-Learning-Based Method to Identify Druggable Binding Sites Using SILCS-Hotspots.

Identifying druggable binding sites on proteins is an important and challenging problem, particularly for cryptic, allosteric binding sites that may not be obvious from X-ray, cryo-EM, or predicted structures. The Site-Identification by Ligand Competitive Saturation (SILCS) method accounts for the flexibility of the target protein using all-atom molecular simulations that include various small molecule solutes in aqueous solution. During the simulations, the combination of protein flexibility and comprehensive sampling of the water and solute spatial distributions can identify buried binding pockets absent in experimentally determined structures. Previously, we reported a method for leveraging the information in the SILCS sampling to identify binding sites (termed Hotspots) of small mono- or bicyclic compounds, a subset of which coincide with known binding sites of drug-like molecules. Here, we build on that physics-based approach and present a ML model for ranking the Hotspots according to the likelihood they can accommodate drug-like molecules (e.g., molecular weight >200 Da). In the independent validation set, which includes various enzymes and receptors, our model recalls 67% and 89% of experimentally validated ligand binding sites in the top 10 and 20 ranked Hotspots, respectively. Furthermore, we show that the model's output Decision Function is a useful metric to predict binding sites and their potential druggability in new targets. Given the utility the SILCS method for ligand discovery and optimization, the tools presented represent an important advancement in the identification of orthosteric and allosteric binding sites and the discovery of drug-like molecules targeting those sites.

Bridging the Gap: Unraveling the Mechanisms of Nanobubble Detachment from Electrode Surfaces

Gas evolution reactions play a fundamental role in the functionality of various electrochemical devices. Recent experimental studies have provided valuable insights into the behavior of gas bubble formation on nanoelectrodes; however, the experiments have limited spatial resolution to fully understand the underlying mechanisms governing the formation of nanobubbles and their stability on the surface as well as the effects of nanoscopic surface defects on their behavior. A comprehensive understanding of how bubbles behave on electrode surfaces and in the electrolyte is essential for optimizing and designing electrochemical processes. In this work, we employ all-atom molecular simulations to explore the effects of surface chemistry and defect geometry on detachment behavior of hydrogen gas bubbles on nickel electrodes at molecular level. Analyzing the dynamics of bubble detachment on the surfaces with different chemistries reveals that the rate of bubble detachment is higher on the strongly hydrophilic surface compared to the surface exhibiting weak hydrophilicity. In addition, our findings indicate that the defect geometry has significant effects on the detachment of bubbles from the surface. Specifically, the width and depth of canonical-shaped defects are demonstrated to be critical parameters affecting the behavior of the hydrogen bubbles on the nickel surface. Overall, our results show there is a complex balance between the degree of surface wettability and surface defects, determining the stability of nanobubble formation on the electrode surfaces. The results of this study could enhance our understanding of bubble detachment dynamics on the electrode surfaces, providing valuable insights for the future design of practical applications.This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC5207NA27344. Release: LLNL-ABS-858292

A molecular dynamics investigation into the polymer tackifiers in supercritical CO2 fracturing fluids under wellbore conditions

SC-CO2 fracturing technology is a feasible method for green extraction of unconventional oil and gas reservoirs. However, due to a series of problems such as sand transportation during SC-CO2 fracturing, CO2 thickening has become the focus of current research. Polymer thickeners have been widely used in oilfields. Due to the macroscopic nature and limitations of laboratory experiments, we have not been able to elucidate the microscopic mechanism of polymer thickening CO2 and the viscosity change of the CO2 fracturing fluid system downhole after polymer addition. In this study, we used all-atom modeling and molecular simulation of the COMPASSII force field. We selected nine polymers to study their microscopic properties and the factors affecting their effects. Finally, we simulated the viscosity change of the CO2 system after the addition of polymer tackifiers in the wellbore under the CO2 fracturing construction conditions by combining the data from one well in the oilfield.

Synthetic Force-Field Database for Training Machine Learning Models to Predict Mobility-Preserving Coarse-Grained Molecular-Simulation Potentials.

