Systematic Comparison of the Structural and Dynamic Properties of Commonly Used Water Models for Molecular Dynamics Simulations

Probing the Degree of Restriction in Solvent Dynamics at the Interface of a Protein-RNA Complex

Protein-RNA complexation is an important step for the regulation of numerous biological processes. Water present at the interface of a protein-RNA complex plays a critical role in guiding its structure, stability, and function. Therefore, studying the microscopic properties of interfacial water is essential to gain molecular insights into the formation of such complexes. In this study, we present results obtained from molecular dynamics (MD) simulations of poly(A)-binding protein (PABP) bound with poly(A) RNA, which is an essential regulatory step to control the deadenylation process, thereby stabilizing cellular mRNAs from degradation. Efforts have been made to explore how such complexation alters the regular dynamical and hydrogen bond properties of water present at the interface. The calculations revealed restricted water dynamics at the interface, characterized by heterogeneous time scales, with the extent of restriction being more pronounced for residues directly involved in protein-RNA binding. In particular, water molecules around the protein's linker, RRM2, and the RNA strand exhibit significantly more restricted motion compared to RRM1 upon complexation. Further, longer relaxation times of hydrogen bonds at the interface due to complex formation have been found to be correlated with increasingly restricted water motions. Notably, the kinetics of hydrogen bonds around the protein's linker, RRM2, and the RNA strand are more strongly influenced by complex formation, underscoring their critical role in mediating protein-RNA interactions.

Neural Network-Based Molecular Dynamics Simulation of Water Assisted by Active Learning

In our study, we combined classical molecular dynamics (MD) simulations with the simulated annealing (SA) method to explore the conformational landscape of water molecules. By using the K-means clustering method, we processed the MD simulation data to extract representative samples of water molecular structures used to train a deep potential (DP) model. Our DeePMD method showed accuracy in predicting water structural properties compared to DFT-MD results. Meanwhile, this approach achieves a balanced prediction of water density and self-diffusion coefficients compared with earlier DeePMD simulations. These results highlight the essential role of representative sampling techniques in training the DP model. Furthermore, we demonstrated the effectiveness of combining the DeePMD simulation with the centroid molecular dynamics (CMD) approach, which incorporates nuclear quantum effects (NQEs). This approach successfully reproduced the shoulder feature at 3250 cm-1 in the Raman spectra for the O-H stretch. Incorporating the path integral method into the DeePMD simulations underscores the importance of considering NQEs in understanding water molecules' structural and dynamic behaviors.

Coarse-grained simulation of water: A comparative study and overview

In spite of the tremendous increase in computational power over the last few decades, the problem of simulating atomistic systems containing large amounts of water molecules over longer lengths and time scales still remains. In this respect, the coarse-grained (CG) force field reduces the computational cost and, therefore, allows simulations of larger systems for longer times. However, the specific scope of the different CG water models is more limited compared to their atomistic counterparts. In this context, we conducted a comparative study on the molecular physical structure, thermodynamic, and dynamic properties of bulk water systems using six distinct CG water models and all-atom (AA) simulations. The six CG simulation procedures involved modeling with three variants of the water model coming from the MARTINI force field, one from the SPICA force field, and the two Iterative Boltzmann Inversion (IBI) derived potentials from the AA simulations. The AA simulations have been performed using the SPC/E and TIP4P force fields. The IBI models, namely SPC/E-IBI and TIP4P-IBI, depict the structural features in close agreement with the atomistic samples. The explicit number of water molecules in the first coordination shell for the three MARTINI models and the SPICA force field is in excellent agreement with the SPC/E and TIP4P values. The ensuing simulated densities for the various water models align significantly with the literature data, indicating the reliability of our approach. The SPC/E and SPICA models stand out in predicting the enthalpy of vaporization among the all-atom and CG force fields, respectively. The two all-atom models and their IBI equivalents are better at representing the isobaric specific heat capacity compared to the other models. The isothermal compressibility is reproduced comprehensively by the SPC/E force field followed by TIP4P, while SPICA is the better choice within the CG models. With respect to the dynamics of the system, the diffusion coefficient of the SPICA force field is in perfect agreement with the experimental data, even better than the atomistic samples. The overall scores of the different models, indicative of their relative performances compared to the other models, have also been computed.

Fitting ambiguities mask deficiencies of the Debye-Hückel theory: revealing inconsistencies of the Poisson-Boltzmann framework and permittivity

The more than 100-year-old Debye-Hückel theory displays the most widely used approach for modeling ionic activities in electrolytes. The Debye-Hückel theory finds widespread application, such as in equations of state and Onsager's theory for conductivities. Here, a theoretical inconsistency of the Debye-Hückel theory is discussed, which originates from the employed Poisson-Boltzmann framework that violates the statistical independence of states presumed for the Boltzmann statistics. Furthermore, the static permittivity of electrolytic solutions is discussed as not directly measurable, while common methods for its extraction from experimental data are assessed as erroneous. A sensitivity analysis of modeled activity coefficients with respect to the permittivity and ionic radii as input parameters is conducted, showing that their influences overshadow physicochemical differences of common variations of Debye-Hückel models. Eventually, this study points out that the justification of the traditional and still often used Debye-Hückel models by experimental validation is affected by fitting ambiguities that eventually impede its predictive capabilities.

Surface Free Energies and Entropy of Aqueous CaCO3 Interfaces

This work uses a recently proposed methodology to calculate the free energies of calcite and aragonite interfaces with water. This method properly includes the entropic contributions, ignored or approximated in previous work. By including this entropic component, we show that the aqueous calcite {101̅4} surface has a lower free energy than any of the aragonite surfaces. This resolves the discrepancies in previous simulation work that suggested that an aragonite nucleus would be more stable than a calcite one. Our analysis of the water structure highlights the generally greater entropic contribution to the interfacial free energy at the aragonite/water interface than at the calcite one. These methods are applied to a range of temperatures to examine how the solution temperature alters the interfacial energies. Our results are then discussed in the context of calcium carbonate nucleation and polymorph-morphology selection under different environmental conditions.

Electrolyte Anions Suppress Hydrogen Generation in Electrochemical CO Reduction on Cu

In this study, we employed electrochemical-mass spectrometry (EC-MS) to elucidate the role of halide anions in electrochemical CO2 and CO reduction. We found that the undesired hydrogen evolution reaction (HER) was significantly suppressed by the anion used. Specifically, the rates of H2 production decreased in the order KF > KCl > KI, meaning that I- most strongly suppressed HER. Interestingly, CO reduction products showed an inverse relationship to HER, with KI leading to the highest rate of CO reduction. By pairing our experimental findings with classical molecular dynamics simulations, we propose a mechanism wherein halide anions influence the dynamic interplay between CO reduction and HER by modulating the competition of H* and CO* for active sites on the Cu surface. We propose that this interaction is enabled by the interfacial concentration of K+ being greater in the presence of F- than in I-. Our results highlight the need to more broadly consider the properties of ions at electrocatalytic interfaces and they point to thus far underappreciated avenues to optimize hydrocarbon production while suppressing hydrogen evolution.

Maximum Entropy-Mediated Liquid-to-Solid Nucleation and Transition

Molecular dynamics (MD) simulations are a powerful tool for studying matter at the atomic scale. However, to simulate solids, an initial atomic structure is crucial for the successful execution of MD simulations but can be difficult to prepare due to insufficient atomistic information. At the same time, wide-angle X-ray scattering (WAXS) measurements can determine the radial distribution function (RDF) of atomic structures. However, the interpretation of RDFs is often challenging. Here, we present an algorithm that can bias MD simulations with RDFs by combining the information on the MD atomic interaction potential and the RDF under the principle of maximum relative entropy. We show that this algorithm can be used to adjust the RDF of one liquid model, e.g., the TIP3P water model, to reproduce the RDF and improve the angular distribution function (ADF) of another model, such as the TIP4P/2005 water model. In addition, we demonstrate that the algorithm can initiate crystallization in liquid systems, leading to both stable and metastable crystalline states defined by the RDF, e.g., crystallization of water to ice and liquid TiO2 to rutile or anatase. Finally, we discuss how this method can be useful for improving interaction models, studying crystallization processes, interpreting measured RDFs, or training machine-learned potentials.

