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The environmental and ecological concerns drive researchers to synthesize functional materials using components from natural resources. Nanocellulose (NC), derived from plants, marine animals, or microorganisms, is a green material attracting attention due to its abundance, biocompatibility, and biodegradability. NC’s interstice properties enable the synthesis of functional nanocomposites in forms like aerogels, foams, paper, sheets, or hollow filaments. This review briefly describes NC classification and production while comprehensively presenting its mechanical, rheological, optical, and electrical properties, offering foundational knowledge for future research. Additionally, it highlights recent developments in NC-based products across fields such as papermaking, water treatment, civil engineering, electronics, cosmetics, food, and medicine. For the first time, this paper explores recent advances in NC molecular simulation, providing insights into structure, arrangement, and interactions through molecular dynamic simulation. Finally, future prospects for NC-based applications are discussed to encourage studies addressing current challenges. [ABSTRACT FROM AUTHOR]

How to increase the stereocomplex crystal (SCs) content of polylactic acid attracts a lot of attention from scientists. In the current work, Monte Carlo simulations are used to construct grafted diblock copolymer systems with different grafting modes and the stereocomplex crystallization of these systems is studied. The results show that the SC contents are highest in the random‐grafted, the row‐staggered‐grafted and the point‐staggered‐grafted copolymer systems, while the SC content is lowest in the uniform‐grafted copolymer systems. This can be attributed to that the random‐grafted, the row‐staggered‐grafted and the point‐staggered‐grafted copolymer systems have the highest local segment miscibility, while the uniform‐grafted copolymer system has the lowest local segment miscibility. In addition, it is also found that the point‐staggered‐grafted copolymer systems with lower chain lengths exhibit higher SC contents due to the stronger segment mobility. [ABSTRACT FROM AUTHOR]

The impact of N,1-dibenzyl-5-methyl-1H-pyrazol-3-carboxamide (BPC) on the carbon steel (CS) corrosion in hydrochloric acid (1 M) was studied in this work, considering concentration and temperature effects. Electrochemical investigation indicated that BPC functions as a mixed-type inhibitor. For an optimal BPC concentration of 125 ppm, the inhibition efficiency of 91.55% was obtained at 298 K. According to adsorption isotherm of Langmuir, the BPC adheres to the CS with standard adsorption free energy (ΔG°ads) of − 26.76 kJ mol−1. Furthermore, the calculation of dissolution activation parameters revealed an increase in energy (Ea) from 46.48 to 94.97 kJ mol−1, an elevation in the enthalpy (∆Ha) from 43.89 to 92.37 kJ mol−1, and a rise in the entropy (∆Sa) from − 91.17 to 51.43 J mol−1 K−1 in the presence of 125 ppm of BPC. The experimental results were confirmed by quantum chemistry calculations based on density functional theory (DFT) and molecular simulations using the Monte Carlo method. These theoretical approaches also allowed for a comparison of the inhibitory performances of BPC with its protonated form, BPCH, the latter being found more effective. Moreover, the study of the radial distribution function g(r) predicted that the nature of the bond formed with the steel surface is of a chemical type. [ABSTRACT FROM AUTHOR]

This study explores the interaction between Vemurafenib (VEM), a potent BRAF inhibitor, and calf thymus DNA (ctDNA) using a comprehensive array of biophysical and computational techniques. The primary objective is to understand the potential off-target effects of VEM on DNA, given its established role in melanoma therapy targeting the BRAF V600E mutation. The investigation employed methods such as ultraviolet–visible absorption spectroscopy, steady-state fluorescence, circular dichroism, isothermal titration calorimetry, and advanced molecular dynamics simulations. The results indicate that VEM interacts with DNA primarily through a minor groove-binding mechanism, causing minimal structural disruption to the DNA double helix. Viscosity measurements and melting temperature analyses further confirmed this non-intercalative mode of binding. Calorimetry data revealed an exothermic, thermodynamically favorable interaction between VEM and ctDNA, driven by both enthalpic and entropic factors. Finally, computer simulations identified the most probable binding site and mode of VEM within the minor groove of the nucleic acid, providing a molecular basis for the experimental findings. [ABSTRACT FROM AUTHOR]

Tuberculosis is one of the most common causes of death worldwide, with a rapid emergence of multi-drug-resistant strains underscoring the need for new antituberculosis drugs. Recent studies indicate that lansoprazole--a known gastric proton pump inhibitor and its intracellular metabolite, lansoprazole sulfide (LPZS)--are potential antituberculosis compounds. Yet, their inhibitory mechanism and site of action still remain unknown. Here, we combine biochemical, computational, and structural approaches to probe the interaction of LPZS with the respiratory chain supercomplex III2IV2 of Mycobacterium smegmatis, a close homolog of Mycobacterium tuberculosis supercomplex. We show that LPZS binds to the Qo cavity of the mycobacterial supercomplex, inhibiting the quinol substrate oxidation process and the activity of the enzyme. We solve high-resolution (2.6 Å) cryo-electron microscopy (cryo-EM) structures of the supercomplex with bound LPZS that together with microsecond molecular dynamics simulations, directed mutagenesis, and functional assays reveal key interactions that stabilize the inhibitor, but also how mutations can lead to the emergence of drug resistance. Our combined findings reveal an inhibitory mechanism of LPZS and provide a structural basis for drug development against tuberculosis. [ABSTRACT FROM AUTHOR]

