Coarse-grained Molecular Model Research Articles

Chain-Length Dependence of Peptide-Lipid Bilayer Interaction Strength and Binding Kinetics: A Combined Theoretical and Experimental Approach.

Physical interactions between polypeptide chains and lipid membranes underlie critical cellular processes. Yet, despite fundamental importance, key mechanistic aspects of these interactions remain elusive. Bulk experiments have revealed a linear relationship between free energy and peptide chain length in a model system, but does this linearity extend to the interaction strength and to the kinetics of lipid binding? To address these questions, we utilized a combination of coarse-grained molecular dynamics (CG MD) simulations, analytical modeling, and atomic force microscopy (AFM)-based single molecule force spectroscopy. Following previous bulk experiments, we focused on interactions between short hydrophobic peptides (WLn, n = 1, ..., 5) with 1-palmitoyl-2-oleoyl-glycero-3-phosphocholine (POPC) bilayers, a simple system that probes peptide primary structure effects. Potentials of mean force extracted from CG MD recapitulated the linearity of free energy with the chain length. Simulation results were quantitatively connected to bulk biochemical experiments via a single scaling factor of order unity, corroborating the methodology. Additionally, CG MD revealed an increase in the distance to the transition state, a result that weakens the dependence of the dissociation force on the peptide chain length. AFM experiments elucidated rupture force distributions and, through modeling, intrinsic dissociation rates. Taken together, the analysis indicates a rupture force plateau in the WLn-POPC system, suggesting that the final rupture event involves the last 2 or 3 residues. In contrast, the linear dependence on chain length was preserved in the intrinsic dissociation rate. This study advances the understanding of peptide-lipid interactions and provides potentially useful insights for the design of peptides with tailored membrane-interacting properties.

Coarse-grained molecular dynamics model of AB diblock copolymers composed of blocks with different Lennard-Jones parameters forming lamellar structures

In thermoplastic elastomers, the structure formation of hard and soft domains and the mechanical response to deformation remain vital areas of interest. To realize the coarse-grained molecular dynamics (CGMD) simulations of systems incorporating the difference in glass transition temperature Tg between domains, we studied AB diblock copolymers (di-BCPs) with asymmetric Lennard-Jones (LJ) parameters (the strength coefficient εLJ and cutoff length rc). For instance, hard and soft domains were realized by assigning the cutoff lengths of each segment to rc,(A,A) = 2.5 (high Tg) and rc,(B,B) = 1.5 (low Tg), respectively. To identify how the cutoff length rc,(B,B) corresponds to the strength coefficient ε(B,B) in the phase separation behavior against the fixed LJ parameters for the A block (rc,(A,A) = 2.5 and ε(A,A) = 1.0), we focused on lamellar structures in which the A and B blocks possess equal segment numbers. For the asymmetric cases with rc,(B,B) < rc,(A,A), we estimated the effective ε(B,B)' on the basis of the DA/DB ratio of the partial domain spacings. We confirmed that the scaling relations (D ∼ Δε1/6, D/N0.7 ∼ Δε, and D/N1/2 ∼ ΔεN) hold, where ε(A,A) = 1, Δε = ((1 + ε(B,B)')/2 – ε(A,B)), and N denotes the number of segments per AB di-BCP chain. Therefore, this work will provide valuable insights for guiding applied research on BCPs.

Predicting the artificial dynamical acceleration of binary hydrocarbon mixtures upon coarse-graining with roughness volumes and simple averaging rules.