Nonequilibrium Binding Free Energy Simulations: Minimizing Dissipation

While nowadays approaches for equilibrium free energy estimation are well established, nonequilibrium simulations represent both an appealing computational opportunity and a challenge. This kind of simulations allows for a trivially parallel scheme, but at the same time the significant amount of irreversible work often generated during the steering process (either alchemical or physical) can hinder the convergence of free energy estimators. Here, we discuss in depth this issue for the protein-ligand binding free energy estimation carried out via physical paths. We found that the water model and the parametrization of the path collective variables have a remarkable impact on the convergence rate of the estimators (e.g., Crooks). Finally, we provide practical recipes to enhance the convergence speed and minimize dissipation.

Study on Influence of Microwave Electric Field Direction on Evaporation Based on Molecular Dynamics

Microwave-assisted evaporation technology is widely used today, but its molecular mechanism is not fully understood. To investigate the molecular mechanism of the influence of microwave electric field direction on water evaporation, this paper designed experiments to measure the microwave energy required to evaporate each gram of water with electric field directions parallel and perpendicular to the water surface. The temperature rise curve of the water is controlled to be consistent in both cases, and the temperature distribution of the water is made uniform by stirring. We found a stable difference in the energy absorbed by the two situations, indicating that the direction of the microwave electric field has an effect on water evaporation. In this article, the evaporation behavior of water molecules under different directional electric fields was then studied by molecular dynamics. The SPCE water molecule model, which simulates pure water systems well, was selected for this calculation. Based on the GROMOS force field, the evaporation enthalpy and surface tension of water under various conditions at constant temperature were calculated and the conclusions were analyzed. The calculated results are in agreement with the experimental results, and the influence of the evaporation enthalpy of water molecules is related to the direction of the microwave electric field.

High-Performance Proton Exchange Membrane with Vertically Aligned Montmorillonite Nanochannels

The traditional perfluorosulfonic acid proton exchange membrane is crucial for proton exchange membrane fuel cells, but its high cost has impeded broader commercialization. In this study, a novel concept of a cost-effective and stable vertically aligned polydopamine-intercalated montmorillonite membrane (VAPMM) is introduced. 2D nanochannels formed within the lamellar structure of polydopamine-coated montmorillonite nanosheets provide a significant stable in-plane proton conductivity of 0.58 S cm-1. The stacked lamellar structure is embedded in epoxy resin to maintain its orientation. Subsequently, precise slicing along the vertical direction of the 2D nanochannels yields a thin film ≈150 µm thick, featuring vertically aligned proton conductive transmembrane nanochannels. When assembled into a membrane electrode assembly with commercial gas diffusion electrodes, the VAPMM exhibits a maximum areal peak power density of up to 534.00 mW cm-2 at 75 °C with 100% RH, surpassing by more than four times that of a commercial Nafion membrane of similar thickness (N117, 183 µm, 116.17 mW cm-2). This study outlines a pathway for developing next-generation proton exchange membranes that are both cost-effective and highly stable. Additionally, it introduces a straightforward method to create fully vertically aligned transmembrane nanochannels while preserving the interlayer structure, which is crucial for advancements in nanofluidics.

Hydration-induced dynamical changes in lyophilised and weakly hydrated apoferritin: insights from molecular dynamics simulation

The dynamics and functionality of proteins are significantly influenced by their interaction with water. For lyophilised (i.e. h ≤ 0.05 where h = g of H2O per g of protein) and weakly hydrated systems (i.e. h ≤ 0.38) hydration generally enhances protein mobility above the so-called 'dynamical transition' temperature (Td > 220 K). However, water-induced mobility hindrance at low temperatures (T < 175 K) has been reported in various proteins of varying secondary structure; namely green fluorescent protein (GFP), pig liver esterase, lysozyme, ribonuclease A (RNAse A) and apoferritin. By focussing on the dynamic behaviour of the apoferritin molecule, this study proposes mechanisms driving these hydration-induced mobility changes, particularly the less understood hindrance at low temperatures. Using atomistic molecular dynamics (MD) simulations of horse spleen apoferritin in the lyophilised (h = 0.05) and weakly hydrated (h = 0.31) states, we report here the impact of water on protein dynamics as a function of temperature. Through residue-specific mean squared displacement (MSD), radial distribution function (RDF), solvent accessible surface area (SASA), local hydration degree and hydrogen bonding analyses, we demonstrate that while water proximity directly correlates with mobility enhancement at high temperatures, the hydration-induced mobility reduction observed at temperatures below 175 K is primarily propagated through the protein backbone.

The evolution of the Amber additive protein force field: History, current status, and future

Molecular dynamics simulations are pivotal in elucidating the intricate properties of biological molecules. Nonetheless, the reliability of their outcomes hinges on the precision of the molecular force field utilized. In this perspective, we present a comprehensive review of the developmental trajectory of the Amber additive protein force field, delving into researchers' persistent quest for higher precision force fields and the prevailing challenges. We detail the parameterization process of the Amber protein force fields, emphasizing the specific improvements and retained features in each version compared to their predecessors. Furthermore, we discuss the challenges that current force fields encounter in balancing the interactions of protein-protein, protein-water, and water-water in molecular dynamics simulations, as well as potential solutions to overcome these issues.

Toward mitigating the impact of non-bulk defects on describing water structure in salt aqueous solutions: Characterizing solution density with a network-based structural indicator

Structural indicators, also known as structural descriptors, including order parameters, have been proposed to quantify the structural properties of water to account for its anomalous behaviors. However, these indicators, mainly designed for bulk water, are not naturally transferrable to the vicinity of ions due to disruptions in the immediate neighboring space and a resulting loss of feature completeness. To address these non-bulk defects, we introduced a structural indicator that draws on the concept of clique number from graph theory and the criterion in agglomerative clustering, denoted as the average cluster number. This structural indicator aims to discern intrinsic structural characteristics within the water molecules regardless of the ions occupying the neighboring space, without requiring additional corrections. From molecular dynamics simulation results for neat water and salt aqueous solutions utilizing the TIP4P/2005 water model and the Madrid-2019 force field, we characterized the variations in densities with temperature using this network-based indicator, thereby demonstrating its practical utility. The findings suggest that at lower temperatures, the addition of ions disrupts the intrinsic structure of water molecules, with this effect diminishing as the temperature rises. Cations with larger charge density tend to induce stronger disruptions. This study highlights the importance of mitigating the impact of non-bulk defects before applying the indicators to analyze water's intrinsic structural properties in solutions. By doing so, the relationship between changes in water structure and solution behaviors can be more accurately assessed.

