Free Energy Landscape Research Articles

Metadynamics Study of Lipid-Mediated Antibacterial Toxin Binding to the EmrE Multiefflux Protein.

EmrE is a bacterial efflux protein in the small multidrug-resistant (SMR) family present in Escherichia coli. Due to its small size, 110 residues in each dimer subunit, it is an ideal model system to study ligand-protein-membrane interactions. Here in our work, we have calculated the free energy landscape of benzyltrimetylammonium (BTMA) and tetraphenyl phosphonium (TPP) binding to EmrE using the enhanced sampling method-multiple walker metadynamics. We estimate that the free energy of BTMA binding to EmrE is -21.2 ± 3.3 kJ/mol and for TPP is -43.6 ± 3.8 kJ/mol. BTMA passes through two metastable states to reach the binding pocket, while TPP has a more complex binding landscape with four metastable states and one main binding site. Our simulations show that the ligands interact with the membrane lipids at a distance 1 nm away from the binding site which forms a broad local minimum, consistent for both BTMA and TPP. This site can be an alternate entry point for ligands to partition from the membrane into the protein, especially for bulky and/or branched ligands. We also observed the membrane lipid and C-terminal 110HisA form salt-bridge interactions with the helix-1 residue 22LysB. Our free energy estimates and clusters are in close agreement with experimental data and give us an atomistic view of the ligand-protein-lipid interactions. Understanding the binding pathway of these ligands can guide us in future design of ligands that can alter or halt the function of EmrE.

Langevin Dynamics Study on the Driven Translocation of Polymer Chains with a Hairpin Structure.

The hairpin structure is a common and fundamental secondary structure in macromolecules. In this work, the process of the translocation of a model polymer chain with a hairpin structure is studied using Langevin dynamics simulations. The simulation results show that the dynamics of hairpin polymer translocation through a nanopore are influenced by the hairpin structure. Hairpin polymers can be classified into three categories, namely, linear-like, unsteady hairpin, and steady hairpin, according to the interaction with the stem structure. The translocation behavior of linear-like polymers is similar to that of a linear polymer chain. The time taken for the translocation of unsteady hairpin polymers is longer than that for a linear chain because it takes a long time to unfold the hairpin structure, and this time increases with stem interaction and decreases with the driving force. The translocation of steady hairpin polymers is distinct, especially under a weak driving force; the difficulty of unfolding the hairpin structure leads to a low translocation probability and a short translocation time. The translocation behavior of hairpin polymers can be explained by the theory of the free-energy landscape.

Atomic resolution map of the solvent interactions driving SOD1 unfolding in CAPRIN1 condensates

Biomolecules can be sequestered into membrane-less compartments, referred to as biomolecular condensates. Experimental and computational methods have helped define the physical-chemical properties of condensates. Less is known about how the high macromolecule concentrations in condensed phases contribute "solvent" interactions that can remodel the free-energy landscape of other condensate-resident proteins, altering thermally accessible conformations and, in turn, modulating function. Here, we use solution NMR spectroscopy to obtain atomic resolution insights into the interactions between the immature form of superoxide dismutase 1 (SOD1), which can mislocalize and aggregate in stress granules, and the RNA-binding protein CAPRIN1, a component of stress granules. NMR studies of CAPRIN1:SOD1 interactions, focused on both unfolded and folded SOD1 states in mixed phase and demixed CAPRIN1-based condensates, establish that CAPRIN1 shifts the SOD1 folding equilibrium toward the unfolded state through preferential interactions with the unfolded ensemble, with little change to the structure of the folded conformation. Key contacts between CAPRIN1 and the H80-H120 region of unfolded SOD1 are identified, as well as SOD1 interaction sites near both the arginine-rich and aromatic-rich regions of CAPRIN1. Unfolding of immature SOD1 in the CAPRIN1 condensed phase is shown to be coupled to aggregation, while a more stable zinc-bound, dimeric form of SOD1 is less susceptible to unfolding when solvated by CAPRIN1. Our work underscores the impact of the condensate solvent environment on the conformational states of resident proteins and supports the hypothesis that ALS mutations that decrease metal binding or dimerization function as drivers of aggregation in condensates.

