Biological processes such as the sequestration of superoxide dismutase 1 (SOD1) into biomolecular condensates, including fused in sarcoma and stress granules, are vital for understanding disease mechanisms, including amyotrophic lateral sclerosis. Moreover, protein-crowder interactions within these condensates are recognized as fundamental to cellular phase separation and disease-related processes. However, the specific role of the hydration environment in governing SOD1's behavior and transition dynamics within these condensates remains poorly understood, limiting our ability to accurately model these critical biological systems. Therefore, we incorporate explicit water into an implicit solvent model (OPEP) to investigate how water influences SOD1's behavior, residence times, and transition rates among associative states. We employ the advanced CVF (Coronas, Vilanova, Franzese) water model, which accurately captures hydrogen-bond networks at the molecular level. While the OPEP model indicates that bovine serum albumin (BSA) crowders reduce SOD1's partition coefficient (PC) primarily through non-specific interactions, our explicit-water approach points to hydration entropy in BSA as a key contributor to the observed PC reduction. This result offers a new perspective on the system's free-energy landscape, complementing those obtained from OPEP alone. Our research supports the notion that explicitly modeling water can enhance our understanding of protein-crowder interactions and their biological implications, further emphasizing the potential role of water in cellular phase separation and disease-related processes.

MOTIVATION:The low-density lipoprotein receptor-related protein 1 (LRP1) plays a critical role in development and transport across the blood-brain barrier (BBB), yet its molecular architecture has remained unresolved due to the absence of an experimentally determined structure. RESULTS:Using homology modeling and neural network-based structure prediction algorithms, complemented with molecular dynamics (MD) simulations, we propose atomistic models of both monomeric and dimeric LRP1 forms. The simulations reveal a plausible dimerization mechanism and provide insight into the dynamic behavior of its flexible domains under physiological conditions. We estimated the energy required to disrupt the non-covalent interactions linking LRP1's α and β chains to be 180±2 kB T. MD simulations further highlight the fundamental role of glycans in stabilizing the dimeric quaternary structure by increasing intra-dimer contacts. The resulting structural models also provide experimentally testable estimates of LRP1 size, domain organization, and interface stability that may guide future imaging and mutagenesis studies. This study enhances our molecular understanding of LRP1-mediated transport across the BBB and the role of glycosylation in protein-protein interactions, opening new avenues for targeted drug design strategies. AVAILABILITY AND IMPLEMENTATION:The monomeric and dimeric LRP1 models are available in ModelArchive under the accession codes ma-k8036 and ma-ubwf7, respectively.

Nanoconfined water plays a crucial role in nanofluidics, biology, and cutting-edge technologies. The process of melting water monolayers and quasi-two-dimensional confined water involves, as an intermediate stage, the hexatic phase-a state that lies between solid and liquid and is characterized by quasi-long-range orientational order and short-range translational order. However, the influence of hydrogen bond (HB) cooperativity in this process has not been thoroughly investigated. This gap hampers our understanding of the phase behavior of confined water and limits the accuracy of our models. To address this, we extend the water model developed by Franzese and Stanley, which explicitly includes many-body interactions (MBIs) of HBs. We distinguish the contributions of three-body and five-body HB-MBIs. Our Monte Carlo calculations in the isobaric-isothermal ensemble produce a detailed pressure-temperature phase diagram, revealing polymorphism and polyhexaticity: low-density square ice and high-density triangular ice are separated from the liquid phase by distinct hexatic phases. Three-body interactions notably promote crystallization and can destabilize the low-density hexatic phase, while cooperative five-body interactions help restore it, thus modifying the thermodynamic landscape. These findings demonstrate that HB-MBIs are key to determining the phase behavior of confined water, influencing phenomena such as non-monotonic specific heat, maximum-density lines, and the accessibility of the liquid-liquid critical point. Beyond advancing theoretical understanding, these results have wide-ranging implications for nanofluidics, interfacial science, and applications in biology, food technology, and pharmaceutics, where controlling water under confinement is essential.

The increasing presence of polluting chemicals in man-made wastewater poses significant environmental and health risks. Advanced oxidation processes, particularly those involving photocatalytic materials like titanium dioxide (TiO2), offer a promising solution for degrading these pollutants. This study employs force field molecular dynamics simulations to investigate the interactions between pollutants, the TiO2 surface, water and ions, aiming to elucidate their role in the adsorption process. The results reveal that the protonation state of pollutants significantly influences their contact with the TiO2 surface, with negatively charged species showing a higher affinity for the surface's active sites, especially those containing carboxylate groups. The formation of hydrogen bond networks affects the stability of these contacts positively, while the tendency of some pollutants to aggregate hinders surface contacts. Furthermore, we observe cations (Na+) to alter the surface-near environment in a typical electrical double-layer manner, as well as to participate in pollutant adsorption and aggregation. These findings provide insights into the adsorption features triggering the initial pollutant degradation on amorphous TiO2, which could enhance the design of more efficient wastewater treatment technologies.