Coarse-grained (CG) molecular models greatly reduce the computational cost of simulations allowing for longer and larger simulations, but come with an artificially increased acceleration of the dynamics when compared to the parent atomistic (AA) simulation. This impedes their use for the quantitative study of dynamical properties. During coarse-graining, grouping several atoms into one CG bead not only reduces the number of degrees of freedom but also reduces the roughness on the molecular surfaces, leading to the acceleration of dynamics. The RoughMob approach [M. K. Meinel and F. Müller-Plathe, J. Phys. Chem. B 126(20), 3737-3747 (2022)] quantifies this change in geometry and correlates it to the acceleration by making use of four so-called roughness volumes. This method was developed using simple one-bead CG models of a set of hydrocarbon liquids. Potentials for pure components are derived by the structure-based iterative Boltzmann inversion. In this paper, we find that, for binary mixtures of simple hydrocarbons, it is sufficient to use simple averaging rules to calculate the roughness volumes in mixtures from the roughness volumes of pure components and add a correction term quadratic in the concentration without the need to perform any calculation on AA or CG trajectories of the mixtures themselves. The acceleration factors of binary diffusion coefficients and both self-diffusion coefficients show a large dependence on the overall acceleration of the system and can be predicted a priori without the need for any AA simulations within a percentage error margin, which is comparable to routine measurement accuracies. Only if a qualitatively accurate description of the concentration dependence of the binary diffusion coefficient is desired, very few additional simulations of the pure components and the equimolar mixture are required.

Ultrasound enhanced diffusion in hydrogels: An experimental and non-equilibrium molecular dynamics study.

Focused ultrasound has experimentally been found to enhance the diffusion of nanoparticles; our aim with this work is to study this effect closer using both experiments and non-equilibrium molecular dynamics. Measurements from single particle tracking of 40nm polystyrene nanoparticles in an agarose hydrogel with and without focused ultrasound are presented and compared with a previous experimental study using 100nm polystyrene nanoparticles. In both cases, we observed an increase in the mean square displacement during focused ultrasound treatment. We developed a coarse-grained non-equilibrium molecular dynamics model with an implicit solvent to investigate the increase in the mean square displacement and its frequency and amplitude dependencies. This model consists of polymer fibers and two sizes of nanoparticles, and the effect of the focused ultrasound was modeled as an external oscillating force field. A comparison between the simulation and experimental results shows similar mean square displacement trends, suggesting that the particle velocity is a significant contributor to the observed ultrasound-enhanced mean square displacement. The resulting diffusion coefficients from the model are compared to the diffusion equation for a two-time continuous time random walk. The model is found to have the same frequency dependency. At lower particle velocity amplitude values, the model has a quadratic relation with the particle velocity amplitude as described by the two-time continuous time random walk derived diffusion equation, but at higher amplitudes, the model deviates, and its diffusion coefficient reaches the non-hindered diffusion coefficient. This observation suggests that at higher ultrasound intensities in hydrogels, the non-hindered diffusion coefficient can be used.

C9orf72 polyPR directly binds to various nuclear transport components

The disruption of nucleocytoplasmic transport (NCT) is an important mechanism in neurodegenerative diseases. In the case of C9orf72-ALS, trafficking of macromolecules through the nuclear pore complex (NPC) might get frustrated by the binding of C9orf72-translated arginine-containing dipeptide repeat proteins (R-DPRs) to the Kapβ family of nuclear transport receptors. Besides Kapβs, several other types of transport components have been linked to NCT impairments in R-DPR-expressed cells, but the molecular origin of these observations has not been clarified. Here, we adopt a coarse-grained molecular dynamics model at amino acid resolution to study the direct interaction between polyPR, the most toxic DPR, and various nuclear transport components to elucidate the binding mechanisms and provide a complete picture of potential polyPR-mediated NCT defects. We found polyPR to directly bind to several isoforms of the Impα family, CAS (the specific exporter of Impα) and RanGAP. We observe no binding between polyPR and Ran. Longer polyPRs at lower salt concentrations also make contact with RanGEF and NTF2. Analyzing the polyPR contact sites on the transport components reveals that polyPR potentially interferes with RanGTP/RanGDP binding, with nuclear localization signal (NLS)-containing cargoes (cargo-NLS) binding to Impα, with cargo-NLS release from Impα, and with Impα export from the nucleus. The abundance of polyPR-binding sites on multiple transport components combined with the inherent polyPR length dependence makes direct polyPR interference of NCT a potential mechanistic pathway of C9orf72 toxicity.