Water Migration through Enzyme Tunnels Is Sensitive to the Choice of Explicit Water Model

The utilization of tunnels and water transport within enzymes is crucial for their catalytic function as water molecules can stabilize bound substrates and help with unbinding processes of products and inhibitors. Since the choice of water models for molecular dynamics simulations was shown to determine the accuracy of various calculated properties of the bulk solvent and solvated proteins, we have investigated if and to what extent water transport through the enzyme tunnels depends on the selection of the water model. Here, we focused on simulating enzymes with various well-defined tunnel geometries. In a systematic investigation using haloalkane dehalogenase as a model system, we focused on the well-established TIP3P, OPC, and TIP4P-Ew water models to explore their impact on the use of tunnels for water molecule transport. The TIP3P water model showed significantly faster migration, resulting in the transport of approximately 2.5 times more water molecules compared to that of the OPC and 1.7 times greater than that of the TIP4P-Ew. Finally, the transport was 1.4-fold more pronounced in TIP4P-Ew than in OPC. The increase in migration of TIP3P water molecules was mainly due to faster transit times through dehalogenase tunnels. We observed similar behavior in two different enzymes with buried active sites and different tunnel network topologies, i.e., alditol oxidase and cytochrome P450, indicating that our findings are likely not restricted to a particular enzyme family. Overall, this study showcases the critical importance of water models in comprehending the use of enzyme tunnels for small molecule transport. Given the significant role of water availability in various stages of the catalytic cycle and the solvation of substrates, products, and drugs, choosing an appropriate water model may be crucial for accurate simulations of complex enzymatic reactions, rational enzyme design, and predicting drug residence times.

A Micro Insight of Water Permeation in Polyurethane: Navigating for Water Transport

Polyurethane (PU) grouting materials are widely used in underground engineering rehabilitation, particularly in reinforcement and waterproofing engineering in deep-water environments. The long-term effect of complex underground environments can lead to nanochannel formation within PU, weakening its repair remediation effect. However, the permeation behavior and microscopic mechanisms of water molecules within PU nanochannels remain unclear. In this paper, a model combining PU nanochannels and water molecules was constructed, and the molecular dynamics simulations method was used to study the effects of water pressure and channel width on permeation behavior and microstructural changes. The results reveal a multi-stage, layered permeation process, with significant acceleration observed at water pressures above 3.08 MPa. Initially, water molecules accelerate but are then blocked by the energy barrier of PU nanochannels. After about 20 ps, water molecules overcome the potential barrier and enter the nanochannel, displaying a secondary acceleration effect, with the maximum permeation depth rises from 1.8 nm to 11.8 nm. As the channel width increases, the maximum permeation depth increases from 7.5 nm to 11.6 nm, with the rate of increase diminishing at larger widths. Moreover, higher water pressure and wider channels enhance the stratification effect. After permeation, a hydrophobic layer of approximately 0.5 nm thickness forms near the channel wall, with a density lower than that of the external water. The middle layer shows a density slightly higher than the external water, and the formation of hydrogen bonds between water molecules increases toward the channel center.

A self-assembling cytotoxic nanotherapeutic strategy for high drug loading and synergistic delivery of molecularly targeted therapies

Despite significant advancements in anticancer nanotherapeutics, the efficient encapsulation of multiple therapeutic modalities within single nanocarriers remains challenging due to the complex requirements of supramolecular self-assembly and/or chemical modification. These intricate synthesis procedures often impede the clinical translation of promising nanomedicines. In this study, we introduce a cost-effective and straightforward self-assembling cytotoxic nanotherapeutic strategy that enables the noncovalent incorporation of water-insoluble anticancer molecular inhibitors with high drug loading. This was achieved through the lipid conjugation of camptothecin, enabling nanoassembly in aqueous solutions devoid of excipients. These nanoassemblies were further developed into nanovehicles capable of encapsulating a high capacity of structurally diverse cargos, including molecularly targeted agents. Notably, nanoassemblies composed of linoleic acid-conjugated camptothecin and sorafenib demonstrated stability and sustained release of their payloads. The combination nanoparticles exhibited synergistic effects and effectively overcame ABCG2-mediated drug resistance in hepatocellular carcinoma (HCC). Systemic administration of these nanotherapeutics led to sustained tumor growth inhibition in various HCC xenograft-bearing mouse models, including a chemically induced orthotopic HCC model. This innovative supramolecular assembly strategy, which allows a single vehicle to deliver multimodal therapies, shows promise in overcoming drug resistance in human HCC and could be adapted for the development of other injectable nanomedicines, warranting further investigation. STATEMENT OF SIGNIFICANCE: This study advances anticancer nanotherapy by developing a simple and cost-effective self-assembling strategy that enables high loading of multiple water-insoluble chemotherapeutics. Using lipid-conjugated camptothecin, we created stable nanoassemblies capable of synergistically delivering diverse molecularly targeted agents. This combinatory platform effectively overcame therapeutic resistance and demonstrated sustained tumor inhibition in hepatocellular carcinoma-bearing mouse models. This new self-assembling cytotoxic nanotherapeutic strategy has potential applications for the development of other injectable nanomedicines.

Water transport mechanisms during pressure-driven transport through polyamide nanogaps

Molecular-scale simulations of pressure-driven transport through polyamide nanogaps (5-100 Å) were performed to investigate fundamental transport mechanisms. Results show that transport in nanogaps ≤  10 Å is always subdiffusive, but superdiffusive transport was observed in nanogaps ≥  20 Å. Near typical operating pressures for applications ( Δ p  = 100 atm), only the 100 Å nanogap exhibited superdiffusive behavior. Since openings in common membrane materials are typically <20 Å, results indicate that subdiffusive to diffusive transport dominates for typical applications, such as reverse osmosis.

Electrified Nanogaps under an AC Field: A Molecular Dynamics Study

The organization and dynamics of ions and water molecules at electrified solid-liquid interfaces are generally well understood under static fields, especially for macroscopic electrochemical systems. In contrast, studies involving alternating (AC) fields tend to be more challenging. In nanoscale systems, added complexity can arise from interfacial interactions and the need to consider ions and molecules explicitly. Here we use molecular dynamics (MD) simulations to investigate the behavior of NaCl aqueous solutions at different concentrations confined in nanogaps under AC fields ranging from 10 MHz to 10 GHz. We explore the impact of the gap size (2-60 nm) and of the solid material composing the electrode (silica, charged silica, or gold). Analysis of the transient and stable responses of the system shows that the total transverse dipole M z,total formed by the water molecules and the ions across the gap is always able to counter the applied field regardless of AC frequency, NaCl concentration, or electrode material. As expected, the ions lag at higher frequencies, leading to a capacitive behavior. This effect is fully compensated by water dipoles that lead the field, reaching a maximum lead at a specific frequency which depends on salt concentration and gap size. Changing the gap size affects the magnitude of M z,total. Finally, the electrode material is shown to affect the electrolyte behavior in the gap region. We anticipate these results to be useful for nanoscale dielectric spectroscopy, including scanning probes.

Unveiling Hierarchical Self-Assembly of Triazolylferrocenyl Dendrimers: Producing Non-Traditional Intrinsically Green Fluorescent Vesosomes for Nanotheranostics

Dendrimers and supramolecular chemistry continue to fascinate researchers due to the endless unrevealed potential of their combination. This study investigates the self-assembly process of a series of hydrophobic triazolylferrocenyl dendrimers in aqueous medium. Deep investigation through NMR spectroscopy, absorption UV-vis spectroscopy along with theoretical simulations demonstrates that the ferrocenyl moieties interact intramolecularly and intermolecularly driving the self-assembly process. Data obtained by DLS, NTA, SEM, TEM, and EF-TEM demonstrate that these dendrimers, in water, spontaneously self-assemble through a hierarchical process. The dendrimers first self-assemble into uniform nanovesicles, which in turn self-assemble into larger vesosomes. The resulting vesosomes emit green non-traditional intrinsic fluorescence, which is a property that emerged from the self-assembled architectures. The vesosomes are efficiently uptaken by cancer cells and induce significant cytotoxic activity against the cancer cell line MCF-7, up to the submicromolar concentration. Positive dendritic effects are identified in the fluorescence intensity and in the cytotoxic activity of the vesosomes, which follow the trend G0-9Fc < G1-27Fc < G2-81Fc. This work showcases the remarkable potential of combining the two dynamic fields of dendrimers and supramolecular chemistry, which resulted in green fluorescent vesosomes capable of performing the dual role of cell imaging and killing, with potential applications in nanotheranostics.