Simulating water droplets made up of millions of molecules and on timescales as needed in biological and technological applications is challenging due to the difficulty of balancing accuracy with computational capabilities. Most detailed descriptions, such as ab initio, polarizable, or rigid models, are typically constrained to a few hundred (for ab initio) or thousands of molecules (for rigid models). Recent machine learning approaches allow for the simulation of up to 4 million molecules with ab initio accuracy but only for tens of nanoseconds, even if parallelized across hundreds of GPUs. In contrast, coarse-grained models permit simulations on a larger scale but at the expense of accuracy or transferability. Here, we consider the CVF molecular model of fluid water, which bridges the gap between accuracy and efficiency for free-energy and thermodynamic quantities due to i) a detailed calculation of the hydrogen bond contributions at the molecular level, including cooperative effects, and ii) coarse-graining of the translational and rotational degrees of freedom of the molecules. The CVF model can reproduce the experimental equation of state and fluctuations of fluid water across a temperature range of 60 degrees around ambient temperature and from 0 to 50 MPa. In this work, we describe efficient parallel Monte Carlo algorithms executed on GPUs using CUDA, tailored explicitly for the CVF model. We benchmark accessible sizes of 17 million molecules with the Metropolis and 2 million with the Swendsen-Wang Monte Carlo algorithm.

When nanoparticles (NPs) are introduced into a biological solution, layers of biomolecules form on their surface, creating a corona. Understanding how the protein's structure evolves into the corona is essential for evaluating the safety and toxicity of nanotechnology. However, the influence of NP properties on protein conformation is not well understood. In this study, we propose a new method that addresses this issue by analyzing multi-component spectral data (UV Resonance Raman, Circular Dichroism, and UV absorbance) using machine learning (ML). We apply the method to fibrinogen, a crucial protein in human blood plasma, at physiological concentrations while interacting with hydrophobic carbon or hydrophilic silicon dioxide NPs, revealing striking differences in the temperature dependence of the protein structure between the two cases. Our unsupervised ML method (a) does not suffer from the challenges associated with the curse of dimensionality, and (b) simultaneously handles spectral data from various sources. The method offers a quantitative analysis of protein structural changes upon adsorption. It enhances the understanding of the correlation between protein structure and NP interactions, which could support the development of nanomedical tools to treat various conditions.

Water is essential for life, and its unique properties present significant scientific challenges because of our limited understanding of its thermodynamic behavior. This knowledge gap hinders the accurate theoretical replication of water's properties across various temperatures and pressures, mainly due to the complex quantum nature of its many-body interactions. To address this challenge, we developed a novel molecular model for bulk liquid water that focuses on the hydrogen bond network and its cooperativity. We show that these factors are crucial to controlling water's thermodynamics. Our study introduces an innovative strategy to derive many-body parameters from quantum calculations, validated by advanced polarizable models and calibrated with experimental data under ambient conditions. Our results demonstrate that this model accurately predicts water's equation of state and response functions over a temperature range of approximately 60 degrees at atmospheric pressure and around 40 degrees up to 50 MPa. This quantitative validation underscores the model's reliability and transferability, providing new insights into water's cooperative fluctuations across a broader range of thermodynamic conditions than previously achieved. Moreover, our model's computational efficiency allows for scalability in simulating water droplets nearing micrometer sizes without extensive computational resources or long simulation times. This breakthrough holds significant theoretical and technological implications, opening avenues for advanced research across various scientific fields and applications.

Controlling emergent structural order in spatially constrained systems is a fundamental challenge. Using large-scale simulations of a model fluid at equilibrium conditions, we show that geometric confinement alone can stabilize fluid and hyperuniform labyrinthine phases. Moreover, confinement can induce self-assembly into distinct regimes—ranging from nonhyperuniform to antihyperuniform configurations—providing a robust mechanism for tuning spatial order. Our results identify confinement as a minimal design principle for engineering systems with target structural properties, including (anti)hyperuniformity, without relying on genetic or chemical specificity, and with broad applications in multiple disciplines and technologies.

Nanoparticles (NPs) in contact with biological fluid adsorb biomolecules into a corona. This corona comprises proteins that strongly bind to the NP (hard corona) and loosely bound proteins (soft corona) that dynamically exchange with the surrounding solution. While the kinetics of hard corona formation is relatively well understood, thanks to experiments and robust simulation models, the experimental characterization and simulation of the soft corona present a more significant challenge. Here, we review the current state of the art in soft corona characterization and introduce a novel open-source computational model to simulate its dynamic behavior, for which we provide the documentation. We focus on the case of transferrin (Tf) interacting with polystyrene NPs as an illustrative example, demonstrating how this model captures the complexities of the soft corona and offers deeper insights into its structure and behavior. We show that the soft corona is dominated by a glassy evolution that we relate to crowding effects. This work advances our understanding of the soft corona, bridging experimental limitations with improved simulation techniques.