Normalizing flows as an enhanced sampling method for atomistic supercooled liquids

Abstract Normalizing flows can transform a simple prior probability distribution into a more complex target distribution. Here, we evaluate the ability and efficiency of generative machine learning methods to sample the Boltzmann distribution of an atomistic model for glass-forming liquids. This is a notoriously difficult task, as it amounts to ergodically exploring the complex free energy landscape of a disordered and frustrated many-body system. We optimize a normalizing flow model to successfully transform high-temperature configurations of a dense liquid into low-temperature ones, near the glass transition. We perform a detailed comparative analysis with established enhanced sampling techniques developed in the physics literature to assess and rank the performance of normalizing flows against state-of-the-art algorithms. We demonstrate that machine learning methods are very promising, showing a large speedup over conventional molecular dynamics. Normalizing flows show performances comparable to parallel tempering and population annealing, while still falling far behind the swap Monte Carlo algorithm. Our study highlights the potential of generative machine learning models in scientific computing for complex systems, but also points to some of its current limitations and the need for further improvement.

Improving Machine Learned Force Fields for Complex Fluids through Enhanced Sampling: A Liquid Crystal Case Study.

Machine learned force fields offer the potential for faster execution times while retaining the accuracy of traditional DFT calculations, making them promising candidates for molecular simulations in cases where reliable classical force fields are not available. Some of the challenges associated with machine learned force fields include simulation stability over extended periods of time and ensuring that the statistical and dynamical properties of the underlying simulated systems are correctly captured. In this work, we propose a systematic training pipeline for such force fields that leads to improved model quality, compared to that achieved by traditional data generation and training approaches. That pipeline relies on the use of enhanced sampling techniques, and it is demonstrated here in the context of a liquid crystal, which exemplifies many of the challenges that are encountered in fluids and materials with complex free energy landscapes. Our results indicate that, whereas the majority of traditional machine learned force field training approaches lead to molecular dynamics simulations that are only stable over hundred-picosecond trajectories, our approach allows for stable simulations over tens of nanoseconds for organic molecular systems comprising thousands of atoms.

Identification of Peregrin inhibitors-modulators by harnessing the computational prowess of molecular simulation and machine learning algorithms

Peregrin is marked as a potential drug target due to its pivotal role in the epigenetic maintenance of cellular metabolism. It is counted among those of the bromodomain-containing protein family. The protein binds to the acetylated lysine of N-terminus histone proteins of the nucleosomes. This bound form neutralizes the otherwise positively charged histone and, thus, crucially impacts the DNA replication mechanism. In several diseases like cancer, bone loss, and leukemia, this peregrin binding impacts the genes’ overexpression. Hence, the present study, computational screening of the bromodomain-specific inhibitors from a library of 5430 compounds retrieved from the ChemDiv database. Two different scoring functions based molecular docking studies have been employed for molecular interaction analysis. Followed by an artificial intelligence-based absolute binding affinity prediction has been confirmed through KDeep, which follows a machine learning step to obtain a precise binding energy score for the set of best compounds in the screening process. The resultant compounds were then screened for pharmacokinetics and drug-likeliness properties. Using molecular dynamics (MD) simulation analysis, the dynamic behavior of the four suggested compounds and Peregrin has been investigated for a 100 ns period. Identified compounds are found to be sufficiently effective in holding their strong interaction affinity toward Peregrin for an adequate time span, and structural integrity is also found to be comparable with the standard compound considered in the study. Mostly, all four potent compounds (K788-9421, S357-0084, S357-0893, S357-0915) are hydrophobic and evaluated for thermodynamics property analysis using MM-GBSA and free energy landscape (FEL) calculations. However, among all, K788-9421 shows comparatively better and energetically most favorable interactions with peregrin. Overall, study findings can lead to further research in developing next-generation broad-spectrum Peregrin inhibitors-modulators; they need extensive experimental validation for considering the compounds for clinical-trial application for being an excellent drug-candidate entity against Peregrin protein.