C9orf72 polyPR directly binds to various nuclear transport components.

The disruption of nucleocytoplasmic transport (NCT) is an important mechanism in neurodegenerative diseases. In the case of C9orf72-ALS, trafficking of macromolecules through the nuclear pore complex (NPC) might get frustrated by the binding of C9orf72-translated arginine-containing dipeptide repeat proteins (R-DPRs) to the Kapβ family of nuclear transport receptors. Besides Kapβs, several other types of transport components have been linked to NCT impairments in R-DPR-expressed cells, but the molecular origin of these observations has not been clarified. Here, we adopt a coarse-grained molecular dynamics model at amino acid resolution to study the direct interaction between polyPR, the most toxic DPR, and various nuclear transport components to elucidate the binding mechanisms and provide a complete picture of potential polyPR-mediated NCT defects. We found polyPR to directly bind to several isoforms of the Impα family, CAS (the specific exporter of Impα) and RanGAP. We observe no binding between polyPR and Ran. Longer polyPRs at lower salt concentrations also make contact with RanGEF and NTF2. Analyzing the polyPR contact sites on the transport components reveals that polyPR potentially interferes with RanGTP/RanGDP binding, with nuclear localization signal (NLS)-containing cargoes (cargo-NLS) binding to Impα, with cargo-NLS release from Impα, and with Impα export from the nucleus. The abundance of polyPR-binding sites on multiple transport components combined with the inherent polyPR length dependence makes direct polyPR interference of NCT a potential mechanistic pathway of C9orf72 toxicity.

Dissipation and recovery in collagen fibrils under cyclic loading: A molecular dynamics study.

The hysteretic behavior exhibited by collagen fibrils, when subjected to cyclic loading, is known to result in both dissipation as well as accumulation of residual strain. On subsequent relaxation, partial recovery has also been reported. Cross-links have been considered to play a key role in overall mechanical properties. Here, we modify an existing coarse-grained molecular dynamics model for collagen fibril with initially cross-linked collagen molecules, which is known to reproduce the response to uniaxial strain, by incorporating reformation of cross-links to allow for possible recovery of the fibril. Using molecular dynamics simulations, we show that our model successfully replicates the key features observed in experimental data, including the movement of hysteresis loops, the time evolution of residual strains and energy dissipation, as well as the recovery observed during relaxation. We also show that the characteristic cycle number, describing the approach toward steady state, has a value similar to that in experiments. We also emphasize the vital role of the degree of cross-linking on the key features of the macroscopic response to cyclic loading.

Ion Conductivity in Salt-Doped Polymers: Combined Effects of Temperature and Salt Concentration.

We construct a coarse-grained molecular dynamics model based on poly(ethylene oxide) and lithium bis(trifluoromethane)sulfonimide salt to examine the combined effects of temperature and salt concentration on the transport properties. Salt doping notably slows the dynamics of polymer chains and reduces ion diffusivity, resulting in a glass transition temperature increase proportional to the salt concentration. The polymer diffusion is shown to be well represented by a modified Vogel-Fulcher-Tamman (M-VFT) equation that accounts for both the temperature and salt concentration dependence. Furthermore, we find that, at any temperature, the concentration dependence of the conductivity is well described by the product of its infinite dilution value and a correction factor accounting for the reduced segmental mobility with increasing salt concentration. These results highlight the important role of polymer segmental mobility in the salt concentration dependence of ion conductivity for temperatures near and above the glass transition.

Impact of Glycosylation on Protein-Protein Self-Interactions of Monoclonal Antibodies.