With the rapid advance of high‐performance computing and electronic technologies, understanding thermal conductivity in materials has become increasingly important. This study presents a novel method: the site‐projected thermal conductivity that quantitatively estimates the local (atomic) contribution to heat transport, leveraging the Green–Kubo thermal transport equations. The effectiveness of this approach on disordered and amorphous graphene, amorphous silicon, and grain boundaries in silicon–germanium alloys is demonstrated. Amorphous graphene reveals a percolation behavior for thermal transport. The results highlight the potential of the method to provide new insights into the thermal behavior of materials, offering a promising avenue for materials design and performance optimization. [ABSTRACT FROM AUTHOR]

Molecular dynamics (MD) simulations are employed to assess the effects of diverse carbon nanomaterials on the thermo‐oxidative aging properties and tribological behavior of perfluoroelastomer (FFKM) in high‐temperature environments. In this study, carbon nanofillers such as graphene nanosheets (GNS), carbon nanotubes (CNTs), hydroxyl‐functionalized graphene (OH‐GNS), and hydroxyl‐functionalized carbon nanotubes (OH‐CNTs) are examined. The aging properties of composite systems are characterized by parameters like cohesive energy density and mean square displacement. The constant strain method is utilized to estimate the shear modulus and bulk modulus. Three‐layer friction structures are established to analyze the mechanism of fillers on the tribological behavior of composites by applying shear loads. According to the MD simulation results, the addition of carbon nanofillers enhances FFKM's thermo‐oxidative aging performance at 533 K, increases its bulk and shear moduli, and reduces the coefficient of friction and abrasion rate of each composite at high temperatures. Among the four nanofillers, OH‐CNTs is the most effective in terms of improving FFKM performance. Stronger dipoledipole interactions and hydrogen bonding are introduced into the system by OH‐CNTs, which improves the stability of the filler‐matrix interface and produces stronger interfacial interactions. This work offers theoretical predictions for the design and optimization of carbon nanomaterial and FFKM polymer composites. [ABSTRACT FROM AUTHOR]

Background: Lumpy skin disease (LSD) is a transboundary virus disease that mostly affects cattle. It has recently been reported all over the world, which highlights the need for efficient control methods. LSD poses serious economic dangers worldwide. Aim: The aim of this study was to screen novel antiviral compounds for the control of LSD. Methods: By using in silico approach, ADMET, docking, and molecular simulations, this work was designed to investigate 13 active compounds for antiviral effects against Lumpy skin disease virus (LSDV). Results: ADMET study of the selected 13 compounds revealed that Apigenin-4'-glucoside and Vidarabine did not show any critical hazards. The docking study identified potential antiviral compounds against LSDV, with Apigenin-4'-glucoside (ΔG = -6.6 ± 1.1) and Vidarabine (ΔG = -5.53 ± 0.73) showing promising interactions with key viral proteins. Molecular dynamics simulations confirmed the stability and robustness of these interactions, suggesting their potential as effective antiviral agents. Conclusion: Molecular analyses verify the strong antiviral activity of apigenin-4'-glucoside against LSDV among the selected compounds. This work sheds light on the way to explore potent anti-LSDV molecule. Moreover, the outcome of the study should screen after more extensive clinical studies. [ABSTRACT FROM AUTHOR]

Between the mid-1970s and mid-1980s, molecular simulations emerged as a transformative force within materials science. Sidney Yip's early contributions at the Massachusetts Institute of Technology, alongside his involvement in the 1985 International School of Physics "Enrico Fermi" in Varenna, Italy, catalyzed the convergence of traditional methods with computational techniques and helped drive a redefinition of the discipline's epistemic and methodological boundaries. This article argues that Yip's biography and professional trajectory as a Chinese-born engineer and scientist in the United States during the Cold War facilitated the acceptance and advancement of molecular simulations within materials research. His work also attracted the interest of leaders from established fields, such as condensed matter physics and chemical physics, to explore the potential applications of these techniques in materials science. In examining his journey, this study illuminates the dual processes of cultural assimilation and hybridity, and highlights Yip's boundary work that promoted the integration of diverse epistemic traditions and heterogeneous communities. The analysis traces the epistemological transformations, methodological shifts, and the institutional and disciplinary dynamics that fostered the incorporation of molecular simulations into materials science. This examination foregrounds the co-construction of scientific knowledge and technological practice through Yip's boundary work, and offers an assessment of his contributions within the broader sociotechnical networks that shaped the field. Recognizing the paucity of existing historiography on the subject, this article aims to establish a framework based on primary sources that can serve as a foundation for future scholarly inquiry. [ABSTRACT FROM AUTHOR]

While transcription factors have been generally perceived as "undruggable," an exception is the HIF‐2 hypoxia‐inducible transcription factor, which contains an internal cavity that is sufficiently large to accommodate a range of small‐molecules, including the therapeutically used inhibitor belzutifan. Given the relatively long ligand residence times of these small molecules and the lack of any experimentally observed pathway connecting the cavity to solvent, there has been great interest in understanding how these drug ligands exit the buried receptor cavity. Here, we focus on the relevant PAS‐B domain of hypoxia‐inducible factor 2α (HIF‐2α) and examine how one such small molecule (THS‐017) exits from the buried cavity within this domain on the seconds‐timescale using atomistic simulations and ZZ‐exchange NMR. To enable the simulations, we applied the weighted ensemble path sampling strategy, which generates continuous pathways for a rare‐event process [e.g., ligand (un)binding] with rigorous kinetics in orders of magnitude less computing time compared to conventional simulations. Results reveal the formation of an encounter complex intermediate and two distinct classes of pathways for ligand exit. Based on these pathways, we identified two pairs of conformational gating residues in the receptor: one for the major class (N288 and S304) and another for the minor class (L272 and M309). ZZ‐exchange NMR validated the kinetic importance of N288 for ligand unbinding. Our results provide an ideal simulation dataset for rational manipulation of ligand unbinding kinetics. [ABSTRACT FROM AUTHOR]