Paraherquamides - A new hope and great expectations of anthelmintic agents: Computational studies

Nematode infections impose a significant health and economic burden, particularly as parasites develop resistance to existing treatments and evade host defenses. This study explores the efficacy of 48 paraherquamide analogs, a class of polycyclic spiro-oxindole alkaloids with unique structural features, as potential anthelmintic agents. Employing advanced computational methods, including molecular docking, MM-GBSA, and molecular dynamics simulations, we assessed the interaction of these analogs with the Ls-AchBP receptor, a model for nematode neurotransmission. Among the analogs studied, Paraherquamide K, Mangrovamide A, and Chrysogenamide A showed comparable docking and MM-GBSA scores to the native antagonist. Notably, their binding interactions exhibited slight distinction attributed to structural differences, such as the absence of a di-oxygenated 7-membered ring. Additionally, these analogs demonstrated robust binding stability in the molecular dynamic simulation studies and favorable pharmacokinetic properties in our in-silico ADME assessment. The insights gained from the study highlight the potential of these analogs as a basis for developing new therapeutics for nematode infections. The promising results from this computational analysis set the stage for subsequent in-vivo validations and pre-clinical studies, contributing to the arsenal against parasitic resistance.

Force Fields for Deep Eutectic Mixtures: Application to Structure, Thermodynamics and 2D-Infrared Spectroscopy

Parametrizing energy functions for ionic systems can be challenging. Here, the total energy function for an eutectic system consisting of water, SCN-, K+ and acetamide is improved vis-a-vis experimentally measured properties. Given the importance of electrostatic interactions, two different types of models are considered: the first (model M0) uses atom-centered multipole whereas the other two (models M1 and M2) are based on fluctuating minimal distributed charges (fMDCM) that respond to geometrical changes of SCN-. The Lennard-Jones parameters of the anion are adjusted to best reproduce experimentally known hydration free energies and densities, which are matched to within a few percent for the final models irrespective of the electrostatic model. Molecular dynamics simulations of the eutectic mixtures with varying water content (between 0 and 100%) yield radial distribution functions and frequency correlation functions for the CN-stretch vibration. Comparison with experiments indicates that models based on fMDCM are considerably more consistent than those using multipoles. Computed viscosities from models M1 and M2 are within 30% of measured values and their change with increasing water content is consistent with experiments. This is not the case for model M0.

Mitochondria-endoplasmic reticulum crosstalk in apoptosis: The interactions of cytochrome c with monooxygenase and its reductase

The crosstalk between endoplasmic reticulum and mitochondria is of significance in apoptosis, in which cytochrome b5 (Cyt b5) is thought to be a major target for cytochrome c (Cyt c) upon its release from the mitochondria. In the absence of Cyt b5, the role of interactions of Cyt c with CYP-dependent monooxygenase system in apoptotic regulation was explored in this study. NADPH-dependent and Cyt c-induced formation of reactive oxygen species (ROS) and NADPH-independent Cyt c unfolding were revealed. With the aid of a CPR inhibitor and CYP antibodies, the interactions among Cyt c, cytochrome P450 reductase (CPR) and cytochrome P450 (CYP) are evidenced, which are found crucial for monooxygenase-derived ROS formation. The underlying structural basis of Cyt c-CYP complex was unveiled by molecular dynamics simulations. This study provides novel insights into how Cyt c regulates ROS formation through the interactions with CPR and CYP, and is implicated for a deeper understanding of the regulation mechanism in the mitochondria-endoplasmic reticulum apoptotic pathway.

Initiation of Medial Calcification: Revisiting Calcium Ion Binding to Elastin

Pathological calcification of elastin, a key connective tissue protein in the medial layers of blood vessels, starts with the binding of calcium ions. This Mini-Review focuses on understanding how calcium ions interact with elastin to initiate calcification at a molecular level, and emphasizes water's critical role in mediating this interaction. In the past decade, great strides have been made in understanding and modeling ion-specific hydration and its effects on biomolecule interactions. However, these advances have been largely absent from our understanding of elastin calcification. Historically, charge-neutral backbone carbonyls and negatively charged carboxyl groups have been proposed as elastin's calcium binding sites. Recently, tropoelastin's only four carboxyl groups have been identified as binding sites from classical molecular dynamics (MD). While carboxyl groups have a much higher affinity for binding calcium ions than backbone carbonyls, conflicting evidence persists for both functional group's importance in elastin calcification. This can be attributed to the fact that divalent ions strongly polarize water, leading to a hydration shell that shields electrostatic forces. The hydration shell surrounding both a calcium ion and either of the proposed binding sites must be displaced to enable binding. Providing our own extended X-ray absorption fine structure (EXAFS) data and complementary simulations, we discuss the potential structures of calcium binding in elastin and review prior knowledge regarding the relative importance of the two proposed binding sites.

Advanced sampling simulations of coupled folding and binding of phage P22 N-peptide to boxB RNA

Protein-RNA interactions are crucially important for numerous cellular processes and often involve coupled folding and binding of peptide segments upon association. The Nut-utilization site (N)-protein of bacteriophages contains an N-terminal arginine-rich motif that undergoes such a folding transition upon binding to the boxB RNA hairpin loop target structure. Molecular dynamics free energy simulations were used to calculate the absolute binding free energy of the N-peptide of bacteriophage P22 in complex with the boxB RNA hairpin motif at different salt concentrations and using two different water force field models. We obtained good agreement with experiment also at different salt concentrations for the TIP4P-D water model that has a stabilizing effect on unfolded protein structures. It allowed us to estimate the free energy contribution resulting from restricting the molecules' spatial and conformational freedom upon binding, which makes a large opposing contribution to binding. In a second set of umbrella sampling simulations to dissociate/associate the complex along a separation coordinate, we analyzed the onset of preorientation of the N-peptide and onset of structure formation relative to the RNA and its dependence on the salt concentration. Peptide orientation and conformational transitions are significantly coupled to the first contact formation between peptide and RNA. The initial contacts are mostly formed between peptide residues and the boxB hairpin loop nucleotides. A complete transition to an α-helical bound peptide conformation occurs only at a late stage of the binding process a few angstroms before the complexed state has been reached. However, the N-peptide orients also at distances beyond the contact distance such that the sizable positive charge points toward the RNA's center-of-mass. Our result may have important implications for understanding protein- and peptide-RNA complex formation frequently involving coupled folding and association processes.

Calculated hydration free energies become less accurate with increases in molecular weight

In order for computer-aided drug design to fulfil its long held promise of delivering new medicines faster and cheaper, extensive development and validation work must be done first. This pertains particularly to molecular dynamics force fields where one important aspect-the hydration free energy (HFE) of small molecules-is often insufficiently analyzed. While most benchmarking studies report excellent accuracies of calculated hydration free energies-usually within 2 kcal/mol of experimental values-we find that deeper analysis reveals significant shortcomings. Herein, we report a dependence of HFE prediction errors on ligand molecular weight-the higher the weight, the bigger the prediction error and the higher the probability the calculated result is erroneous by a large amount. We show that in the drug-like molecular weight region, HFE predictions can easily be off by 5 kcal/mol or more. This is likely to be highly problematic in a drug discovery and development setting. We make our HFE results and molecular descriptors freely and fully available in order to encourage deeper analysis of future molecular dynamics results and facilitate development of the next generation of force fields.