Isoniazid Derivatives as Potential Lipoxygenase‐15 Inhibitors: In‐vitro and In‐silico Studies

AbstractThe enzymatic oxidation of polyunsaturated fatty acids, particularly arachidonic acid, produces important lipid mediators such as leukotrienes and prostaglandins, which are critical for triggering inflammatory cascades in the body. The enzyme lipoxygenase‐15 (LOX‐15) plays a crucial role in the oxygenation of unsaturated fatty acids. Targeting this enzyme for inhibition is essential for diminishing inflammation and averting numerous potentially fatal diseases. Therefore, the inhibitory activity of a series of isoniazid derivatives, selected from our in‐house library, was assessed against LOX‐15 by employing both in vitro and in silico methods. Among the 23 compounds evaluated, 15 showed activity in vitro. Notably, compound 7 was the most effective, demonstrating an IC50 value of 1.4±0.1 μM while the standard inhibitor, NDGA, showed an inhibition at 9.4±0.6 μM. These compounds were found to competitively inhibit LOX‐15, with Ki values ranging from 1.2–7.1 μM. Molecular docking was utilized to investigate the intermolecular interactions among the active compounds. The thermodynamic stability of the compound 7 and NDGA in complex with LOX‐15 was verified through 100 ns of molecular dynamics simulations. Post‐simulation analyses involved RMSD, RMSF, RoG, free energy landscape, principal component analysis, and dynamic cross‐correlation matrix. Compound 7 and NDGA had MM/GBSA binding energies of −54.14±2.55 kcal/mol and −35.24±3.23 kcal/mol, respectively. The study underscores the significant potential of isoniazid derivatives in LOX‐15 inhibition and their role in managing inflammatory diseases.

Multiple Parameter Replica Exchange Gaussian Accelerated Molecular Dynamics for Enhanced Sampling and Free Energy Calculation of Biomolecular Systems.

This study introduces a novel method named multiple parameter replica exchange Gaussian accelerated molecular dynamics (MP-Rex-GaMD), building on the Gaussian accelerated molecular dynamics (GaMD) algorithm. GaMD enhances sampling and retrieves free energy information for biomolecular systems by adding a harmonic boost potential to smooth the potential energy surface without the need for predefined reaction coordinates. Our innovative approach advances the acceleration power and energetic reweighting accuracy of GaMD by incorporating a replica exchange algorithm that enables the exchange of multiple parameters, including the GaMD boost parameters of force constant and energy threshold, as well as temperature. Applying MP-Rex-GaMD to the three model systems of dialanine, chignolin, and HIV protease, we demonstrate its superior capability over conventional molecular dynamics and GaMD simulations in exploring protein conformations and effectively navigating various biomolecular states across energy barriers. MP-Rex-GaMD allows users to accurately map free energy landscapes through energetic reweighting, capturing the ensemble of biomolecular states from low-energy conformations to rare high-energy transitions within practical computational time scales.

Variational time reversal for free-energy estimation in nonequilibrium steady states.

Studying the structure of systems in nonequilibrium steady states necessitates tools that quantify population shifts and associated deformations of equilibrium free-energy landscapes under persistent currents. Within the framework of stochastic thermodynamics, we establish a variant of the Kawasaki-Crooks equality that relates nonequilibrium free-energy corrections in overdamped Langevin systems to heat dissipation statistics along time-reversed relaxation trajectories computable with molecular simulation. Using stochastic control theory, we arrive at a general variational approach to evaluate the Kawasaki-Crooks equality and use it to estimate distribution functions of order parameters in specific models of driven and active matter, attaining substantial improvement in accuracy over simple perturbative methods.