Protein self-interactions measured via second osmotic virial coefficients (B22) and dynamic light scattering interaction parameter values (kD) are often used as metrics for assessing the favorability of protein candidates and different formulations during monoclonal antibody (MAb) product development. Model predictions of B22 or kD typically do not account for glycans, though glycosylation can potentially impact experimental MAb self-interactions. To the best of our knowledge, the impact of MAb glycosylation on the experimentally measured B22 and kD values has not yet been reported. B22 and kD values of two fully deglycosylated MAbs and their native (i.e., fully glycosylated) counterparts were measured by light scattering over a range of pH and ionic strength conditions. Significant differences between B22 and kD of the native and deglycosylated forms were observed at a range of low to high ionic strengths used to modulate the effect of electrostatic contributions. Differences were most pronounced at low ionic strength, indicating that electrostatic interactions are a contributing factor. Though B22 and kD values were statistically equivalent at high ionic strengths where electrostatics were fully screened, we observed protein-dependent qualitative differences, which indicate that steric interactions may also play a role in the observed B22 and kD differences. A domain-level coarse-grained molecular model accounting for charge differences was considered to potentially provide additional insight but was not fully predictive of the behavior across all of the solution conditions investigated. This highlights that both the level of modeling and lack of inclusion of glycans may limit existing models in making quantitatively accurate predictions of self-interactions.

Dynamic Nanocrystal Superlattices with Thermally Triggerable Lubricating Ligands.

The size-dependent and collective physical properties of nanocrystals (NCs) and their self-assembled superlattices (SLs) enable the study of mesoscale phenomena and the design of metamaterials for a broad range of applications. However, the limited mobility of NC building blocks in dried NCSLs often hampers the potential for employing postdeposition methods to produce high-quality NCSLs. In this study, we present tailored promesogenic ligands that exhibit a lubricating property akin to thermotropic liquid crystals. The lubricating ability of ligands is thermally triggerable, allowing the dry solid NC aggregates deposited on the substrates with poor ordering to be transformed into NCSLs with high crystallinity and preferred orientations. The interplay between the dynamic behavior of NCSLs and the molecular structure of the ligands is elucidated through a comprehensive analysis of their lubricating efficacy using both experimental and simulation approaches. Coarse-grained molecular dynamic modeling suggests that a shielding layer from mesogens prevents the interdigitation of ligand tails, facilitating the sliding between outer shells and consequently enhancing the mobility of NC building blocks. The dynamic organization of NCSLs can also be triggered with high spatial resolution by laser illumination. The principles, kinetics, and utility of lubricating ligands could be generalized to unlock stimuli-responsive metamaterials from NCSLs and contribute to the fabrication of NCSLs.

Distilling coarse-grained representations of molecular electronic structure with continuously gated message passing.

Bottom-up methods for coarse-grained (CG) molecular modeling are critically needed to establish rigorous links between atomistic reference data and reduced molecular representations. For a target molecule, the ideal reduced CG representation is a function of both the conformational ensemble of the system and the target physical observable(s) to be reproduced at the CG resolution. However, there is an absence of algorithms for selecting CG representations of molecules from which complex properties, including molecular electronic structure, can be accurately modeled. We introduce continuously gated message passing (CGMP), a graph neural network (GNN) method for atomically decomposing molecular electronic structure sampled over conformational ensembles. CGMP integrates 3D-invariant GNNs and a novel gated message passing system to continuously reduce the atomic degrees of freedom accessible for electronic predictions, resulting in a one-shot importance ranking of atoms contributing to a target molecular property. Moreover, CGMP provides the first approach by which to quantify the degeneracy of "good" CG representations conditioned on specific prediction targets, facilitating the development of more transferable CG representations. We further show how CGMP can be used to highlight multiatom correlations, illuminating a path to developing CG electronic Hamiltonians in terms of interpretable collective variables for arbitrarily complex molecules.

Optimizing Excipient Properties to Prevent Aggregation in Biopharmaceutical Formulations.