Prediction of binding affinity and enthalpy of CB7 with alkaloids by attach-pull-release molecular dynamics simulations study

Host-guest complex has attracted much attention because of their fantastic capability. Accurate prediction of their binding affinity and enthalpy is essential to the rational design of guest molecules. The attach-pull-release (APR) method proposed by Henriksen et al. (J. Chem. Theory Comput., 2015, 11:4377.) shows good prediction capability of binding affinity especially for host-guest system. In order to further evaluate the performance of APR method in practice, we have conducted the calculations on the macrocycle cucurbit [7]urils (CB7) encapsulated with four structurally similar alkaloids (berberine, coptisine, epiberberine and palmatine) with two force fields (GAFF and GAFF2) and three water models (TIP3P, SPC/E and OPC). Compared to the experimental data, the calculation by the combination of GAFF2 and SPC/E force field presents the best performance, of which the Pearson correlation coefficients (R2) is 0.95, and the root-mean-square-deviation is 3.04 kcal/mol. While the predictions from GAFF force field all overestimated the binding affinity, suggesting a systematic error may be involved. Comparison of calculation also indicates that the accuracy of prediction was susceptible to the combination of force field. Therefore, it would be necessary to repeat the simulation with different combination of force fields in practice.

Water will Find Its Way: Transport through Narrow Tunnels in Hydrolases

An aqueous environment is vital for life as we know it, and water is essential for nearly all biochemical processes at the molecular level. Proteins utilize water molecules in various ways. Consequently, proteins must transport water molecules across their internal network of tunnels to reach the desired action sites, either within them or by functioning as molecular pipes to control cellular osmotic pressure. Despite water playing a crucial role in enzymatic activity and stability, its transport has been largely overlooked, with studies primarily focusing on water transport across membrane proteins. The transport of molecules through a protein's tunnel network is challenging to study experimentally, making molecular dynamics simulations the most popular approach for investigating such events. In this study, we focused on the transport of water molecules across three different α/β-hydrolases: haloalkane dehalogenase, epoxide hydrolase, and lipase. Using a 5 μs adaptive simulation per system, we observed that only a few tunnels were responsible for the majority of water transport in dehalogenase, in contrast to a higher diversity of tunnels in other enzymes. Interestingly, water molecules could traverse narrow tunnels with subangstrom bottlenecks, which is surprising given the commonly accepted water molecule radius of 1.4 Å. Our analysis of the transport events in such narrow tunnels revealed a markedly increased number of hydrogen bonds formed between the water molecules and protein, likely compensating for the steric penalty of the process. Overall, these commonly disregarded narrow tunnels accounted for ∼20% of the total water transport observed, emphasizing the need to surpass the standard geometrical limits on the functional tunnels to properly account for the relevant transport processes. Finally, we demonstrated how the obtained insights could be applied to explain the differences in a mutant of the human soluble epoxide hydrolase associated with a higher incidence of ischemic stroke.

Getting the intermolecular forces correct: introducing the ASTA strategy for a water model

Having a force field for water providing good bulk properties is paramount for modern studies of most biological systems. Some of the most common three-site force fields are TIP3, SPC/ε or OPC3, providing a decent range of bulk properties. That does not mean though, that they have realistic inter-atomic forces. These force fields have been parameterized with a top-down approach, meaning, by fitting the force field parameters to the experimental bulk properties. This approach has been the governing strategy also for many variants of four- and more-site models. We test a bottom-up approach, in which the force field is parameterized by optimizing the non-bonded inter-atomic forces. Our philosophy is that correct inter-atomic forces lead to correct geometrical and dynamical properties. The first system we try to optimize with the accurately system tailored atomic (ASTA) approach is water, but we aim to eventually probe other systems in the future as well. We applied our ASTA strategy to find a good set of parameters providing accurate bulk properties for the simple three-site force field forms, and also for AMOEBA, a more detailed and polarizable force field. Even though our bottom-up approach did not provide satisfactory results for the simple three-site force fields (with fixed charges), for the case of the AMOEBA force field it led to a modification of the original strategy, giving very good intra- and inter-molecular forces, as compared to accurate quantum chemically calculated reference forces. At the same time, important bulk properties, in this study restricted to the density and diffusion, were accurately reproduced with respect to the experimental values.

Response of Foldable Protein Conformations to Non-Physiological Perturbations: Interplay of Thermal Factors and Confinement

Technological advances frequently interface biomolecules with nanomaterials at non-physiological conditions, necessitating response characterization of key processes. Similar encounters are expected in cellular contexts. We report in silico investigations of the response of diverse protein conformational states to lowering of temperature and imposition of spatial constraints. Conformational states are represented by folded form of the Albumin binding domain (ABD) protein, its compact denatured form, and structurally disordered nascent folding elements. Data from extensive simulations are evaluated to elicit structural, thermodynamic and dynamic responses of the states and their associated environment. Analyses reveal alterations to folding propensity with reduced thermal energy and confinement, with signatures of trend reversal in highly disordered states. Across temperatures, confinement has restrictive effects on volume and energetic fluctuations, leading to narrowing of differences in isothermal compressibility (κ) and heat capacities (Cp). While excess (over ideal gas) entropy of the hydration layer marks dependence on the conformational state at bulk, confinement triggers erasure of differences. These observations are largely consistent with timescales of protein-water hydrogen bonding dynamics. The results implicate multi-factorial associations within a simple bio-nano complex. We expect the current study to motivate investigations of more biologically relevant interfaces towards mechanistic understanding and potential applications.

Dynamics of confined water inside carbon nanotubes based on studying tetrahedral order parameters

Water dynamics inside hydrophobic confinement, such as carbon nanotubes (CNTs), has garnered significant attention, focusing on water diffusion. However, a crucial aspect remains unexplored - the influence of confinement size on water ordering and intrinsic hydrogen bond dynamics. To address this gap, we conducted extensive molecular dynamics simulations to investigate local ordering and intrinsic hydrogen bond dynamics of water molecules within CNTs of various sizes (length:20 nm, diameters: 1.0 nm to 5.0 nm) over a wide range of temperatures (260K, 280K, 300K, and 320K). A striking observation emerged: in smaller CNTs, water molecules adopt an icy structure near tube walls while maintaining liquid state towards the center. Notably, water behavior within a 2.0 nm CNT stands out as an anomaly, distinct from other CNT sizes considered in this study. This anomaly was explained through the formation of water layers inside CNTs. The hydrogen bond correlation function of water within CNTs decayed more slowly than bulk water, with an increasing rate as CNT diameter increased. In smaller CNTs, water molecules hold onto their hydrogen bond longer than larger ones. Interestingly, in larger CNTs, the innermost layer's hydrogen bond lasts a shorter time compared to the other layers, and this changes with temperature.

Development, validation and analysis of a human profurin 3D model using comparative modeling and molecular dynamics simulations

The emergence of new viruses can lead to the outbreak of pandemics as occurred at the end of 2019 with the coronavirus disease (or COVID-19). The fastest way to effectively control viral infections is to develop broad-spectrum antivirals that can fight at least an entire class of viruses. Profurin, the furin precursor propeptide, is responsible for the autoactivation step which is crucial for the maturation of several viral substrates. This role makes the study of furin and profurin interactions interesting for the development of new potential broad-spectrum antivirals for the treatment against several human viral diseases. Since there is no 3D model of profurin published in the literature or deposited in a database, this work reports the development, validation and analysis of a profurin 3D model using comparative modeling and molecular dynamics. The model is available in ModelArchive at https://www.modelarchive.org/doi/10.5452/ma-ct8l7. The usage of this model will make possible further studies of molecular docking and MD simulations of the profurin-furin system, in the design of new potential broad-spectrum antivirals for the treatment against several human viral diseases.Communicated by Ramaswamy H. Sarma.