(Invited) Analysis of Interfacial Species at Finite Bias - Calcium Borohydride in Tetrahydrofuran

To understand the operation of complex electrolytes we must determine which species are present and active at the interface with the electrode. We combine molecular dynamics sampling with continuum modeling to determine the speciation of Ca(BH4)2 in THF next to a model electrode (graphite). Mulitvalent ions in organic solvents with poor dielectric screening may exist in multiple coordinated states. We explore the free energy landscape associated with the interconversion of species in the bulk electrolyte using metadynamics simulations. We find that the chemical equilibrium between neutral and charged species is dominated by disproportionation following dimer formation - consistent with experimental findings. We also make use of clustering analysis to reveal distinct molecular conformations within constrained coordination states. To assess how the chemical equilibrium may be modified by the potential difference at the electrode, we develop a continuum model based on the modified Poisson-Boltzmann equation, with volume exclusion. This model also incorporates molecular-scale details through sampled free energy profiles of each relevant species within the solvent as a function of distance from the electrode. We find that a narrow (approximately 1 nm) region near the electrode, with highly variable speciation driven by potential difference, dominates the electrochemical activity of this electrolyte.

(Invited) Towards an Expanded Palette of Materials and Mechanisms for Neuromorphic Computing

The metal-insulator transitions of electron correlated transition metal oxides provide an attractive vector for achieving large conductance switching with minimal energy dissipation. However, given the sparse and disconnected current knowledge of neuromorphic materials, a fundamental understanding of descriptors of neuromorphic function formulated in terms of intrinsic material properties, and the influence of atomistic defects on mesoscale domain evolution in the presence of external applied fields is currently lacking. Using VO2 and M x V2O5 compounds as model systems, I will detail our efforts to develop a systematic understanding of how compositional modifications through substitutional or interstitial doping alter transformation characteristics such as the transition temperature, magnitude of switching, energy dissipation, and hysteresis.The inclusion of dopant atoms strongly modifies the free energy landscapes in terms of relative phase stabilities, transformation barriers, and pathways; thereby profoundly altering the coupling of lattice, electronic, and spin degrees of freedom in a non-trivial manner. I will particularly focus on the three distinct mechanisms: (a) discovery of diffusive dopants that provide a distinctive new way to alter the dynamics of electronic transitions; (b) cation shuttling and polaron oscillation as a means of engendering metal—insulator transitions; and (c) lattice-anharmonicity-driven mechanisms in compounds with stereochemically active lone pairs. Acknowledgements: Research supported as part of REMIND, an Energy Frontier Research Center funded by the U.S. Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES), under DE-SC0023353

Parameter Optimization Method in Multidimensional Umbrella Sampling.

Umbrella sampling (US) is an effective method for calculating free-energy landscapes (FELs). However, the complexity of controlling the sampling positions complicates multidimensional FEL calculations. In this study, we proposed a method for controlling sampling by optimizing the US parameters. This method comprises the introduction of a target point and the optimization of the parameters to sample a window around this point. We approximated each window to normal distributions using an umbrella integration method and calculated the divergences between the window distributions and the state distributed at the target position by a variationally enhanced sampling method. Thus, the minimization of the divergence facilitated sampling around the target point, after which the parameters could be optimized on the fly while performing equilibration simulation. In practice, our method employs bias potentials with off-diagonal terms, ensuring a more efficient calculation of multidimensional FEL. Additionally, we developed an algorithm for determining the target point for automated FEL search; the algorithm samples in a specified direction while controlling the overlap of distributions. We performed three different FEL calculations as examples: (1) the calculation of the permeation of a water molecule through a lipid bilayer (one-dimensional FEL), (2) the calculation of the internal structural changes in alanine dipeptide in water (two-dimensional FEL), and (3) the calculation of the internal structural changes from a β-strand structure to an α-helix structure in alanine decapeptide (Ala10, 16-dimensional FEL). These results confirmed that our method could control the number of US windows and calculate the high-dimensional FELs that could not be evaluated by the conventional US method.

A novel kinetic model to demonstrate the independent effects of ATP and ADP/Pi concentrations on sarcomere function.