Balancing accuracy and efficiency is a common problem in molecular simulation. This tradeoff is evident in coarse-grained molecular dynamics simulation, which prioritizes efficiency, and all-atom molecular simulation, which prioritizes accuracy. Despite continuous efforts, creating a coarse-grained model that accurately captures both the system's structure and dynamics remains elusive. In this article, we present a data-driven approach for constructing coarse-grained models that aim to describe both the structure and dynamics of the system equally well. While the development of machine learning models is well-received in the scientific community, the significance of dataset creation for these models is often overlooked. However, data-driven approaches cannot progress without a robust dataset. To address this, we construct a database of synthetic coarse-grained potentials generated from unphysical all-atom models. A neural network is trained with the generated database to predict the coarse-grained potentials of real liquids. We evaluate their quality by calculating the combined loss of structural and dynamical accuracy upon coarse-graining. When we compare our machine learning-based coarse-grained potential with the one from iterative Boltzmann inversion, the machine learning prediction turns out better for all eight hydrocarbon liquids we studied. As all-atom surfaces turn more nonspherical, both ways of coarse-graining degrade. Still, the neural network outperforms iterative Boltzmann inversion in constructing good quality coarse-grained models for such cases. The synthetic database and the developed machine learning models are freely available to the community, and we believe that our approach will generate interest in efficiently deriving accurate coarse-grained models for liquids.

Disjoining Pressure Decay Length in Room-Temperature Ionic Liquids: A Molecular Simulation Study.

We present all-atom molecular simulations to investigate the behavior of 1-ethyl-3-methylimidazolium tetrafluoroborate ([EMIM][BF4]) in negatively charged carbon nanopores of different widths (h = 5÷15 nm) and lengths (l = 4÷10 nm). The goal of our study was to determine how the disjoining pressure varies as a function of the pore width at different lengths and to understand the influence of edge effects. Our results show that the edge effect decreases as the pore length increases. Using an exponential function, we can approximate the disjoining pressure at large pore widths and use this approximation to estimate the decay length that can correlate with the electrostatic screening length. The results agreed well with those of previous experimental studies.

How alginate lyase produces quasi-monodisperse oligosaccharides: A normal-mode-based docking and molecular dynamics simulation study

Oligosaccharide degradation products of alginate (AOS) hold significant potential in diverse fields, including pharmaceuticals, health foods, textiles, and agricultural production. Enzymatic alginate degradation is appealing due to its mild conditions, predictable activity, high yields, and controllability. However, the alginate degradation often results in a complex mixture of oligosaccharides, necessitating costly purification to isolate highly active oligosaccharides with a specific degree of polymerization (DP). Addressing this, our study centers on the alginate lyase AlyB from Vibrio Splendidus OU02, which uniquely breaks down alginate into mono-distributed trisaccharides. This enzyme features a polysaccharide lyase family 7 domain (PL-7) and a CBM32 carbohydrate-binding module connected by a helical structure. Through normal-mode-based docking and all-atom molecular simulations, we demonstrate that AlyB's substrate and product specificities are influenced by the spatial conformation of the catalytic pocket and the flexibility of its structure. The helically attached CBM is pivotal in releasing trisaccharides, which is crucial for avoiding further degradation. This study sheds light on AlyB's specificity and efficiency and contributes to the evolving field of enzyme design for producing targeted oligosaccharides, with significant implications for various bioindustries.

Conformational Dynamics of the Most Efficient Carboxylase Contributes to Efficient CO2 Fixation.

Crotonyl-CoA carboxylase/reductase (Ccr) is one of the fastest CO2 fixing enzymes and has become part of efficient artificial CO2-fixation pathways in vitro, paving the way for future applications. The underlying mechanism of its efficiency, however, is not yet completely understood. X-ray structures of different intermediates in the catalytic cycle reveal tetramers in a dimer of dimers configuration with two open and two closed active sites. Upon binding a substrate, this active site changes its conformation from the open state to the closed state. It is challenging to predict how these coupled conformational changes will alter the CO2 binding affinity to the reaction's active site. To determine whether the open or closed conformations of Ccr affect binding of CO2 to the active site, we performed all-atom molecular simulations of the various conformations of Ccr. The open conformation without a substrate showed the highest binding affinity. The CO2 binding sites are located near the catalytic relevant Asn81 and His365 residues and in an optimal position for CO2 fixation. Furthermore, they are unaffected by substrate binding, and CO2 molecules stay in these binding sites for a longer time. Longer times at these reactive binding sites facilitate CO2 fixation through the nucleophilic attack of the reactive enolate in the closed conformation. We previously demonstrated that the Asn81Leu variant cannot fix CO2. Simulations of the Asn81Leu variant explain the loss of activity through the removal of the Asn81 and His365 binding sites. Overall, our findings show that the conformational dynamics of the enzyme controls CO2 binding. Conformational changes in Ccr increase the level of CO2 in the open subunit before the substrate is bound, the active site closes, and the reaction starts. The full catalytic Ccr cycle alternates among CO2 addition, conformational change, and chemical reaction in the four subunits of the tetramer coordinated by communication between the two dimers.