The 4,6-substituted-1,3,5-triazin-2(1H)-ones are promising inhibitors of human DNA topoisomerase IIα. To further develop this chemical class targeting the enzyme´s ATP binding site, the triazin-2(1H)-one substitution position 6 was optimized. Inspired by binding of preclinical substituted 9H-purine derivative, bicyclic substituents were incorporated at position 6 and the utility of this modification was validated by a combination of molecular simulations, dynamic pharmacophores, and free energy calculations. Considering also predictions of Deepfrag, a software developed for structure-based lead optimization based on deep learning, compounds with both bicyclic and monocyclic substitutions were synthesized and investigated for their inhibitory activity. The SAR data showed that the bicyclic substituted compounds exhibited good inhibition of topo IIα, comparable to their mono-substituted counterparts. Further evaluation on a panel of human protein kinases showed selectivity for the inhibition of topo IIα. Mechanistic studies indicated that the compounds acted predominantly as catalytic inhibitors, with some exhibiting topo IIα poison effects at higher concentrations. Integration of STD NMR experiments and molecular simulations, provided insights into the binding model and highlighted the importance of the Asn120 interaction and hydrophobic interactions with substituents at positions 4 and 6. In addition, NCI-60 screening demonstrated cytotoxicity of the compounds with bicyclic substituents and identified sensitive human cancer cell lines, underlining the translational relevance of our findings for further preclinical development of this class of compounds. The study highlights the synergy between simulation and AI-based approaches in efficiently guiding molecular design for drug optimization, which has implications for further preclinical development of this class of compounds.

Background: Lumpy skin disease (LSD) is a transboundary virus disease that mostly affects cattle. It has recently reported all over the world, which highlights the need for efficient control methods. LSD poses serious economic dangers worldwide. Aim: The aim of this study was to screen novel antiviral compounds for the control of LSD. Methods: By using in silico approach, ADMET, docking, and molecular simulations, this work was designed to investigate 13 active compounds for antiviral effects against LSDV. Results: ADMET study of the selected 13 compounds revealed that Apigenin-4'-glucoside and Vidarabine did not show any critical hazards. The docking study identified potential antiviral compounds against LSDV, with Apigenin-4'-glucoside (ΔG = -6.6 ± 1.1) and Vidarabine (ΔG = -5.53 ± 0.73) showing promising interactions with key viral proteins. Molecular dynamics simulations confirmed the stability and robustness of these interactions, suggesting their potential as effective antiviral agents. Conclusion: Molecular analyses verify the strong antiviral activity of apigenin-4'-glucoside against LSDV among the selected compounds. This work sheds light on the way to explore potent anti-LSDV molecule. Moreover, the outcome of the study should screen after more extensive clinical studies. [Open Vet J 2024; 14(11.000): 2806-2816]

The development of drug resistance in cancer cells poses a significant challenge for treatment, with nearly 90% of cancer-related deaths attributed to it. Over 50% of ovarian cancer patients and 30–40% of breast cancer patients exhibit resistance to therapies such as Taxol. Previous literature has shown that cytotoxic cancer therapies and ionizing radiation damage tumors, prompting cancer cells to exploit the autotaxin (ATX)–lysophosphatidic acid (LPA)–lysophosphatidic acid receptor (LPAR) signaling axis to enhance survival pathways, thus reducing treatment efficacy. Therefore, targeting this signaling axis has become a crucial strategy to overcome some forms of cancer resistance. Addressing this challenge, we identified and assessed ATX-1d, a novel compound targeting ATX, through computational methods and in vitro assays. ATX-1d exhibited an IC50 of 1.8 ± 0.3 μM for ATX inhibition and demonstrated a significant binding affinity for ATX, as confirmed by MM-GBSA, QM/MM-GBSA, and SAPT in silico methods. ATX-1d significantly amplified the potency of paclitaxel, increasing its effectiveness tenfold in 4T1 murine breast carcinoma cells and fourfold in A375 human melanoma cells without inducing cytotoxic effects as a single agent. [ABSTRACT FROM AUTHOR]

Understanding the intracellular dynamics of brain cells entails performing three-dimensional molecular simulations incorporating ultrastructural models that can capture cellular membrane geometries at nanometer scales. While there is an abundance of neuronal morphologies available online, e.g. from NeuroMorpho.Org , converting those fairly abstract point-and-diameter representations into geometrically realistic and simulation-ready, i.e. watertight, manifolds is challenging. Many neuronal mesh reconstruction methods have been proposed; however, their resulting meshes are either biologically unplausible or non-watertight. We present an effective and unconditionally robust method capable of generating geometrically realistic and watertight surface manifolds of spiny cortical neurons from their morphological descriptions. The robustness of our method is assessed based on a mixed dataset of cortical neurons with a wide variety of morphological classes. The implementation is seamlessly extended and applied to synthetic astrocytic morphologies that are also plausibly biological in detail. Resulting meshes are ultimately used to create volumetric meshes with tetrahedral domains to perform scalable in silico reaction-diffusion simulations for revealing cellular structure–function relationships. Availability and implementation: Our method is implemented in NeuroMorphoVis , a neuroscience-specific open source Blender add-on, making it freely accessible for neuroscience researchers. [ABSTRACT FROM AUTHOR]