Understanding muscle contraction mechanisms is a standing challenge, and one of the approaches has been to create models of the sarcomere-the basic contractile unit of striated muscle. While these models have been successful in elucidating many aspects of muscle contraction, they fall short in explaining the energetics of functional phenomena, such as rigor, and in particular, their dependence on the concentrations of the biomolecules involved in the cross-bridge cycle. Our hypothesis posits that the stochastic time delay between ATP adsorption and ADP/Pi release in the cross-bridge cycle necessitates a modeling approach where the rates of these two reaction steps are controlled by two independent parts of the total free energy change of the hydrolysis reaction. To test this hypothesis, we built a two-filament, stochastic-mechanical half-sarcomere model that separates the energetic roles of ATP and ADP/Pi in the cross-bridge cycle's free energy landscape. Our results clearly demonstrate that there is a nontrivial dependence of the cross-bridge cycle's kinetics on the independent concentrations of ATP, ADP, and Pi. The simplicity of the proposed model allows for analytical solutions of the more basic systems, which provide novel insight into the dominant mechanisms driving some of the experimentally observed contractile phenomena.

Suppressing Mycobacterium tuberculosis virulence and drug resistance by targeting Eis protein through computational drug discovery.

Tuberculosis (TB) remains a critical health threat, particularly with the emergence of multidrug-resistant strains. This demands attention from scientific communities and healthcare professionals worldwide to develop effective treatments. The enhanced intracellular survival (Eis) protein is an acetyltransferase enzyme of Mycobacterium tuberculosis that functions by adding acetyl groups to aminoglycoside antibiotics, which interferes with their ability to bind to the bacterial ribosome, thereby preventing them from inhibiting protein synthesis and killing the bacterium. Therefore, targeting this protein accelerates the chance of restoring the aminoglycoside drug activity, thereby reducing the emergence of drug-resistant TB. For this, we have screened 406,747 natural compounds from the Coconut database against Eis protein. Based on MM/GBSA rescoring binding energy, the top 5 most prominent natural compounds, viz. CNP0187003 (-96.14kcal/mol), CNP0176690 (-93.79kcal/mol), CNP0136537 (-92.31kcal/mol), CNP0398701 (-91.96kcal/mol), and CNP0043390 (-91.60kcal/mol) were selected. These compounds exhibited the presence of a substantial number of hydrogen bonds and other significant interactions confirming their strong binding affinity with the Eis protein during the docking process. Subsequently, the MD simulation of these compounds exhibited that the Eis-CNP0043390 complex was the most stable, followed by Eis-CNP0187003 and Eis-CNP0176690 complex, further verified by binding free energy calculation, principal component analysis (PCA), and Free energy landscape analysis. These compounds demonstrated the most favourable results in all parameters utilised for this investigation and may have the potential to inhibit the Eis protein. There these findings will leverage computational techniques to identify and develop a natural compound inhibitor as an alternative for drug-resistant TB.

Robustaflavone as a novel scaffold for inhibitors of native and auto-proteolysed human neutrophil elastase

ABSTRACT Human neutrophil elastase (HNE) plays a key role in initiating inflammation in the cardiopulmonary and systemic contexts. Pathological auto-proteolysed two-chain (tc) HNE exhibits reduced binding affinity with inhibitors. Using AutoDock Vina v1.2.0, 66 flavonoid inhibitors, sivelestat and alvelestat were docked with single-chain (sc) HNE and tcHNE. Schrodinger PHASE v13.4.132 was used to generate a 3D-QSAR model. Molecular dynamics (MD) simulations were conducted with AMBER v18. The 3D-QSAR model for flavonoids with scHNE showed r 2 = 0.95 and q 2 = 0.91. High-activity compounds had hydrophobic A/A2 and C/C2 rings in the S1 subsite, with hydrogen bond donors at C5 and C7 positions of the A/A2 ring, and the C4’ position of the B/B1 ring. All flavonoids except robustaflavone occupied the S1’-S2’ subsites of tcHNE with decreased AutoDock binding affinities. During MD simulations, robustaflavone remained highly stable with both HNE forms. Principal Component Analysis suggested that robustaflavone binding induced structural stability in both HNE forms. Cluster analysis and free energy landscape plots showed that robustaflavone remained within the sc and tcHNE binding site throughout the 100 ns MD simulation. The robustaflavone scaffold likely inhibits both tcHNE and scHNE. It is potentially superior to sivelestat and alvelestat and can aid in developing therapeutics targeting both forms of HNE.