Glass Transition and Structure of Organic Polymers from All-Atom Molecular Simulations

Glass Transition and Structure of Organic Polymers from All-Atom Molecular Simulations

Chain-Increment Approach to the Mutual Miscibility of Polymers with All-Atom Molecular Simulation

The mutual miscibility of polymers was addressed with all-atom molecular dynamics (MD) simulation and the energy-representation theory of solvation. The chain-increment method was extended to polymer mixtures, and the mixing free energy was related to the incremental free energy for the constituent polymer species that is the free-energy change of turning on the interaction of a monomer in the tagged polymer chain with the surrounding molecules. The free energy was then computed for the mixing of polyethylene (PE) and poly(vinylidene difluoride) (PVDF) and of poly(vinyl alcohol) (PVA) and polyvinylpyrrolidone (PVP). It was seen that the incremental free energy is insensitive to the location of the monomer in the inner part of the polymer chain and depends on the mixing ratio of the polymer system in parallel with the average interaction energy of the incremented monomer with the surrounding polymers. The intermolecular interactions of each polymer species were further analyzed at atomic resolution. The PVDF–PVDF interaction was found to become less favorable (more positive) upon addition of PE in the mixture of PE and PVDF, making them immiscible with each other. PVP favors PVA more than PVP, on the other hand, leading to the miscibility of PVA and PVP. The connection of our formulation of the mixing free energy with the Flory–Huggins expression is also described.

Integrated Covalent Drug Design Workflow Using Site Identification by Ligand Competitive Saturation.

Covalent drug design is an important component in drug discovery. Traditional drugs interact with their target in a reversible equilibrium, while irreversible covalent drugs increase the drug-target interaction duration by forming a covalent bond with targeted residues and thus may offer a more effective therapeutic approach. To facilitate the design of this class of ligands, computational methods can be used to help identify reactive nucleophilic residues, frequently cysteines, on a target protein for covalent binding, to test various warhead groups for their potential reactivities, and to predict noncovalent contributions to binding that can facilitate drug-target interactions that are important for binding specificity. To further aid covalent drug design, we extended a functional group mapping approach based on explicit solvent all-atom molecular simulations (SILCS: site identification by ligand competitive saturation) that intrinsically considers protein flexibility, functional group, and protein desolvation along with functional group-protein interactions. Through docking of a library of representative warhead fragments using SILCS-Monte Carlo (SILCS-MC), reactive cysteines can be correctly identified for proteins being tested. Furthermore, a machine learning model was trained to quantify the effectiveness of various warhead groups for proteins using metrics from SILCS-MC as well as experimental model compound warhead reactivity data. The ability to rank covalent molecular binders with similar warheads using SILCS ligand grid free energy (LGFE) ranking was also tested for several proteins. Based on these tools, an integrated SILCS-based workflow was developed, named SILCS-Covalent, which can both qualitatively and quantitatively inform covalent drug discovery.

Using 2'-deoxy-ADP to probe stability of the myosin interacting heads motif at atomic resolution.

In addition to active cycling states, myosins within the thick filament can access an ‘inactive’ conformation called the interacting heads motif (IHM) that has been associated with the energy-conserving super relaxed (SRX) state of muscle. Thus, accessing the IHM conformation is a means of thick filament-based regulation of muscle. Only a handful of cryo-EM structures of IHM myosin are available. The large size of the IHM has similarly challenged all-atom molecular simulations of the structure. Using ANTON2, we have performed explicit solvent, all-atom molecular dynamics simulations of human cardiac β-myosin in the IHM confirmation on the microsecond timescale. In recent experimental studies, the small molecule ATP analogue, 2’-deoxy-ATP (dATP), has been shown to destabilize myosin heads from the IHM into disordered, more active states. To complement these experimental studies, we simulated β-myosin in the IHM conformation in which ADP.Pi was replaced by dADP.Pi. These simulations showed that dADP reduced the stability of the IHM by reducing the number of interactions between S1 heads. They also show that the tails of dADP.Pi-bound heads adopt conformations distinct from existing atomic models obtained with cryoEM. Thus, simulations suggest that dynamics in the RLC-binding region of the tail and at the head-head interface both contribute to IHM stability. Further, they suggest that departure from the IHM state involves coordinated motions in regions of myosin separated by over 100 Å. Ongoing analysis of the degree to which altered dynamics within the nucleotide binding pocket influence the conformation of both the head interface and RLC-binding region of the tail. These novel simulations should also prompt further research into the contribution of tail dynamics into IHM stability as well as interest in mutations that influence tail dynamics.