In various industrial applications focusing on environmental sustainability, the purification of H2 from CO2-containing streams is crucial. Membrane-based separation processes are preferred over other gas separation techniques due to their superior efficiency and selectivity and lower energy consumption. Membranes composed of inorganic nanoporous materials, known for their uniform pore size distribution and high permeability, exhibit remarkable gas separation performance. Recently, two-dimensional (2D) nanomaterials with high surface area and tunable functional groups have gained attention for membrane-based gas separation applications, owing to their thermal and mechanical durability. Therefore, we carried out a simulation study at the nanoscale level encompassing multiple analytical viewpoints of 2D MXene membranes: (i) A collection of 730 MXene structures was evaluated for single gas H2/CO2 separation, and 700 of these surpassed the Robeson upper bound, indicating their significant potential for replacing conventional polymeric membranes. Moreover, VCrNF2, MoWCF2, Y2NO2, and Sc2NO2 nanomaterials were listed as potential membranes according to the predefined ranking criteria. (ii) The performance of mixed matrix membranes (MMMs) made of five different polymers and all MXene nanomaterials was calculated with Maxwell's model. (iii) The effect of interlayer distance of MXene nanosheets on H2/CO2 separation was examined over the top four MXene nanomaterials and experimentally highly studied Ti2CO2 MXene. The optimum interlayer distance was defined as 5.5 Å for effective separation. (iv) Finally, Y2NO2 and Ti2CO2 MXene membranes were compared in terms of concentration polarization. Y2NO2 is more susceptible to CO2-related concentration polarization, which hinders CO2 transport and improves H2/CO2 separation in single gas measurements. By examining MXene membranes for H2/CO2 separation in multiple aspects, we aimed to demonstrate their potential and further guide the experimental studies. [ABSTRACT FROM AUTHOR]

The perception of time undergoes a radical shift between the human scale and the nanoscale. In an age of rapidly evolving media and scientific technologies, we need to understand how these impact human perception and visual culture. This essay explores computational molecular simulations through the lenses of temporal media theory and moving image practice. Emerging from a creative fellowship with a physical chemistry research group, I focus on two moving image works that depict crystalline structures. One is a nanoscale computational simulation of soot formation and the other is a durational video artwork showing the dissolution of sugar. Computational molecular simulations are shown to produce a feeling of time by smearing an extremely short duration across a longer perceptible duration. This analysis uncovers how the awareness of media as a construct troubles our chronoception (perception of time), while unexpectedly, the screen becomes complicit in scientists' expert temporal understanding. The videos present vastly different spatial and temporal scales and have different chronoceptive effects: one gives a sense of being within time, the other across time. Ultimately, computational simulations emerge as isomorphic media that have explicit aesthetic properties that connect us to the implicit, abstract energetics of chemical reactivity. [ABSTRACT FROM AUTHOR]

Hemorphins are short atypical opioid peptide fragments embedded in the β-chain of hemoglobin. They have received considerable attention recently due to their interaction with opioid receptors. The affinity of hemorphins to opioid receptors μ-opioid receptor (MOR), δ-opioid receptor (DOR), and κ-opioid receptor (KOR) has been well established. However, the underlying binding mode and molecular interactions of hemorphins in opioid receptors remain largely unknown. Here, we report the pattern of interaction of camel and other mammalian hemorphins with DOR. Extensive in silico docking and molecular dynamics simulations were employed to identify intermolecular interactions and binding energies were calculated to determine the affinity of these peptides for DOR. Longer forms of hemorphins - hemorphin-7, hemorphin-6, camel hemorphin-7, and camel hemorphin-6 had strong interactions with DOR. However, camel hemorphin-7 and camel hemorphin-6 had high binding affinity towards DOR. Thus, the findings of this study provide molecular insights into how hemorphins, particularly camel hemorphin variants, could be a therapeutic agent for pain regulation, stress management, and analgesia.

Abstract Cardiovascular diseases, including heart failure, stroke, and hypertension, affect 608 million people worldwide and cause 32% of deaths. Combination therapy is required in 60% of patients, involving concurrent Renin–Angiotensin–Aldosterone-System (RAAS) and Neprilysin inhibition. This study introduces a novel multi-target in-silico modeling technique (mt-QSAR) to evaluate the inhibitory potential against Neprilysin and Angiotensin-converting enzymes. Using both linear (GA-LDA) and non-linear (RF) algorithms, mt-QSAR classification models were developed using 983 chemicals to predict inhibitory effects on Neprilysin and Angiotensin-converting enzymes. The Box-Jenkins method, feature selection method, and machine learning algorithms were employed to obtain the most predictive model with ~ 90% overall accuracy. Additionally, the study employed virtual screening of designed scaffolds (Chalcone and its analogues, 1,3-Thiazole, 1,3,4-Thiadiazole) applying developed mt-QSAR models and molecular docking. The identified virtual hits underwent successive filtration steps, incorporating assessments of drug-likeness, ADMET profiles, and synthetic accessibility tools. Finally, Molecular dynamic simulations were then used to identify and rank the most favourable compounds. The data acquired from this study may provide crucial direction for the identification of new multi-targeted cardiovascular inhibitors.

The receptor binding domain (RBD) of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) spike protein must undergo a crucial conformational transition to invade human cells. It is intriguing that this transition is accompanied by a synchronized movement of the entire spike protein. Therefore, it is possible to design allosteric regulators targeting non‐RBD but hindering the conformational transition of RBD. To understand the allosteric mechanism in detail, we establish a computational framework by integrating coarse‐grained molecular dynamic simulations and a state‐of‐the‐art neural network model called neural relational inference. Leveraging this framework, we have elucidated the allosteric pathway of the SARS‐CoV‐2 spike protein at the residue level and identified the molecular mechanisms involved in the transmission of allosteric signals. The movement of D614 is coupled with that of Q321. This interaction subsequently influences the movement of K528/K529, ultimately coupling with the movement of RBD during conformational changes. Mutations that weaken the interactions within this pathway naturally block the allosteric signal transmission, thereby modulating the conformational transitions. This observation also offers a rationale for the distinct allosteric patterns observed in the SARS‐CoV spike protein. Our result provides a useful method for analyzing the dynamics of potential viral variants in the future. [ABSTRACT FROM AUTHOR]