Structural generation by inverse transformation using principal component analysis enhances conformational sampling of protein

Abstract Molecular dynamics (MD) simulations are frequently used to elucidate the molecular mechanisms underlying protein behavior. Based on a conformational search with MD simulations, protein structures rich in high-dimensional data can be quantitatively evaluated in free-energy landscapes (FELs). Generally, FELs are defined in low-dimensional subspaces spanned by reaction coordinates to characterize biological functions. When calculating FELs of proteins, principal component analysis (PCA) is particularly useful for capturing large-amplitude motions via dimensionality reduction into low-dimensional subspaces. In this study, to efficiently calculate FELs, a simple and convenient method is proposed by accelerating conformational search in a PCA subspace, which is achieved by quick generation of protein configurations. Specifically, inverse transformation driven by PCA facilitates the quick generation of diverse protein configurations from arbitrary grids in a defined PCA subspace. In our conformational search, a set of newly generated configurations serves as initial structures for multiple MD simulations, enabling one to calculate FELs of proteins by building Markov state models from their multiple trajectories. In conclusion, the conformational search from protein configurations broadly distributed in a PCA subspace accelerates FEL calculations, which supports a comprehensive approach to understanding collective protein dynamics.

Marine fungal diversity unlocks potent antivirals against monkeypox through methyltransferase inhibition revealed by molecular dynamics and free energy landscape

The escalating threat posed by the Monkeypox virus (MPXV) to global health necessitates the urgent discovery of effective antiviral agents, as there are currently no specific drugs available for its treatment, and existing inhibitors are hindered by toxicity and poor pharmacokinetic profiles. This study aimed to identify potent MPXV inhibitors by screening a diverse library of small molecule compounds derived from marine fungi, focusing on the viral protein VP39, a key methyltransferase involved in viral replication. An extensive virtual screening process identified four promising compounds—CMNPD15724, CMNPD28811, CMNPD30883, and CMNPD18569—alongside a control molecule. Rigorous evaluations, including re-docking, molecular dynamics (MD) simulations, and hydrogen bond analysis, were conducted to assess their inhibitory potential against MPXV VP39. CMNPD15724 and CMNPD30883, in particular, demonstrated a superior binding affinity and stable interactions within the target protein's active site throughout the MD simulations, suggesting a capacity to overcome the limitations associated with sinefungin. The stability of these VP39-compound complexes, corroborated by MD simulations, provided crucial insights into the dynamic behavior of these interactions. Furthermore, Principal Component Analysis (PCA) based free energy landscape assessments offered a detailed understanding of the dynamic conformational changes and energetic profiles underlying these compounds' functional disruption of VP39. These findings establish CMNPD15724, CMNPD28811, CMNPD30883, and CMNPD18569 as promising MPXV inhibitors and highlight marine fungi as a valuable source of novel antiviral agents. These compounds represent potential candidates for further experimental validation, advancing the development of safer and more effective therapeutic options to combat this emerging viral infection.

Role of interaction mode of phenanthrene derivatives as selective PDE5 inhibitors using molecular dynamics simulations and quantum chemical calculations.

Phosphodiesterase type 5 (PDE5) inhibitors play a crucial role in blocking PDE5 to improve erectile dysfunction (ED). However, most PDE5 drugs revealed side effects including the loss of vision due to the PDE6 inhibition. Phenanthrene derivatives isolated from E. macrobulbon were previously reported as PDE5 inhibitors. Two phenanthrene derivatives (cpds 1-2) revealed better inhibition to PDE5 than PDE6 and cpd 1 is more selective to PDE5 than cpd 2. To elucidate why the phenanthrene derivatives could inhibit PDE5 and PDE6, their binding modes were investigated using molecular dynamics simulations and quantum chemical calculations, as compared to the PDE5 drugs. From the results, all four drugs and phenanthrene derivatives revealed similar π-π interactions to Phe820 in PDE5. Additional H-bond interaction to Gln817 in PDE5 resulted in better PDE5 inhibition of vardenafil and tadalafil. Moreover, cpds 1-2 were able to form the H-bond interaction with Asp764 in PDE5. In the case of the PDE6, the loss of π-π interaction to Phe776 and H-bond interaction to Gln773 indicated the important points for losing the PDE6 inhibition. In conclusion, to develop the new potent PDE5 inhibitors, not only the important interaction with PDE5 but also the interaction with PDE6 should be considered. In phenanthrene derivatives, the middle ring was significant to form π-π interactions to Phe820 in PDE5 and hydroxyl substituent was also the key part to form the H-bond interaction with Asp764 in PDE5. Principal component analysis (PCA) and free energy landscape (FEL) analysis indicated the stability of the system. The bioavailability, drug-likeness, and pharmacokinetics of phenanthrene derivatives were also predicted. These derivatives revealed good drug-likeness and GI absorption. The obtained results showed that phenanthrene derivatives could be interesting for the development of PDE5 inhibitors in the future.