Simulating water droplets made up of millions of molecules and on timescales as needed in biological and technological applications is challenging due to the difficulty of balancing accuracy with computational capabilities. Most detailed descriptions, such as ab initio, polarizable, or rigid models, are typically constrained to a few hundred (for ab initio) or thousands of molecules (for rigid models). Recent machine learning approaches allow for the simulation of up to 4 million molecules with ab initio accuracy but only for tens of nanoseconds, even if parallelized across hundreds of GPUs. In contrast, coarse-grained models permit simulations on a larger scale but at the expense of accuracy or transferability. Here, we consider the CVF molecular model of fluid water, which bridges the gap between accuracy and efficiency for free-energy and thermodynamic quantities due to i) a detailed calculation of the hydrogen bond contributions at the molecular level, including cooperative effects, and ii) coarse-graining of the translational and rotational degrees of freedom of the molecules. The CVF model can reproduce the experimental equation of state and fluctuations of fluid water across a temperature range of 60° around ambient temperature and from 0 to 50 MPa. In this work, we describe efficient parallel Monte Carlo algorithms executed on GPUs using CUDA, tailored explicitly for the CVF model. We benchmark accessible sizes of 17 million molecules with the Metropolis and 2 million with the Swendsen-Wang Monte Carlo algorithm.

Confinement can significantly alter fluid properties, offering potential for specific technological applications. However, achieving precise control over the structural complexity of confined fluids and soft matter remains challenging, as it often requires careful tuning of system parameters. In this study, we perform large-scale molecular dynamics simulations of a fluid confined in a slit pore, employing an interaction potential applicable to a broad range of soft matter. Confinement induces the fluid to form two-dimensional (2D) layers, each of which self-assembles into structures of varying complexity. Notably, we observe a suppression of large-scale density fluctuations-akin to crystals-within complex nontrivial fluid-like geometries, leading to the emergence of disordered hyperuniformity. We demonstrate that fluid properties can be precisely adjusted by modifying control parameters. This work provides a foundation for designing experimental protocols to regulate the structure of self-assembling materials, stabilize disordered hyperuniform structures, and facilitate the fabrication of 2D arrays with tailored complexity. Additionally, it offers a new perspective for analyzing biological monolayers, such as epithelial tissues.

Water's unique anomalies are vital in various applications and biological processes, yet the molecular mechanisms behind these anomalies remain debated, particularly in the metastable liquid phase under supercooling and stretching conditions. Experimental challenges in these conditions have led to simulations suggesting a liquid-liquid phase transition between low-density and high-density water phases, culminating in a liquid-liquid critical point (LLCP). However, these simulations are limited by computational expense, small system sizes, and reliability of water models. Using the FS model, we improve accuracy in predicting water's density and response functions across a broad range of temperatures and pressures. The FS model avoid by design first-order phase transitions towards crystalline phases, allowing thorough exploration of the metastable phase diagram. We employ advanced numerical techniques to bypass dynamical slowing down and perform finite-size scaling on systems significantly larger than those used in previous analyses. Our study extrapolates thermodynamic behavior in the infinite-system limit, accurately demonstrating the existence of the LLCP in the 3D Ising universality class at TC = 186 +/- 4 K and PC = 174 +/- 14 MPa, following a liquid-liquid phase separation below 200 MPa. These predictions align with recent experimental data and more sophisticated models, highlighting that hydrogen bond cooperativity governs the LLCP and the origin of water anomalies. Moreover, we observe that the hydrogen bond network exhibits substantial cooperative fluctuations at scales larger than 10 nm, even at temperatures relevant to biopreservation. These findings have significant implications for fields such as nanotechnology and biophysics, offering new insights into water's behavior under varied conditions.

Lipid nanoparticles own a remarkable potential in nanomedicine, only partially disclosed. While the clinical use of liposomes and cationic lipid-nucleic acid complexes is well-established, liquid lipid nanoparticles (nanoemulsions), solid lipid nanoparticles, and nanostructured lipid carriers have even greater possibilities. However, they face obstacles in being used in clinics due to a lack of understanding about the molecular mechanisms controlling their drug loading and release, interactions with the biological environment (such as the protein corona), and shelf-life stability. To create effective drug delivery carriers and successfully translate bench research to clinical settings, it is crucial to have a thorough understanding of the internal structure of lipid nanoparticles. Through synchrotron small-angle X-ray scattering experiments, we determined the spatial distribution and internal structure of the nanoparticles' lipid, surfactant, and the bound water in them. The nanoparticles themselves have a barrel-like shape that consists of coplanar lipid platelets (specifically cetyl palmitate) that are covered by loosely spaced polysorbate 80 surfactant molecules, whose polar heads retain a large amount of bound water. To reduce the interface cost of bound water with unbound water without stacking, the platelets collapse onto each other. This internal structure challenges the classical core-shell model typically used to describe solid lipid nanoparticles and could play a significant role in drug loading and release, biological fluid interaction, and nanoparticle stability, making our findings valuable for the rational design of lipid-based nanoparticles.