The substrate‐binding domain 2 (SBD2) is an important part of the bacterial glutamine (GLN) transporter and mediates binding and delivery of GLN to the transporter translocation subunit. The SBD2 consists of two domains, D1 and D2, that bind GLN in the space between domains in a closed structure. In the absence of ligand, the SBD2 adopts an open conformation with larger space between domains. The GLN binding and closing are essential for the subsequent transport into the cell. Arginine (ARG) can also bind to SBD2 but does not induce closing and inhibits GLN transport. We use atomistic molecular dynamics (MD) simulations in explicit solvent to study ARG binding in the presence of the open SBD2 structure and observed reversible binding to the native GLN binding site with similar contacts but no transition to a closed SBD2 state. Absolute binding free energy simulations predict a considerable binding affinity of ARG and GLN to the binding site on the D1 domain. Free energy simulations to induce subsequent closing revealed a strong free energy penalty in case of ARG binding in contrast to GLN binding that favors the closed SBD2 state but still retains a free energy barrier for closing. The simulations allowed the identification of the molecular origin of the closing penalty in case of bound ARG and suggested a mutation of lysine at position 373 to alanine that strongly reduced the penalty and allowed closing even in the presence of bound ARG. The study offers an explanation of the molecular mechanism of how ARG competitively inhibits GLN from binding to SBD2 and from triggering the transition to a closed conformation. The proposed Lys373Ala mutation shows promise as a potential tool to validate whether a conformational mismatch between open SBD2 and the translocator is responsible for preventing ARG uptake to the cell. [ABSTRACT FROM AUTHOR]

The elastic response of liquid crystals (LCs) to external perturbations is of eminent importance for both their theoretical understanding and practical application. While the elasticity of thermotropic nematic LCs has been widely studied, far less is known about the elastic properties of lyotropic nematic LCs. In recent years however, several observations of spontaneous mirror symmetry breaking in lyotropic LCs (LLCs) confined in curved geometries stimulated increasing scientific interest in the elastic behaviour of lyotropic nematics. In this review article, we have now compiled the known experimental data on the elastic constants of different classes of nematic LLCs, ranging from micellar via chromonic and polymeric to particle-based LLCs. The experimental findings are compared with simulation results on various model systems and discussed in the light of current theoretical concepts. As a result of these considerations, it is shown that the elastic properties of nematic LLCs can indeed be very different and strongly depending on the nature of their particular nanoscopic building blocks (such as micelles, molecular stacks, semiflexible polymer chains, solid nanoparticles, biofilaments), namely their size, aspect ratio and flexibility, as well as their specific interactions. Nevertheless, in all cases the twist constant $ K_{22} $ K 22 is by far the smallest of the three elastic constants of a certain system and an order of magnitude smaller than typical values found in thermotropic nematics. It also appears that the splay constant $ K_{11} $ K 11 is mainly determined by the aspect ratio of the particular LLC building blocks and the bend constant $ K_{33} $ K 33 by their flexibility. The one-constant approximation, often used for thermotropic nematics, clearly fails in the case of LLCs. Finally, our observations also make obvious that the current knowledge about the elastic properties of LLCs is still incomplete and thus improvements are necessary, both in terms of experimental investigations and theoretical studies. Abbreviations: 5CB: 4-Cyano-4'-pentylBiphenyl; BLG: β-LactoGlobulin; CCNC: Carboxylated Cellulose NanoCrystal; CDEAB: N; N-Dimethyl-N-Ethylhexadecyl-Ammonium Bromide; CNC: Cellulose NanoCrystal; COM: Centre Of Mass; CsPFO: Caesium PerFluoroOctanoate; DACl: DecylAmmonium Chloride; DCF: Direct Correlation Function; DFT: Density Functional Theory; DLS: Dynamic Light Scattering; DOH: 1-Decanol; DSCG: DiSodium CromoGlycate; GB: Gay–Berne; HSC: Hard SpheroCylinder; LLC: Lyotropic Liquid Crystal; LCLC: Lyotropic Chromonic Liquid Crystal; MC: Monte Carlo; MD: Molecular Dynamics; NC: Nematic phase of rod-like (Calamitic) building blocks; ND: Nematic phase of Disc-like building blocks; ODF: Orientational Distribution Function; PBG: Poly-γ-Benzyl-Glutamate; SDS: Sodium Dodecyl Sulfate; SCNC: Sulfated Cellulose NanoCrystal; SSY: SunSet Yellow; TMV: Tobacco Mosaic Virus [ABSTRACT FROM AUTHOR]

Thermal and themo-mechanical properties of alkali-silica reaction (ASR) products are poorly studied. The existent property data refers to theoretical considerations and do not account for the fact that ASR products can be crystalline, nanocrystalline and potentially amorphous. Here, the thermal conductivity, heat capacity, and coefficient of thermal expansion of crystalline structures (based on Na- and K-shlykovite), nanocrystalline structure (based on defective K-shlykovite structures), and amorphous ASR product are calculated using molecular simulations. Semi-classical estimates of the thermal conductivity, heat capacity, and standard molar entropy are provided. The anisotropy of thermal conductivity and thermal expansion is quantified. Nanoacoustics parameters (sound velocities, phonon free path and relaxation time) are calculated. These results contribute to completing property data for ASR products. [ABSTRACT FROM AUTHOR]