Arrhythmogenic cardiomyopathy-related cadherin variants affect desmosomal binding kinetics

Cadherins are calcium dependent adhesion proteins that establish and maintain the intercellular mechanical contact by bridging the gap between adjacent cells. Desmoglein-2 (Dsg2) and desmocollin-2 (Dsc2) are tissue specific cadherin isoforms of the cell-cell contact in cardiac desmosomes. Mutations in the DSG2-gene and in the DSC2-gene are related to arrhythmogenic right ventricular cardiomyopathy (ARVC) a rare but severe heart muscle disease. Here, several possible homophilic and heterophilic binding interactions of wild-type Dsg2, wild-type Dsc2, as well as one Dsg2- and two Dsc2-variants, each associated with ARVC, are investigated. Using single molecule force spectroscopy (SMFS) with atomic force microscopy (AFM) and applying Jarzynski's equality the kinetics and thermodynamics of Dsg2/Dsc2 interaction can be determined. The free energy landscape of Dsg2/Dsc2 dimerization exposes a high activation energy barrier, which is in line with the proposed strand-swapping binding motif. Although the binding motif is not affected by any of the mutations, the binding kinetics of the interactions differ significantly from the wild-type. While wild-type cadherins exhibit an average complex lifetime of approx. 0.3 s interactions involving a variant consistently show - lifetimes that are substantially larger. The lifetimes of the wild-type interactions give rise to the picture of a dynamic adhesion interface consisting of continuously dissociating and (re)associating molecular bonds, while the delayed binding kinetics of interactions involving an ARVC-associated variant might be part of the pathogenesis. Our data provide a comprehensive and consistent thermodynamic and kinetic description of cardiac cadherin binding, allowing detailed insight into the molecular mechanisms of cell adhesion.

Nanoparticle agglomeration in molten salt nanofluids: Free energy landscape and its impacts on thermal transport property degradation

The nanofluids consisting of molten salt and nanoparticles can gain enhanced thermal performance for heat transfer and energy storage processes. Agglomeration of nanoparticles is ubiquitous in this colloidal dispersion, deteriorating the dispersed states of the nanocomposite interiors, and its microscopic mechanisms are unsatisfactorily explored. In this work, molecular dynamics simulations combined with umbrella sampling technique to capture the relative dispersion state of nanoparticles in molten salts effectively were performed to reveal the free energy landscape of agglomeration and its influence on heat transport. The dominant deep minima in the free energy surface demonstrates that the agglomeration of MgO nanoparticles in the molten NaCl salt is principally induced by van der Waals attractions between MgO nanoparticles and aggravated by large surface area. The unstable dispersion of nanoparticles is the most fatal problem for enhanced thermal conductivity during agglomeration, and the relative distance of MgO nanoparticles has less impacts on the thermal conductivity of the melt. The stronger adsorption of ions around nanoparticles can improve their relative dispersion, and utilize interfacial effects to enhance the thermal conductivity. Combined with the benefit of ordered ionic layer on the surface, the MgO nanoparticles of 10 Å diameter (specific surface area of ∼ 938.80 m2/g) become the optimal choice for molten NaCl. With the mechanisms of interfacial microstructures preventing agglomerated nanoparticles and improving heat transfer of molten salts revealed, this work can enlighten the selection or feasible modification for nanoparticles in various high-temperature molten salts, since the mechanisms are universal to other nanofluids, especially for ionic liquid based nanofluids.