Linking the outer membrane composition of salmonella to protective antibody responses.

Invasive non-typhoidal Salmonella (iNTS) infection has a high risk of fatality. Currently, there is no licensed vaccine against iNTS, making creating an effective vaccine priority for regions where the rate of these infections is high. In particular, an ideal vaccine should be protective against multiple serovars of Salmonella. The correlation of the protective properties of antibodies raised against Salmonella with distinct types and lengths of O-antigen repeats and membrane proteins has not been thoroughly investigated yet, but experiments conducted on mice have revealed some important differences. We hypothesize that these differences can be explained by the physical structure of the bacterial outer envelope and the accessibility of proteinaceous epitopes to antibodies. To test this hypothesis, we modeled the outer membrane for two of the most common iNTS serovars, Salmonella enterica Enteritidis (SEn) and Typhimurium (STm), along with the outer membrane proteins OmpD and OmpA. We set up all-atomic molecular simulations (MD) for both proteins separately for the serotypes STm and SEn, and for LPS with different lengths of O4 and O9 O-Ag repeats, giving, overall, 20 systems for 1.5 microseconds each. From these MD simulations, we measured various quantities, including Solvent Accessible Surface Area (SASA) of extracellular loops of the proteins, root-mean-square fluctuation (RMSF) of the residues, and the protein to O-Ag contact area, in order to explain the molecular underpinnings of differential antibody responses being raised by distinct serotypes.

Quantifying photosynthetic metabolites permeability across bacterial microcompartments using computational microscopy

Carboxysomes are Bacterial microcompartments, found in cyanobacteria that locally concentrate carbon dioxide (CO2) to improve the efficiency of the enzyme RuBisCO, a key step towards photosynthetic carbon fixation by the Calvin-Benson-Bassham (CBB) cycle. The carboxysome shell is made up of proteins that encapsulate RuBisCO and carbonic anhydrase. Carbonic anhydrase converts soluble bicarbonate to CO2, increasing the local concentration of CO2 for RuBisCO. In addition to CO2, other metabolites such as oxygen (O2), bicarbonate (HCO3−), 3-phosphoglyceric acid (3-PGA) and ribulose-1,5-bisphosphate (RuBP) also need to permeate through the carboxysome shell to efficiently perform CO2 fixation. Quantifying the permeability of the shell for these metabolites currently remains a challenge and one of the critical design features related to the design of synthetic bacterial microcompartments for metabolic and sustainablebioengineering applications. Leveraging a high resolution cryo-electron microscopy structure of a synthetic beta-carboxysome shell and new graphics processing unit (GPU)-resident molecular simulation engines, we determine the permeability of photosynthetic metabolites across synthetic carboxysome shell through unbiased molecular simulation at all-atom resolution. We find that the carboxysome itself is not selectively permeable to bicarbonate over carbon dioxide, as originally hypothesized. Instead, the carboxysome shell proteins form a general barrier to maintain the carbon dioxide gradient generated by carbonic anhydrase activity within the carboxysome, compensating for inefficiencies of endogenous RuBisCO, specifically slow turnover rate, and photorespiration. The results from all-atom molecular simulations presented here provide a detailed mechanistic picture, using the shell permeabilities to engineer smart synthetic bacterial microcompartments and to improve photosynthetic efficiency with a wide range of applications in sustainable, metabolic, agricultural, and biomedical engineering.

Induced ion correlations lead to DNA attraction in manganese.