Excipients are included within protein biotherapeutic solution formulations to improve colloidal and conformational stability but are generally not designed for the specific purpose of preventing aggregation and improving cryoprotection in solution. In this work, we have explored the relationship between the structure and antiaggregation activity of excipients by utilizing coarse-grained molecular dynamics modeling of protein-excipient interaction. We have studied human serum albumin as a model protein, and we report the interaction of 41 excipients (polysorbates, fatty alcohol ethoxylates, fatty acid ethoxylates, phospholipids, glucosides, amino acids, and others) in terms of the reduction of solvent accessible surface area of aggregation-prone regions, proposed as a mechanism of aggregation prevention. Polyoxyethylene sorbitan had the greatest degree of interaction with aggregation-prone regions, decreasing the solvent accessible surface area of APRs by 20.7 nm2 (40.1%). Physicochemical descriptors generated by Mordred are employed to probe the structure-property relationship using partial least-squares regression. A leave-one-out cross-validated model had a root-mean-square error of prediction of 4.1 nm2 and a mean relative error of prediction of 0.077. Generally, longer molecules with a large number of alcohol-terminated PEG units tended to interact more, with qualitatively different protein interactions, wrapping around the protein. Shorter or less ethoxylated compounds tend to form hemimicellar clusters at the protein surface. We propose that an improved design would feature many short chains of 5 to 10 PEG units in many distinct branches and at least some hydrophobic content in the form of medium-length or greater aliphatic chains (i.e., six or more carbon atoms). The combination of molecular dynamics simulation and quantitative modeling is an important first step in an all-purpose protein-independent model for the computer-aided design of stabilizing excipients.

Coarse-Graining with Equivariant Neural Networks: A Path Toward Accurate and Data-Efficient Models.

Machine learning has recently entered into the mainstream of coarse-grained (CG) molecular modeling and simulation. While a variety of methods for incorporating deep learning into these models exist, many of them involve training neural networks to act directly as the CG force field. This has several benefits of which the most significant is accuracy. Neural networks can inherently incorporate multibody effects during the calculation of CG forces, and a well-trained neural network force field outperforms pairwise basis sets generated from essentially any methodology. However, this comes at a significant cost. First, these models are typically slower than pairwise force fields, even when accounting for specialized hardware, which accelerates the training and integration of such networks. The second and the focus of this paper is the need for a considerable amount of data to train such force fields. It is common to use 10s of microseconds of molecular dynamics data to train a single CG model, which approaches the point of eliminating the CG model's usefulness in the first place. As we investigate in this work, this "data-hunger" trap from neural networks for predicting molecular energies and forces can be remediated in part by incorporating equivariant convolutional operations. We demonstrate that, for CG water, networks that incorporate equivariant convolutional operations can produce functional models using data sets as small as a single frame of reference data, while networks without these operations cannot.

A consistent framework to characterize the impact of co-solvents in the key process thermophysical properties of choline chloride-based DESs

In the pursuit of environmentally greener solvents, Deep Eutectic Solvents (DESs) have emerged as suitable solutions for diverse industrial applications. The reliable prediction of the physicochemical properties of DESs, affecting the design and operation of processes, is a challenging task that can be accelerated through computational modelling, reducing the amount of experimental work. The purpose of this contribution is to introduce a straightforward yet robust coarse-grained molecular model, able to characterize the thermodynamic properties of several choline chloride-based DESs, including the effect of water and alcohols as co-solvents. This is carried out using the soft-SAFT equation of state by developing an accurate and transferable parametrization. A description of the key properties of pure DESs and their mixtures, including density, speed of sound, isentropic compressibility, viscosity and activity coefficients, is provided and discussed thoroughly, revealing good agreement with the available experimental data. This methodology allows the screening of the thermophysical properties of DESs, whose knowledge is essential pre-requisite for process design.