General Aqueous System Simulation through an AI-Embedded Metaverse Chemistry Laboratory

Recent decades have witnessed the rapid development of autonomous laboratories and artificial intelligence, where experiments can be automatically run and optimized. Although human work is reduced, the total time of experimental optimization is still consuming due to limitations of the current ab metaverse framework, which accurately predicts the future state of the system by receiving and analyzing in situ experimental data. To substitute for traditional simulation methods, we designed a physically endorsed deep learning model to predict the future system picture ranging from atomic image to bulk appearance, intensively using the correlations between properties of the system. Through this framework, we studied the general aqueous system, covering 100+ common ionic solutions. We can accurately simulate properties for a general aqueous system as well as predict the time of solvation of ionic compounds ahead of real experiments. In this way, the experiments can be optimized more efficiently without waiting for the end of a bad iteration. We hope our work offers a fresh direction for the digitization of chemical information, enhancing access to and use of experimental data in advancing the field of physical chemistry.

Toward a Better Understanding of the Poly(glycerol sebacate)-Water Interface

Molecular simulations were conducted to provide a better description of the poly(glycerol sebacate) (PGS)-water interface. The density and the glass-transition temperature as well as their dependencies on the degree of esterification were examined in close connection with the available experimental data. The work of adhesion and water contact angle were calculated as a function of the degree of esterification. A direct correlation was established between the strength of the hydrogen bond network in the interfacial region and the change in the water contact angle with respect to the degree of esterification. The interfacial region was described by local density profiles and orientations of the water molecules.

The emergence of machine learning force fields in drug design

In the field of molecular simulation for drug design, traditional molecular mechanic force fields and quantum chemical theories have been instrumental but limited in terms of scalability and computational efficiency. To overcome these limitations, machine learning force fields (MLFFs) have emerged as a powerful tool capable of balancing accuracy with efficiency. MLFFs rely on the relationship between molecular structures and potential energy, bypassing the need for a preconceived notion of interaction representations. Their accuracy depends on the machine learning models used, and the quality and volume of training data sets. With recent advances in equivariant neural networks and high-quality datasets, MLFFs have significantly improved their performance. This review explores MLFFs, emphasizing their potential in drug design. It elucidates MLFF principles, provides development and validation guidelines, and highlights successful MLFF implementations. It also addresses potential challenges in developing and applying MLFFs. The review concludes by illuminating the path ahead for MLFFs, outlining the challenges to be overcome and the opportunities to be harnessed. This inspires researchers to embrace MLFFs in their investigations as a new tool to perform molecular simulations in drug design.

Understanding X-ray absorption in liquid water using triple excitations in multilevel coupled cluster theory

X-ray absorption (XA) spectroscopy is an essential experimental tool to investigate the local structure of liquid water. Interpretation of the experiment poses a significant challenge and requires a quantitative theoretical description. High-quality theoretical XA spectra require reliable molecular dynamics simulations and accurate electronic structure calculations. Here, we present the first successful application of coupled cluster theory to model the XA spectrum of liquid water. We overcome the computational limitations on system size by employing a multilevel coupled cluster framework for large molecular systems. Excellent agreement with the experimental spectrum is achieved by including triple excitations in the wave function and using molecular structures from state-of-the-art path-integral molecular dynamics. We demonstrate that an accurate description of the electronic structure within the first solvation shell is sufficient to successfully model the XA spectrum of liquid water within the multilevel framework. Furthermore, we present a rigorous charge transfer analysis of the XA spectrum, which is reliable due to the accuracy and robustness of the electronic structure methodology. This analysis aligns with previous studies regarding the character of the prominent features of the XA spectrum of liquid water.

Study on the Transport Properties of SO2 and NO at the Interface of H2O2 Solutions Using Molecular Dynamics

Gas-liquid interfaces have a unique structure different from the bulk phase due to the complex intermolecular interactions within them and are regarded as barriers that prevent gases from entering solution or as channels that affect gas reactions. In this study, the adsorption and mass-transfer mechanisms of sulfur dioxide and nitric oxide at the gas-liquid interface of a H2O2 solution were comprehensively analyzed using molecular dynamics (MD) simulations. The analysis on molecule angle showed that H2O molecules tended to align parallel to the solution surface on the surface of the H2O2 solution. Regardless of whether the gas was adsorbed on the surface of the solution or not, H2O2 molecules were always perpendicular to the interface of the solution. The analysis on molecule angle and radial distribution function revealed that the H atoms of H2O molecules had a corresponding turn, and SO2 molecules were greatly affected by the attraction of H2O2 molecules during the adsorption of gas molecules on the interface. Steered MD was utilized to investigate the mass-transfer process of SO2 and NO molecules across the gas-liquid interface. The S atoms of SO2 molecules were significantly influenced by H2O2 molecules, while the O atoms of NO molecules gradually transitioned from the gas phase to the liquid phase. The results provided information on how gas molecules interacted with the surface of the solution and the specific details of the molecular orientation at the solution surface.

Insights into Aerosol Emission Control in the Postcombustion CO2 Capture Process: From Cluster Formation to Aerosol Growth

Aerosols produced in the amine carbon capture process can lead to secondary environmental pollution. This study employs molecular dynamics (MD) simulations to investigate cluster formation, amine behavior, and aerosol growth of amines, essential for reducing amine aerosol emissions. Results showed that the cluster evolution process can be divided into cluster formation and growth in terms of molecular content, and the nucleation rate for the present systems was estimated in the order of 1028 cm-3 s-1. CO2 absorption was observed alongside successful nucleation, with CO2 predominantly localizing in the cluster's outer layer postabsorption. Monoethanolamine (MEA) exhibited robust electrostatic interactions with other components via hydrogen bonding, leading to its migration toward regions where CO2 and H2O coexisted within the cluster. While MEA presence markedly spurred cluster formation, its concentration had a marginal effect on the final cluster size. Elevating water content can augment the aerosol growth rate. However, altering the gas saturation is possible only within narrow confines by introducing vapor. Contrarily, gas cooling introduced dual, opposing effects on aerosol growth. These findings, including diffusion coefficients and growth rates, enhance theoretical frameworks for predicting aerosol formation in absorbers, aiding in mitigating environmental impacts of amine-based carbon capture.

Homogeneous water vapor condensation with a deep neural network potential model

Molecular-level nucleation has not been clearly understood due to the complexity of multi-body potentials and the stochastic, rare nature of the process. This work utilizes molecular dynamics (MD) simulations, incorporating a first-principles-based deep neural network (DNN) potential model, to investigate homogeneous water vapor condensation. The nucleation rates and critical nucleus sizes predicted by the DNN model are compared against commonly used semi-empirical models, namely extended simple point charge (SPC/E), TIP4P, and OPC, in addition to classical nucleation theory (CNT). The nucleation rates from the DNN model are comparable with those from the OPC model yet surpass the rates from the SPC/E and TIP4P models, a discrepancy that could mainly arise from the overestimated bulk free energy by SPC/E and TIP4P. The surface free energy predicted by CNT is lower than that in MD simulations, while its bulk free energy is higher than that in MD simulations, irrespective of the potential model used. Further analysis of cluster properties with the DNN model unveils pronounced variations of O-H bond length and H-O-H bond angle, along with averaged bond lengths and angles that are enlarged during embryonic cluster formation. Properties such as cluster surface free energy and liquid-to-vapor density transition profiles exhibit significant deviations from CNT assumptions.