Divalent counterions show the ion specificity effect. In a free solution, Mn2+ ions can condense DNA into a disorder bundle of structures while the same valence Mg2+ ions dissolve DNA assemblies. Theoretical models were proposed to explain the Mn2+-induced condensation of DNA with limited success. Here, we employ all-atom molecular simulations to investigate the ion specificity effect on DNA-DNA interactions. An exhaustive sampling of inter-helical distance and angular orientations afforded by metadynamics simulations allowed us to construct the free energy surface of the DNA-DNA interactions. In excellent agreement with experiments, we observe that dsDNA strands show attraction in the presence of Mn2+, while they remain repulsive at the same ionic strength in MgCl2. To explain the disparate behavior of the two divalent cations, we investigate cation distributions and preferential binding sites. We observe significant differences in binding modes between Mn2+ and Mg2+. The manganese ions bind prevalently to the phosphate backbone, while magnesium ions occupy both backbone and the major groove of the DNA. We develop a simple like-charge attraction model based on the preferential binding sites derived from simulations. Our model explains the observed differences between the two cations successfully. It highlights the importance of induced ion correlations between adjacent pairs as the mechanism leading to attraction. Our results shed light into Mn2+ induced like charge attraction mechanism of DNA. The method can be extended to study other condensing agents.

Unveiling the interdomain dynamics of type II DNA topoisomerase through all-atom simulations: Implications for understanding its catalytic cycle

Type IIA DNA topoisomerases are complex molecular nanomachines that manage topological states of the DNA molecule in the cell and play a crucial role in cellular processes such as cell division and transcription. They are also established targets of cancer chemotherapy. Starting from the available crystal structure of a fully catalytic topoisomerase IIA homodimer from Saccharomyces cerevisiae, we constructed three states of this molecular motor primarily changing the configurations of the DNA segment bound in the DNA gate and performed μs-long all-atom molecular simulations. A comprehensive analysis revealed a sliding motion within the DNA gate and a teamwork between the N-gate and DNA gate that may be associated with the necessary molecular events that allow passage of the T-segment of DNA. The observed movement of the ATPase dimer relative to the DNA domain was reflected in different interaction patterns between the K-loops of the transducer domain and the B-A-B form of the bound DNA. Based on the obtained results, we mapped simulated configurations to the structures in the proposed catalytic cycle through which type IIA topoisomerases exert their function and discussed the possible transition events. The results extend our understanding of the mechanism of action of type IIA topoisomerases and provide an atomistic interpretation of some of the observed features of these molecular motors.

Atomistic Pictures of Self-Assembled Helical Peptide Nanofibers.

Spontaneous self-assembly of peptides has been at the forefront of supramolecular chemistry and materials science research over the last two decades. Despite the wealth of information on the morphology of the assembled objects, atomic resolution details of molecular arrangements inside them are largely unknown. In this paper, we investigated non-covalent assemblies of zwitterionic l-phenylalanine tripeptides in water using all-atom explicit-solvent molecular dynamics computer simulations. Our studies produced atomistic pictures of spontaneously assembled nanofibers composed of hundreds of peptide molecules. The dimensions of the nanofibers varied from 10 to 18 nm, with irregular helical twists along the long axes. Previously published experimental data, acquired under similar conditions, provided direct validation of the fibrous morphology and indirect support for the non-trivial helicity observed in our simulations. Quantitative analyses of peptide-water and peptide-peptide interactions revealed heterogeneous local environments of molecules across the nanometer length scales. The combination of electrostatic, hydrogen bonding, van der Waals, and hydrophobic interactions, adopted by a single molecule, was dependent on its relative position inside the fiber. Despite the presence of three hydrophobic phenyl groups, very few molecules were found to be completely shielded from the surrounding water, indicating a subtle role of the hydrophobic effect. Limited conformational flexibility of the tripeptide, along with bare electrostatic interactions, appeared to play a crucial role in the emergence of fibrous morphology of the nanostructures. Our analyses led us to formulate plausible qualitative explanations of the assembly behavior in terms of thermodynamic driving forces and kinetic considerations. We established a clear relationship between details of chemical interactions operating within few molecules and characteristics of the self-assembled states at much longer length scales.

Small molecules targeting the disordered transactivation domain of the androgen receptor induce the formation of collapsed helical states

Intrinsically disordered proteins, which do not adopt well-defined structures under physiological conditions, are implicated in many human diseases. Small molecules that target the disordered transactivation domain of the androgen receptor have entered human trials for the treatment of castration-resistant prostate cancer (CRPC), but no structural or mechanistic rationale exists to explain their inhibition mechanisms or relative potencies. Here, we utilize all-atom molecular dynamics computer simulations to elucidate atomically detailed binding mechanisms of the compounds EPI-002 and EPI-7170 to the androgen receptor. Our simulations reveal that both compounds bind at the interface of two transiently helical regions and induce the formation of partially folded collapsed helical states. We find that EPI-7170 binds androgen receptor more tightly than EPI-002 and we identify a network of intermolecular interactions that drives higher affinity binding. Our results suggest strategies for developing more potent androgen receptor inhibitors and general strategies for disordered protein drug design.