Development of mechanically-consistent coarse-grained molecular dynamics model: case study of mechanics of spider silk

Understanding mechanics of spider silk holds immense importance due to its potential to drive innovation in the development of materials with exceptional mechanical characteristics suited for a wide range of applications. Coarse-grained (CG) molecular simulations plays a particularly valuable role in this endeavor, allowing for the efficient investigation of spider silk’s mechanical properties. Our research is centered on the examination of spider silk, which comprises major ampullate silk protein (MaSp1). To achieve this, we developed a CG molecular dynamics model. Our investigation began with a focus on MaSp1 chains subjected to uniaxial tensile load, with comparisons made between the CG model results and all-atom simulations. Subsequently, we extended our simulations to encompass more extensive systems, including fully-ordered MaSp1 bundles undergoing uniaxial static stretching. Through comparison with existing literature, we assess how well the CG model reproduces the mechanical properties of spider silk in highly ordered structures. Furthermore, we explored a scenario where MaSp1 bundles were randomly positioned and stretched, providing valuable insights into silk behavior when the initial structure lacks order. Another simulation involved random positioning, but with some degree of orientation in the loading direction, allowing for a closer examination of the initial structure’s influence.

Studying the mechanical behavior of a generic thermoplastic by means of a fast coarse-grained molecular dynamics model

Polymers play an emerging role in modern engineering applications due to their comparatively low cost, low density, and versatile manufacturing. The addition of nano-sized fillers further enhances the polymer’s properties but also induces a strong dependence on the resulting microstructure, particularly the matrix-filler interphase. Since an experimental characterization of this nano-sized interphase is extremely difficult, molecular dynamics (MD) simulations are used to study the effects at such small scales. However, MD’s high computational costs usually limit the scope of a mechanical characterization. Therefore, this study presents the methodology and tools to generate and analyze samples of an efficient generic thermoplastic model. In this first contribution, we focus on the neat polymer and introduce a versatile and numerically stable self-avoiding random walker with adjustable linearity of chain growth. Moreover, we verify our equilibration procedure by preparing samples in liquid and solid state which behave physically sound. Finally, we perform uniaxial tensile tests with a maximum strain of 10 % to evaluate the mechanical properties. In the liquid case, the polymer chains are sufficiently mobile, such that the tensile stresses fluctuate only around zero, while the solid exhibits an almost linear elastic regime followed by a nonlinear part. This contribution forms the basis for a thorough mechanical characterization of polymer nanocomposites which we will address in future studies. The methodology and tools introduced are not limited to our generic polymer, but applicable to many coarse-grained models.

Branching in molecular structure enhancement of solubility in CO2.

Most compounds of some 1,000 amu molecular weight (MW) and higher are poorly soluble in carbon dioxide (CO2). Only at very high pressure, there may be mild solubility. This limits the use of CO2 as a solvent and modifications of CO2 properties through additives. We have developed a coarse-grained molecular model to investigate the dependency of the solubility of hydrocarbon oligomers (MW of ∼1,000 amu) in CO2 and on the molecular structure. The coarse-grained model is optimized by the particle swarm optimization algorithm to reproduce density, surface tension, and enthalpy of vaporization of a highly branched hydrocarbon oligomer (poly-1-decene with six repeating units). We demonstrate that branching in molecular structure of oligomers significantly increases solubility in CO2. The branching in molecular structure results in up to 270-time enhancement of solubility in CO2 than an n-alkane with the same MW. The number of structural edges (methyl group) is a key in improved CO2-philicity. The solubility of poly-1-decene with nine repeating units (MW of 1,264.4 amu) is higher in CO2 than poly-1-dodecene with six repeating units (MW of 1,011.93 amu) because it has more structural edges (10 vs. 7). These results shed light on the enhancement of CO2-philicity by altering molecular structure rather than modifying chemical composition in compounds.

DiAMoNDBack: Diffusion-Denoising Autoregressive Model for Non-Deterministic Backmapping of Cα Protein Traces.