Tetramethrin (TMT) is a commonly used insecticide and has a carcinogenic and neurodegenerative effect on humans. The binding mechanism and toxicological implications of TMT to human serum albumin (HSA) were examined in this study employing a combination of biophysical and computational methods indicating moderate binding affinity and potential hepato and renal toxicity. Fluorescence quenching experiments showed that TMT binds to HSA with a moderate affinity, and the binding process was spontaneous and predominantly enthalpy‐driven. Circular dichroism spectroscopy revealed that TMT binding did not induce any significant conformational changes in HSA, resulting in no changes in its alpha‐helix content. The binding site and modalities of TMT interactions with HSA as computed by molecular docking and molecular dynamics simulations revealed that it binds to Sudlow site II of HSA via hydrophobic interactions through its dimethylcyclopropane carboxylate methyl propanyl group. The structural dynamics of TMT induce proper fit into the binding site creating increased and stabilizing interactions. Additionally, molecular mechanics–Poisson Boltzmann surface area calculations also indicated that non‐polar and van der Waals were found to be the major contributors to the high binding free energy of the complex. Quantum mechanics (QM) revealed the conformational energies of the binding confirmation and the degree of deviation from the global minimum energy conformation of TMT. The results of this study provide a comprehensive understanding of the binding mechanism of TMT with HSA, which is important for evaluating the toxicity of this insecticide in humans. [ABSTRACT FROM AUTHOR]

Evidence that the electronic structure of atoms persists in molecules to a much greater extent than has been usually admitted is presented. This is achieved by resorting to N-electron real-space descriptors instead of one- or at most two-particle projections like the electron or exchange-correlation densities. Here, the 3N-dimensional maxima of the square of the wavefunction, the so-called Born maxima, are used. Since this technique is relatively unknown to the crystallographic community, a case-based approach is taken, revisiting first the Born maxima of atoms in their ground state and then some of their excited states. It is shown how they survive in molecules and that, beyond any doubt, the distribution of electrons around an atom in a molecule can be recognized as that of its isolated, in many cases excited, counterpart, relating this fact with the concept of energetic promotion. Several other cases that exemplify the applicability of the technique to solve chemical bonding conflicts and to introduce predictability in real-space analyses are also examined.

The fat mass and obesity-associated (FTO) protein catalyzes metal-dependent modifications of nucleic acids, namely the demethylation of methyl adenosine inside mRNA molecules. The FTO protein has been identified as a potential target for developing anticancer therapies. Identifying a suitable ligand-targeting FTO protein is crucial to developing chemotherapeutic medicines to combat obesity and cancer. Scientists worldwide have employed many methodologies to discover a potent inhibitor for the FTO protein. This study uses deep learning-based methods and molecular docking techniques to investigate the FTO protein as a target. Our strategy involves systematically screening a database of small chemical compounds. By utilizing the crystal structures of the FTO complexed with ligands, we successfully identified three small-molecule chemical compounds (ZINC000003643476, ZINC000000517415, and ZINC000001562130) as inhibitors of the FTO protein. The identification process was accomplished by employing a combination of screening techniques, specifically deep learning (DeepBindGCN) and Autodock vina, on the ZINC database. These compounds were subjected to comprehensive analysis using 100 nanoseconds of molecular dynamics and binding free energy calculations. The findings of our study indicate the identification of three candidate inhibitors that might effectively target the human fat mass and obesity protein. The results of this study have the potential to facilitate the exploration of other chemicals that can interact with FTO. Conducting biochemical studies to evaluate these compounds’ effectiveness may contribute to improving fat mass and obesity treatment strategies.

The fat mass and obesity-associated (FTO) protein catalyzes metal-dependent modifications of nucleic acids, namely the demethylation of methyl adenosine inside mRNA molecules. The FTO protein has been identified as a potential target for developing anticancer therapies. Identifying a suitable ligand-targeting FTO protein is crucial to developing chemotherapeutic medicines to combat obesity and cancer. Scientists worldwide have employed many methodologies to discover a potent inhibitor for the FTO protein. This study uses deep learning-based methods and molecular docking techniques to investigate the FTO protein as a target. Our strategy involves systematically screening a database of small chemical compounds. By utilizing the crystal structures of the FTO complexed with ligands, we successfully identified three small-molecule chemical compounds (ZINC000003643476, ZINC000000517415, and ZINC000001562130) as inhibitors of the FTO protein. The identification process was accomplished by employing a combination of screening techniques, specifically deep learning (DeepBindGCN) and Autodock vina, on the ZINC database. These compounds were subjected to comprehensive analysis using 100 nanoseconds of molecular dynamics and binding free energy calculations. The findings of our study indicate the identification of three candidate inhibitors that might effectively target the human fat mass and obesity protein. The results of this study have the potential to facilitate the exploration of other chemicals that can interact with FTO. Conducting biochemical studies to evaluate these compounds' effectiveness may contribute to improving fat mass and obesity treatment strategies. [ABSTRACT FROM AUTHOR]