The molecular mechanisms of water's unique anomalies are still debated upon. Experimental challenges have led to simulations suggesting a liquid-liquid (LL) phase transition, culminating in the supercooled region's LL critical point (LLCP). Computational expense, small system sizes, and the reliability of water models often limit these simulations. We adopt the CVF model, which is reliable, transferable, scalable, and efficient across a wide range of temperatures and pressures around ambient conditions. By leveraging the timescale separation between fast hydrogen bonds and slow molecular coordinates, the model allows a thorough exploration of the metastable phase diagram of liquid water. Using advanced numerical techniques to bypass dynamical slowing down, we perform finite-size scaling on larger systems than those used in previous analyses. Our study extrapolates thermodynamic behavior in the infinite-system limit, demonstrating the existence of the LLCP in the 3D Ising universality class in the low-temperature, low-pressure side of the line of temperatures of maximum density, specifically at T-C = 186 +/- 4 K and P-C = 174 +/- 14 MPa, at the end of a liquid-liquid phase separation stretching up to similar to 200 MPa. These predictions align with recent experimental data and sophisticated models, highlighting that hydrogen bond cooperativity governs the LLCP and the origin of water anomalies. We also observe substantial cooperative fluctuations in the hydrogen bond network at scales larger than 10 nm, even at temperatures relevant to biopreservation. These findings have significant implications for nanotechnology and biophysics, providing new insights into water's behavior under varied conditions.

The blood-brain barrier is a highly complex physiological barrier that separates the blood from the central nervous system to maintain the latter's biological equilibrium. LDL receptor-related protein 1 (LRP1) is a receptor involved in BBB transcytosis and can be used by physiological or artificially induced processes. LRP1 is critical for the trafficking of misfolded proteins such as amyloid β, hyper-phosphorylated tau, and α-synuclein. Understanding its structure and function is essential to fully understanding neurological diseases like Alzheimer's, Parkinson's, Huntington's, and other related dementias. LRP1 is a modular membrane protein composed of 4544 amino acids, around 1200 of which are involved in three long and flexible structures that contain coordinated calcium ions and are decorated with small sugar chains called glycans. These three flexible components are believed to have an active role in ligand binding activity and activate a peculiar and very efficient transport mechanism. No crystal structure of LRP1 is currently available. Here, we present two LRP1 conformers representing the extremes of a conformational spectrum ranging from a completely stretched, oligomer-like structure to a stable dimeric structure. The first is based on physical and biological considerations and has been built with the RoseTTAFold deep learning tool; the second conformer is obtained via homology modelling using the experimental observation of a homologous protein, LRP2, as template. We aim to assess the two conformers' free energy variation considering the dynamics of the flexible domains obtained with atomistic molecular dynamics while rigidly fixing β-propellers' coordinates. The obtained conformations are solvated with the BF method to estimate the species' free energy of solvation.

Water is essential for innumerable natural, technological, and industrial activities. The advent of nanotechnology has allowed a better understanding of water at the nanoscale and its interaction with bio-organic, inorganic, and nanostructured interfaces. This knowledge offers clues to designing new nanomaterials or nanotechnological devices. Nowadays, a wide range of nanomaterials provides very efficient and tunable interfacial processes that can be exploited for many applications, including water remediation. Therefore, in this article, we survey the more representative nanomaterials and interfacial phenomena to enhance water purification processes. The article starts with the fundamental concepts of water at the nanoscale and its crucial role in interacting with surfaces. Then we discuss relevant nanointerfacial strategies to alleviate the problem of water pollution and shortage.

The separation of liquid mixture components is relevant to many applications—ranging from water purification to biofuel production—and is a growing concern related to the UN Sustainable Development Goals (SDGs), such as “Clean water and Sanitation” and “Affordable and clean energy”. One promising technique is using graphene slit-pores as filters, or sponges, because the confinement potentially affects the properties of the mixture components in different ways, favoring their separation. However, no systematic study has shown how the size of a pore changes the thermodynamics of the surrounding mixture. Here, we focus on water–methanol mixtures and explore, using Molecular Dynamics simulations, the effects of a graphene pore, with size ranging from 6.5 to 13 Å, for three compositions: pure water, 90%–10%, and 75%–25% water–methanol. We show that tuning the pore size can change the mixture pressure, density and composition in bulk due to the size-dependent methanol sequestration within the pore. Our results can help in optimizing the graphene pore size for filtering applications.