Coarse-grained molecular models of proteins permit access to length and time scales unattainable by all-atom models and the simulation of processes that occur on long time scales, such as aggregation and folding. The reduced resolution realizes computational accelerations, but an atomistic representation can be vital for a complete understanding of mechanistic details. Backmapping is the process of restoring all-atom resolution to coarse-grained molecular models. In this work, we report DiAMoNDBack (Diffusion-denoising Autoregressive Model for Non-Deterministic Backmapping) as an autoregressive denoising diffusion probability model to restore all-atom details to coarse-grained protein representations retaining only Cα coordinates. The autoregressive generation process proceeds from the protein N-terminus to C-terminus in a residue-by-residue fashion conditioned on the Cα trace and previously backmapped backbone and side-chain atoms within the local neighborhood. The local and autoregressive nature of our model makes it transferable between proteins. The stochastic nature of the denoising diffusion process means that the model generates a realistic ensemble of backbone and side-chain all-atom configurations consistent with the coarse-grained Cα trace. We train DiAMoNDBack over 65k+ structures from the Protein Data Bank (PDB) and validate it in applications to a hold-out PDB test set, intrinsically disordered protein structures from the Protein Ensemble Database (PED), molecular dynamics simulations of fast-folding mini-proteins from DE Shaw Research, and coarse-grained simulation data. We achieve state-of-the-art reconstruction performance in terms of correct bond formation, avoidance of side-chain clashes, and the diversity of the generated side-chain configurational states. We make the DiAMoNDBack model publicly available as a free and open-source Python package.

Coarse-grained model simulation-guided localized DNA signal amplification probe for miRNA detection

DNA-based enzyme-free signal amplification strategies are widely employed to detect biomarkers in low abundance. To enhance signal amplification, localized DNA reaction units which increases molecular collision probability is commonly utilized. However, the current understanding of the structure-function relationships in localized DNA signal amplification probes is limited, leading to unsatisfied performance. In this study, we introduced a coarse-grained molecular model to simulate the dynamic behavior of two DNA reaction units within a DNA enzyme-free signal amplification circuit called Localized Catalytic Hairpin Assembly (LCHA). We investigated the impact of localized distance and flexibility on reaction performance. The most efficient LCHA probe guided by simulation exhibits sensitivity 28 times greater that of free CHA, with a detection limit of miR-21 reaching 16 pM, while the least effective LCHA probe demonstrated a modest improvement of only 7 times. We successfully employed the optimized probe to differentiate cancer cells from normal cells based on their miR-21 expression levels, showcasing its quantification ability. By elucidating the mechanistic insights and structure-function relationship in our work, we aim to contribute valuable information that can save users' time and reduce costs when designing localized DNA probes. With a comprehensive understanding of how the localization affects probe performance, researchers can now make more informed and efficient decisions during the design process. This work would find broad applications of DNA nanotechnology in biosensing, biocomputing, and bionic robots.

Mechanical properties and pore network connectivity of sodium montmorillonite as predicted by a coarse-grained molecular model

The study of ionic transport through hydrated sodium montmorillonite (Na-MMT)—the main swelling component of bentonite—is of significant interest to better understand its ability to contain contaminants. From a macroscopic viewpoint, porosity and tortuosity can be viewed as scaling factors connecting diffusion coefficients in the clay material to free diffusion coefficients of ions in water. In this work, the mechanical properties, porosity and tortuosity of Na-MMT were calculated using a bottom-up approach. First, the constructed coarse-grained mesoscopic models of Na-MMT were fitted to all-atom molecular dynamics simulations. Thirty different models—each containing one thousand 120 Å-wide hexagonal Na-MMT platelets—were generated. The dry densities considered herein range from 0.8 to 1.3 g/cm3. Second, calculated the models' elastic constants were in excellent agreement with experimental values reported in the literature. Third, each system's porosity, pore size distribution and pore network were analysed. Fourth, the pore network information was used to create a 3D image of hydrated Na-MMT, and random walk simulations were employed to evaluate its tortuosity. Finally, the porosity and tortuosity values estimated using the models were compared to macroscopic experimental values describing tritium and iodide diffusion in Na-MMT dominant bentonite. The values obtained using the models were fairly consistent with experimental values reported in the literature.