Clay minerals are implicated in the retention of biomolecules within organic matter in many soil environments. Spectroscopic studies have proposed several mechanisms for biomolecule adsorption on clays. Here, we employ molecular dynamics simulations to investigate these mechanisms in hydrated adsorbate conformations of montmorillonite, a smectite-type clay, with ten biomolecules of varying chemistry and structure, including sugars related to cellulose and hemicellulose, lignin-related phenolic acid, and amino acids with different functional groups. Our molecular modeling captures biomolecule-clay and biomolecule-biomolecule interactions that dictate selectivity and competition in adsorption retention and interlayer nanopore trapping, which we determine experimentally by nuclear magnetic resonance (NMR) and X-ray diffraction, respectively. Specific adsorbate structures are important in facilitating the electrostatic attraction and Van der Waals energies underlying the hierarchy in biomolecule adsorption. Stabilized by a network of direct and water-bridged hydrogen bonds, favorable electrostatic interactions drive this hierarchy whereby amino acids with positively charged side chains are preferentially adsorbed on the negatively charged clay surface compared to the sugars and carboxylate-rich aromatics and amino acids. With divalent metal cations, our model adsorbate conformations illustrate hydrated metal cation bridging of carboxylate-containing biomolecules to the clay surface, thus explaining divalent cation-promoted adsorption from our experimental data. Adsorption experiments with a mixture of biomolecules reveal selective inhibition in biomolecule adsorption, which our molecular modeling attributes to electrostatic biomolecule-biomolecule pairing that is more energetically favorable than the biomolecule-clay complex. In sum, our findings highlight chemical and structural features that can inform hypotheses for predicting biomolecule adsorption at water-clay interfaces. [ABSTRACT FROM AUTHOR]

Context: This study employs a data-guided approach to evaluate zeolites for hydrogen storage, utilizing molecular simulations. The development of efficient and practical hydrogen storage materials is crucial for advancing clean energy technologies. Zeolites have shown promise as potential candidates due to their unique porous structure and tunable properties. However, the selection and design of suitable zeolites for hydrogen storage remain challenging. Therefore, this work aims to address this materials science question by utilizing molecular simulations and data-guided approaches to evaluate zeolites' performance for hydrogen storage. The results obtained from this study provide valuable insights into the evaluation of zeolites for hydrogen storage. Through molecular simulations, we analyze the adsorption behavior of hydrogen molecules in various zeolite structures. The performance of different zeolite frameworks in terms of hydrogen storage capacity, adsorption energy, and diffusion properties is assessed. Linde type A zeolite (LTA) had the highest capacity with a hydrogen capacity of 4.8wt% out of the 233 investigated zeolites. Furthermore, we investigate the influence of different factors such as mass (M), density (D), helium void fraction (HVF), accessible pore volume (APV), gravimetric surface area (GSA), and largest overall cavity diameter (Di) on the hydrogen storage performance of zeolites. The results show that Di, D, and M have a negative effect on the percentage weight capacity, while GSA and VSA have the highest positive contribution to the percentage weight. This study, therefore, provides new insights into the factors that affect their hydrogen storage capacity by exhibiting the importance of considering multiple factors when evaluating the performance of zeolites and demonstrates the potential of combining different computational methods to provide a more comprehensive understanding of materials. The current study contributes to the understanding of zeolite-based materials for hydrogen storage applications, aiding in the development of more efficient and practical hydrogen storage systems. Methods: Computational techniques were employed to investigate the hydrogen storage properties of zeolites. Molecular simulations were performed using classical force fields and molecular dynamics methods. The calculations were carried out at a force field level of theory with the GGA functional. To accurately capture the thermodynamics and kinetics of hydrogen adsorption, enhanced sampling techniques such as Monte Carlo simulations and molecular dynamics with metadynamics were utilized. We employed Grand Canonical Monte Carlo (GCMC) simulations to model hydrogen adsorption in zeolite structures for hydrogen storage. Our approach involved performing a substantial number of Monte Carlo steps (10,000) to ensure system equilibration and precise results. We defined a cutoff distance for particle interactions as 12.5 Ǻ and considered 0.000e framework charge per cell and 0.000e sorbate charge in energy calculations. The choice of an appropriate simulation cell size (50 × 50 × 50) Ǻ was crucial, mirroring real-world conditions. We specified lower and upper fugacity values (1 to 10 atm) to capture the range of gas pressures in the simulations. These methodical steps collectively enabled us to accurately model hydrogen adsorption within zeolites, forming the core of our hydrogen storage evaluation. In this research, we utilized DFT calculations to thoroughly investigate the interactions between zeolites and hydrogen. We employed pseudopotentials to describe electron behavior in zeolite systems, choosing them in line with DFT norms and basis set compatibility. Our simulation cell design replicated zeolite periodicity and eliminated boundary effects. Pre-geometry optimization was performed with HyperChem29, ensuring stable conformations with strict convergence criteria. We utilized 6–31 + G(d) and LanL2DZ basis sets for light and heavy atoms, aligning with field standards for computational efficiency and precision. A machine learning algorithm was used to rank the importance of various structural features such as mass (M), density (D), helium void fraction (HVF), accessible pore volume (APV), gravimetric surface area (GSA), and largest overall cavity diameter (Di) and how they affect the capacity of the zeolites. Machine learning analysis was performed with the Scikit-learn library, an open-source Python tool. We employed a range of machine learning models, including SVMs, random forests, and neural networks, primarily for data analysis and feature extraction. Pearson correlation analysis, a classical statistical technique, was used to evaluate linear relationships between variables and assess the strength and direction of these relationships. It served as a complementary tool to understand the interplay of variables in our dataset, distinguishing it from machine learning algorithms. Further quantum chemical calculations were also performed to calculate the adsorption energy, global reactivity electronic descriptors, and natural bond orbital analysis in order to provide insights into the interaction of the zeolites with hydrogen. The simulations and data analysis were performed using BIOVIA material studio software, Gaussian, and Origin Pro software. [ABSTRACT FROM AUTHOR]