Evaluation of Adenanthera pavonina-derived compounds against diabetes mellitus: insight into the phytochemical analysis and in silico assays

Adenanthera pavonina is a medicinal plant with numerous potential secondary metabolites showing a significant level of antidiabetic activity. The objective of the current study was to identify potential phytochemicals from the methanolic leaf extract of Adenanthera pavonina as therapeutic agents against diabetes mellitus using GC-MS and in silico methods. The GC-MS analysis of the leaf extract revealed a total of 17 phytochemicals. Molecular docking was performed using these phytochemicals, targeting the mutated insulin receptor tyrosine kinase (5hhw), which inhibits glucose uptake by cells. Diazoprogesterone (-9.2 kcal/mol), 2,4,4,7a-Tetramethyl-1-(3-oxobutyl)octahydro-1H-indene-2-carboxylic acid (-6.9 kcal/mol), and 2-Naphthalenemethanol, decahydro-.alpha.,.alpha.,4a-trimethyl-8-methylene-, [2R-(2.alpha.,4a.alpha.,8a.beta.)] (-6.6 kcal/mol) exhibited better binding with the target protein. The ADMET analysis was performed for the top three compounds with the best docking scores, which showed positive results with no observed toxicity in the AMES test. Furthermore, the molecular dynamics study confirmed the favorable binding of Diazoprogesterone, 2,4,4,7a-Tetramethyl-1-(3-oxobutyl)octahydro-1H-indene-2-carboxylic acid and 2-Naphthalenemethanol, decahydro-.alpha.,.alpha.,4a-trimethyl-8-methylene-, [2R-(2.alpha.,4a.alpha.,8a.beta.)] with the receptor throughout the 100 ns simulation period.

Unraveling Partial Coalescence Between Droplet and Oil-Water Interface in Water-in-Oil Emulsions under a Direct-Current Electric Field via Molecular Dynamics Simulation

When the electric field strength (E) surpasses a certain threshold, secondary droplets are generated during the coalescence between water droplets in oil and the oil-water interface (so-called the droplet-interface partial coalescence phenomenon), resulting in a lower efficiency of droplet electrocoalescence. This study employs molecular dynamics (MD) simulations to investigate the droplet-interface partial coalescence phenomenon under direct current (DC) electric fields. The results demonstrate that intermolecular interactions, particularly the formation of hydrogen bonds, play a crucial role in dipole-dipole coalescence. Droplet-interface partial coalescence is categorized into five regimes based on droplet morphology. During the contact and fusion of the droplet with the water layer, the dipole moment of the droplet exhibits alternating increases and decreases along the electric field direction. Electric field forces acting on sodium ions and the internal interactions within droplets promote the process of droplet-interface partial coalescence. High field strengths cause significant elongation of the droplet, leading to its fragmentation into multiple segments. The migration of hydrated ions has a dual impact on the droplet-interface partial coalescence, with both facilitative and suppressive effects. The time required for droplet-interface partial coalescence initially decreases and subsequently increases as the field strength increases, depending on the competitive relationship between the extent of droplet stretching and the electric field force. This work provides molecular insights into the droplet-interface coalescence mechanisms in water-in-oil emulsions under DC electric fields.

Liquid-Vapor Coexistence and Spontaneous Evaporation at Atmospheric Pressure of Common Rigid Three-Point Water Models in Molecular Simulations

Molecular dynamics (MD) simulations are widely used to investigate molecular systems at atomic resolution including biomolecular structures, drug-receptor interactions, and novel materials. Frequently, MD simulations are performed in an aqueous solution with explicit models of water molecules. Commonly, such models are parameterized to reproduce the liquid phase of water under ambient conditions. However, often, simulations at significantly higher temperatures are also of interest. Hence, it is important to investigate the equilibrium of the liquid and vapor phases of molecular models of water at elevated temperatures. Here, we evaluate the behavior of 11 common rigid three-point water models over a wide range of temperatures. From liquid-vapor coexistence simulations, we estimated the critical points and studied the spontaneous evaporation of these water models. Moreover, we investigated the influence of the system size, choice of the pressure-coupling algorithm, and rate of heating on the process and compared them with the experimental data. We found that modern rigid three-point water models reproduce the critical point surprisingly well. Furthermore, we discovered that the critical temperature correlates with the quadrupole moment of the respective water model. This indicates that the spatial arrangement of the partial charges is important for reproducing the liquid-vapor phase transition. Our findings may guide the selection of water models for simulations conducted at high temperatures.

Conformational Preferences at the Glycosidic Linkage of Saccharides in Solution as Deduced from NMR Experiments and MD Simulations: Comparison to Crystal Structures

Glycans are central to information content and regulation in biological systems. These carbohydrate molecules are active either as oligo- or polysaccharides, often in the form of glycoconjugates. The monosaccharide entities are joined by glycosidic linkages and stereochemical arrangements are of utmost importance in determining conformation and flexibility of saccharides. The conformational preferences and population distributions at the glycosidic torsion angles φ and ψ have been investigated for O-methyl glycosides of three disaccharides where the substitution takes place at a secondary alcohol, viz., in α-l-Fucp-(1→3)-β-d-Glcp-OMe, α-l-Fucp-(1→3)-α-d-Galp-OMe and α-d-Glcp-(1→4)-α-d-Galp-OMe, corresponding to disaccharide structural elements present in bacterial polysaccharides. Stereochemical differences at or adjacent to the glycosidic linkage were explored by solution state NMR spectroscopy using one-dimensional 1 H,1 H-NOESY NMR experiments to obtain transglycosidic proton-proton distances and one- and two-dimensional heteronuclear NMR experiments to obtain 3 JCH transglycosidic coupling constants related to torsion angles φ and ψ. Computed effective proton-proton distances from molecular dynamics (MD) simulations showed excellent agreement to experimentally derived distances for the α-(1→3)-linked disaccharides and revealed that for the bimodal distribution at the ψ torsion angle for the α-(1→4)-linked disaccharide experiment and simulation were at variance with each other, calling for further force field developments. The MD simulations disclosed a highly intricate inter-residue hydrogen bonding pattern for the α-(1→4)-linked disaccharide, including a nonconventional hydrogen bond between H5' in the glucosyl residue and O3 in the galactosyl residue, supported by a large downfield 1 H NMR chemical shift displacement compared to α-d-Glcp-OMe. Comparison of population distributions of the glycosidic torsion angles φ and ψ in the disaccharide entities to those of corresponding crystal structures highlighted the potential importance of solvation on the preferred conformation.

Nanoinformatics based insights into the interaction of blood plasma proteins with carbon based nanomaterials: Implications for biomedical applications

In the past three decades, interest in using carbon-based nanomaterials (CBNs) in biomedical application has witnessed remarkable growth. Despite the rapid advancement, the translation of laboratory experimentation to clinical applications of nanomaterials is one of the major challenges. This might be attributed to poor understanding of bio-nano interface. Arguably, the most significant barrier is the complexity that arises by interplay of several factors like properties of nanomaterial (shape, size, surface chemistry), its interaction with suspending media (surface hydration and dehydration, surface reconstruction and release of free surface energy) and the interaction with biomolecules (conformational change in biomolecules, interaction with membrane and receptor). Tailoring a nanomaterial that minimally interacts with protein and lipids in the medium while effectively acts on target site in biological milieu has been very difficult. Computational methods and artificial intelligence techniques have displayed potential in effectively addressing this problem. Through predictive modelling and deep learning, computer-based methods have demonstrated the capability to create accurate models of interactions between nanoparticles and cell membranes, as well as the uptake of nanomaterials by cells. Computer-based simulations techniques enable these computational models to forecast how making particular alterations to a material's physical and chemical properties could enhance functional aspects, such as the retention of drugs, the process of cellular uptake and biocompatibility. We review the most recent progress regarding the bio-nano interface studies between the plasma proteins and CBNs with a special focus on computational simulations based on molecular dynamics and density functional theory.