Role of Repeated Conformational Transitions in Substrate Binding of Adenylate Kinase.

The catalytic cycle of the enzyme adenylate kinase involves large conformational motions between open and closed states. A previous single-molecule experiment showed that substrate binding tends to accelerate both the opening and the closing rates and that a single turnover event often involves multiple rounds of conformational switching. In this work, we showed that the repeated conformational transitions of adenylate kinase are essential for the relaxation of incorrectly bound substrates into the catalytically competent conformation by combining all-atom and coarse-grained molecular simulations. In addition, free energy calculations based on all-atom and coarse-grained models demonstrated that the enzyme with incorrectly bound substrates has much a lower free energy barrier for domain opening compared to that with the correct substrate conformation, which may explain the the acceleration of the domain opening rate by substrate binding. The results of this work provide mechanistic understanding to previous experimental observations and shed light onto the interplay between conformational dynamics and enzyme catalysis.

Catalytic Mechanism of ATP Hydrolysis in the ATPase Domain of Human DNA Topoisomerase IIα.

Human DNA topoisomerase IIα is a biological nanomachine that regulates the topological changes of the DNA molecule and is considered a prime target for anticancer drugs. Despite intensive research, many atomic details about its mechanism of action remain unknown. We investigated the ATPase domain, a segment of the human DNA topoisomerase IIα, using all-atom molecular simulations, multiscale quantum mechanics/molecular mechanics (QM/MM) calculations, and a point mutation study. The results suggested that the binding of ATP affects the overall dynamics of the ATPase dimer. Reaction modeling revealed that ATP hydrolysis favors the dissociative substrate-assisted reaction mechanism with the catalytic Glu87 serving to properly position and polarize the lytic water molecule. The point mutation study complemented our computational results, demonstrating that Lys378, part of the important QTK loop, acts as a stabilizing residue. The work aims to pave the way to a deeper understanding of these important molecular motors and to advance the development of new therapeutics.

Density and viscosity of liquid mixtures formed by n-hexane, ethanol, and cyclopentyl methyl ether

• Experimental data of density and viscosity of liquid mixtures. • Theoretical modeling of density and viscosity of liquid mixtures. • Equilibrium Molecular Dynamics of density and viscosity of liquid mixtures • SAFT VR Mie EoS couple to FVT for ternary mixtures. Experimental determination, theoretical modeling, and molecular simulation have been combined to describe atmospherical density and viscosity for liquid n-hexane, ethanol, and cyclopentyl methyl ether pure compounds as a function of temperature and all the corresponding binary and ternary liquid mixtures at 298.15 K over the whole mole fraction range. Experimental determinations are carried out using a Stabinger viscosimeter. The reported density and viscosity data are modeled with the Statistical Associating Fluid Theory of Variable Range employing a Mie potential (SAFT-VR-Mie) equation of state coupled to the free volume theory (FVT). All-Atom molecular simulations employ equilibrium molecular dynamics (EMD) to obtain the corresponding densities from Isobaric-Isothermal (NPT) ensemble and viscosities from the microcanonical (NVE) and using the Green–Kubo formulation. According to the results, density and viscosity decrease with temperature for pure liquid fluids. For the case of liquid mixtures, densities and viscosities negatively deviate from the corresponding linear trend, and no ternary stationary points are detected under the explored conditions. The experimental data of both properties in binary mixtures are well correlated by a Redlich–Kister polynomial, and the ternary mixture is acceptably correlated by only using binary parameters. From a theoretical perspective, densities of binary and ternary mixtures are excellently predicted from the SAFT-VR-Mie EoS, whereas viscosities of the binary mixtures are well correlated from SAFT-VR-Mie EoS + FVT, while the viscosities of the ternary mixture are well predicted using the same framework. EMD accurately describes the density of pure fluids and mixtures, but it exhibits more significant deviations in viscosity predictions. Moreover, the qualitative viscosity trend with temperature and composition is maintained. Finally, SAFT-VR-Mie and EMD are used to describe the hydrogen bond (HB) network formed within the mixtures at different alcohol compositions, which is also used to validate the selected association scheme. HB formation varies with respect to ethanol concentration, and the results show a negative deviation from compositions linearity, showing good agreement between both approaches.