The unique viviparous Pacific Beetle cockroaches provide nutrition to their embryo by secreting milk proteins Lili-Mip, a lipid-binding glycoprotein that crystallises in-vivo. The resolved in-vivo crystal structure of variably glycosylated Lili-Mip shows a classical Lipocalin fold with an eight-stranded antiparallel beta-barrel enclosing a fatty acid. The availability of physiologically unaltered glycoprotein structure makes Lili-Mip a very attractive model system to investigate the role of glycans on protein structure, dynamics, and function. Towards that end, we have employed all-atom molecular dynamics simulations on various glycosylated stages of a bound and free Lili-Mip protein and characterised the impact of glycans and the bound lipid on the dynamics of this glycoconjugate. Our work provides important molecular-level mechanistic insights into the role of glycans in the nutrient storage function of the Lili-Mip protein. Our analyses show that the glycans stabilise spatially proximal residues and regulate the low amplitude opening motions of the residues at the entrance of the binding pocket. Glycans also preserve the native orientation and conformational flexibility of the ligand. However, we find that either deglycosylation or glycosylation with high-mannose and paucimannose on the core glycans, which better mimic the natural insect glycosylation state, significantly affects the conformation and dynamics. A simple but effective distance- and correlation-based network analysis of the protein also reveals the key residues regulating the barrel's architecture and ligand binding characteristics in response to glycosylation. [ABSTRACT FROM AUTHOR]

Reproductive processes in fishes are regulated by the hypothalamic-pituitary-gonadal (HPG) axis, much like in tetrapods. Within this system, Gonadotropin-Releasing Hormone (GnRH) is released by the hypothalamus, binding to GnRH receptors in the pituitary gland and stimulating the secretion of gonadotropin hormones. The current study aimed to analyze the GnRH receptor in Oncorhynchus mykiss (rainbow trout) using a computational and structural biology approach. The GnRH receptor gene of O. mykiss comprises a nucleotide sequence of 1707 base pairs with an open reading frame of 1251 base pairs, which is responsible for encoding 416 amino acids. It was found that the GnRH receptor contains leucine (L) as the most abundant amino acid. The secondary structure revealed that alpha helices constitute the largest percentage (36 %) with 153 residues, followed by extended strands with 77 residues (17.51 %). The GnRH receptor contains 26 negatively charged and 37 positively charged amino acid residues. The highest hydrophilicity was observed for lysine (K) at position 310, with a value of −3.900, while the highest hydrophobicity was found for leucine (L) at position 290, with a value of 3.80. Molecular docking analysis showed that the most favorable binding energy was observed for Gestrinone (−7.8 kcal/mol). Gestrinone was found to form hydrogen bonds with MET160, LUE245, LUE62, TYR216, and GLN209 residues of GnRH. Moreover, molecular dynamics revealed that the complexes form robust and enduring connections, indicating their structural integrity throughout the simulation. The results of this study provide insights into the protein modeling, molecular docking, and virtual screening of antagonist ligands against the GnRH receptor. Additionally, they may significantly aid in the advancement and improvement of therapeutic strategies targeted at treating various fish reproductive dysfunctions.

Ionic conductivity plays an important role towards the application of ionic liquids as electrolytes in next-generation batteries and electrochemical processes and is often estimated using the Nernst–Einstein formalism in molecular simulation-based studies. The Nernst–Einstein formalism is useful for dilute systems where ions do not interact with each other, restricting its applicability to dilute solutions. However, this approximation fails in concentrated solutions where ion interactions become significant, which is usually encountered for pure ionic liquids. These ion-ion correlations can dramatically affect ionic conductivity predictions in comparison to that computed under the Nernst–Einstein formalism. This study highlights the challenges associated with calculating ionic conductivity using Einstein formalism and subsequently provides a workflow for such calculations. It has been found that a minimum trajectory length of 60 ns is required to achieve converged results for Einstein ionic conductivity. Guidance is also given to reduce the computational resource requirements for Einstein conductivity determination. This simplification will enable researchers to estimate Einstein conductivity in ionic liquids more efficiently.

Gene mutations are a source of genetic instability which fuels the progression of cancer. Mutations in BRCA1 and BRCA2 are considered as major drivers in the progression of breast cancer and their detection indispensable for devising therapeutic and management approaches. The current study aims to identify novel pathogenic and recurrent mutations in BRCA1 and BRCA2 in Pakhtun population from the Khyber Pakhtunkhwa. To determine the BRCA1 and BRCA2 pathogenic mutation prevalence in Pakhtun population from KP, whole exome sequencing of 19 patients along with 6 normal FFPE embedded blocks were performed. The pathogenicity of the mutations were determined and they were further correlated with different hormonal, sociogenetic and clinicopathological features. We obtained a total of 10 mutations (5 somatic and 5 germline) in BRCA1 while 27 mutations (24 somatic and 3 germline) for BRCA2. Five and seventeen pathogenic or deleterious mutations were identified in BRCA1 and BRCA2 respectively by examining the mutational spectrum through SIFT, PolyPhen-2 and Mutation Taster. Among the SNVs, BRCA1 p.P824L, BRCA2 p. P153Q, p.I180F, p.D559Y, p.G1529R, p.L1576F, p.E2229K were identified as mutations of the interaction sites as predicted by the deep algorithm based ISPRED-SEQ prediction tool. SAAFEQ-SEQ web-based algorithm was used to calculate the changes in free energy and effect of SNVs on protein stability. All SNVs were found to have a destabilizing effect on the protein. ConSurf database was used to determine the evolutionary conservation scores and nature of the mutated residues. Gromacs 4.5 was used for the molecular simulations. Ramachandran plots were generated using procheck server. STRING and GeneMania was used for prediction of the gene interactions. The highest number of mutations (BRCA1 7/10, 70 %) were on exon 9 and (BRCA2, 11/27; 40 %) were on exon 11. 40 % and 60 % of the BRCA2 mutations were associated Grade 2 and Grade 3 tumors respectively. The present study reveals unique BRCA1 and BRCA2 mutations in Pakhtun population. We further suggest sequencing of the large cohorts for further characterizing the pathogenic mutations.