Probing the dynamic landscape of peptides in molecular assemblies by synergized NMR experiments and MD simulations

Peptides or proteins containing small biomolecular aggregates, such as micelles, bicelles, droplets and nanodiscs, are pivotal in many fields ranging from structural biology to pharmaceutics. Monitoring dynamics of such systems has been limited by the lack of experimental methods that could directly detect their fast (picosecond to nanosecond) timescale dynamics. Spin relaxation times from NMR experiments are sensitive to such motions, but their interpretation for biomolecular aggregates is not straightforward. Here we show that the dynamic landscape of peptide-containing molecular assemblies can be determined by a synergistic combination of solution state NMR experiments and molecular dynamics (MD) simulations. Solution state NMR experiments are straightforward to implement without an excessive amount of sample, while direct combination of spin relaxation data to MD simulations enables interpretation of dynamic landscapes of peptides and other aggregated molecules. To demonstrate this, we interpret NMR data from transmembrane, peripheral, and tail anchored peptides embedded in micelles. Our results indicate that peptides and detergent molecules do not rotate together as a rigid body, but peptides rotate in a viscous medium composed of detergent micelle. Spin relaxation times also provide indirect information on peptide conformational ensembles. This work gives new perspectives on peptide dynamics in complex biomolecular assemblies.

Overlay databank unlocks data-driven analyses of biomolecules for all

Tools based on artificial intelligence (AI) are currently revolutionising many fields, yet their applications are often limited by the lack of suitable training data in programmatically accessible format. Here we propose an effective solution to make data scattered in various locations and formats accessible for data-driven and machine learning applications using the overlay databank format. To demonstrate the practical relevance of such approach, we present the NMRlipids Databank-a community-driven, open-for-all database featuring programmatic access to quality-evaluated atom-resolution molecular dynamics simulations of cellular membranes. Cellular membrane lipid composition is implicated in diseases and controls major biological functions, but membranes are difficult to study experimentally due to their intrinsic disorder and complex phase behaviour. While MD simulations have been useful in understanding membrane systems, they require significant computational resources and often suffer from inaccuracies in model parameters. Here, we demonstrate how programmable interface for flexible implementation of data-driven and machine learning applications, and rapid access to simulation data through a graphical user interface, unlock possibilities beyond current MD simulation and experimental studies to understand cellular membranes. The proposed overlay databank concept can be further applied to other biomolecules, as well as in other fields where similar barriers hinder the AI revolution.

Water molecules mute the dependence of the double-layer potential profile on ionic strength

We present the results of molecular dynamics simulations of a nanoscale electrochemical cell. The simulations include an aqueous electrolyte solution with varying ionic strength (i.e., concentrations ranging from 0-4 M) between a pair of metallic electrodes held at constant potential difference. We analyze these simulations by computing the electrostatic potential profile of the electric double-layer region and find it to be nearly independent of ionic concentration, in stark contrast to the predictions of standard continuum-based theories. We attribute this lack of concentration dependence to the molecular influences of water molecules at the electrode-solution interface. These influences include the molecular manifestation of water's dielectric response, which tends to drown out the comparatively weak screening requirement of the ions. To support our analysis, we decompose water's interfacial response into three primary contributions: molecular layering, intrinsic (zero-field) orientational polarization, and the dipolar dielectric response.

Core-ionization spectrum of liquid water

We present state-of-the-art calculations of the core-ionization spectrum of water. Despite significant progress in procedures developed to mitigate various experimental complications and uncertainties, the experimental determination of ionization energies of solvated species involves several non-trivial steps such as assessing the effect of the surface potential, electrolytes, and finite escape depths of photoelectrons. This provides a motivation to obtain robust theoretical values of the intrinsic bulk ionization energy and the corresponding solvent-induced shift. Here we develop theoretical protocols based on coupled-cluster theory and electrostatic embedding. Our value of the intrinsic solvent-induced shift of the 1sO ionization energy of water is -1.79 eV. The computed absolute position and the width of the 1sO peak in photoelectron spectrum of water are 538.47 eV and 1.44 eV, respectively, agreeing well with the best experimental values.

Developing an Implicit Solvation Machine Learning Model for Molecular Simulations of Ionic Media

Molecular dynamics (MD) simulations of biophysical systems require accurate modeling of their native environment, i.e., aqueous ionic solution, as it critically impacts the structure and function of biomolecules. On the other hand, the models should be computationally efficient to enable simulations of large spatiotemporal scales. Here, we present the deep implicit solvation model for sodium chloride solutions that satisfies both requirements. Owing to the use of the neural network potential, the model can capture the many-body potential of mean force, while the implicit water treatment renders the model inexpensive. We demonstrate our approach first for pure ionic solutions with concentrations ranging from physiological to 2 M. We then extend the model to capture the effective ion interactions in the vicinity and far away from a DNA molecule. In both cases, the structural properties are in good agreement with all-atom MD, showcasing a general methodology for the efficient and accurate modeling of ionic media.

Anisotropic Interfacial Force Field for Interfaces of Water with Hexagonal Boron Nitride

This study introduces an anisotropic interfacial potential that provides an accurate description of the van der Waals (vdW) interactions between water and hexagonal boron nitride (h-BN) at their interface. Benchmarked against the strongly constrained and appropriately normed functional, the developed force field demonstrates remarkable consistency with reference data sets, including binding energy curves and sliding potential energy surfaces for various configurations involving a water molecule adsorbed atop the h-BN surface. These findings highlight the significant improvement achieved by the developed force field in empirically describing the anisotropic vdW interactions of the water/h-BN heterointerfaces. Utilizing this anisotropic force field, molecular dynamics simulations demonstrate that atomically flat, pristine h-BN exhibits inherent hydrophobicity. However, when atomic-step surface roughness is introduced, the wettability of h-BN undergoes a significant change, leading to a hydrophilic nature. The calculated water contact angle (WCA) for the roughened h-BN surface is approximately 64°, which closely aligns with experimental WCA values ranging from 52° to 67°. These findings indicate the high probability of the presence of atomic steps on the surfaces of the experimental h-BN samples, emphasizing the need for further experimental verification. The development of the anisotropic interfacial force field for accurately describing interactions at the water/h-BN heterointerfaces is a significant advancement in accurately simulating the wettability of two-dimensional (2D) materials, offering a reliable tool for studying the dynamic and transport properties of water at these interfaces, with implications for materials science and nanotechnology.

Lessons Learned from Multiobjective Automatic Optimizations of Classical Three-Site Rigid Water Models Using Microscopic and Macroscopic Target Experimental Observables

The development of accurate water models is of primary importance for molecular simulations. Despite their intrinsic approximations, three-site rigid water models are still ubiquitously used to simulate a variety of molecular systems. Automatic optimization approaches have been recently used to iteratively refine three-site water models to fit macroscopic (average) thermodynamic properties, providing state-of-the-art three-site models that still present some deviations from the liquid water properties. Here, we show the results obtained by automatically optimizing three-site rigid water models to fit a combination of microscopic and macroscopic experimental observables. We use Swarm-CG, a multiobjective particle-swarm-optimization algorithm, for training the models to reproduce the experimental radial distribution functions of liquid water at various temperatures (rich in microscopic-level information on, e.g., the local orientation and interactions of the water molecules). We systematically analyze the agreement of these models with experimental observables and the effect of adding macroscopic information to the training set. Our results demonstrate how adding microscopic-rich information in the training of water models allows one to achieve state-of-the-art accuracy in an efficient way. Limitations in the approach and in the approximated description of water in these three-site models are also discussed, providing a demonstrative case useful for the optimization of approximated molecular models, in general.