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Vitrimers are an innovative class of polymers that boast a remarkable fusion of mechanical and dynamic features, complemented by the added benefit of end-of-life recyclability. This extraordinary blend of properties makes them highly attractive for a variety of applications, such as the automotive sector, soft robotics, and the aerospace industry. At their core, vitrimer materials consist of crosslinked covalent networks that have the ability to dynamically reorganize in response to external factors, including temperature changes, pressure variations, or shifts in pH levels. In this review, the aim is to delve into the latest advancements in the theoretical understanding and computational design of vitrimers. The review begins by offering an overview of the fundamental principles that underlie the behavior of these materials, encompassing their structures, dynamic behavior, and reaction mechanisms. Subsequently, recent progress in the computational design of vitrimers is explored, with a focus on the employment of molecular dynamics (MD)/Monte Carlo (MC) simulations and density functional theory (DFT) calculations. Last, the existing challenges and prospective directions for this field are critically analyzed, emphasizing the necessity for additional theoretical and computational advancements, coupled with experimental validation.

Molecularly imprinted polymers (MIPs) are artificial receptors equipped with selective recognition sites for target molecules. One of the most promising strategies for protein MIPs relies on the exploitation of short surface‐exposed protein fragments, termed epitopes, as templates to imprint binding sites in a polymer scaffold for a desired protein. However, the lack of high‐resolution structural data of flexible surface‐exposed regions challenges the selection of suitable epitopes. Here, we addressed this drawback by developing a polyscopoletin‐based MIP that recognizes recombinant proteins via imprinting of the widely used Strep‐tag II affinity peptide (Strep‐MIP). Electrochemistry, surface‐sensitive IR spectroscopy, and molecular dynamics simulations were employed to ensure an utmost control of the Strep‐MIP electrosynthesis. The functionality of this novel platform was verified with two Strep‐tagged enzymes: an O2‐tolerant [NiFe]‐hydrogenase, and an alkaline phosphatase. The enzymes preserved their biocatalytic activities after multiple utilization confirming the efficiency of Strep‐MIP as a general biocompatible platform to confine recombinant proteins for exploitation in biotechnology. [ABSTRACT FROM AUTHOR]

Hydrogen–Deuterium exchange mass spectrometry's (HDX-MS) utility in identifying and characterizing protein–small molecule interaction sites has been established. The regions that are seen to be protected from exchange upon ligand binding indicate regions that may be interacting with the ligand, giving a qualitative understanding of the ligand binding pocket. However, quantitatively deriving an accurate high-resolution structure of the protein–ligand complex from the HDX-MS data remains a challenge, often limiting its use in applications such as small molecule drug design. Recent efforts have focused on the development of methods to quantitatively model Hydrogen–Deuterium exchange (HDX) data from computationally modeled structures to garner atomic level insights from peptide-level resolution HDX-MS. One such method, HDX ensemble reweighting (HDXer), employs maximum entropy reweighting of simulated HDX data to experimental HDX-MS to model structural ensembles. In this study, we implement and validate a workflow which quantitatively leverages HDX-MS data to accurately model protein–small molecule ligand interactions. To that end, we employ a strategy combining computational protein–ligand docking, molecular dynamics simulations, HDXer, and dimensional reduction and clustering approaches to extract high-resolution drug binding poses that most accurately conform with HDX-MS data. We apply this workflow to model the interaction of ERK2 and FosA with small molecule compounds and inhibitors they are known to bind. In five out of six of the protein–ligand pairs tested, the HDX derived protein–ligand complexes result in a ligand root-mean-square deviation (RMSD) within 2.5 Å of the known crystal structure ligand. [ABSTRACT FROM AUTHOR]

Recent breakthroughs in protein structure prediction have enhanced the precision and speed at which protein configurations can be determined. Additionally, molecular dynamics (MD) simulations serve as a crucial tool for capturing the conformational space of proteins, providing valuable insights into their structural fluctuations. However, the scope of MD simulations is often limited by the accessible timescales and the computational resources available, posing challenges to comprehensively exploring protein behaviors. Recently emerging approaches have focused on expanding the capability of AlphaFold2 (AF2) to predict conformational substates of protein. Here, we benchmark the performance of various workflows that have adapted AF2 for ensemble prediction and compare the obtained structures with ensembles obtained from MD simulations and NMR. We provide an overview of the levels of performance and accessible timescales that can currently be achieved with machine learning (ML) based ensemble generation. Significant minima of the free energy surfaces remain undetected. [Display omitted] • Ensemble prediction quality depends on training input to AlphaFold 2 (AF2) • MSA subsampling predicts ensembles but may miss key protein conformations • Current ensembles cannot reliably determine free energy, conformations, or properties • ⁠Ensemble data is crucial to improve conformational model accuracy Riccabona et al. underscore the importance of accurate structural data in predicting protein structural ensembles. They note that although rapid methods like MSA subsampling can generate ensembles, they often overlook functionally significant conformations, thereby missing crucial kinetic and thermodynamic insights. [ABSTRACT FROM AUTHOR]

Immunotherapy is a promising treatment for cancer that aims to boost the immune system's response to cancer cells. This can be achieved by blocking Programmed cell death protein 1/Programmed death-ligand 1 (PD1/PDL1), which activates T cells. In this work, the aim was to find high-affinity drugs against PDL1 using computational tools and conjugate nanoparticles with them. The cytotoxic activity of the nanoparticle conjugated drugs was then tested. The screening of 100,000 drugs from the ZINC database and FDA-approved drugs was done computationally. The physicochemical properties and toxicity of the drugs were analyzed using SwissADME and ProTox-II, respectively. Silver nanoparticles (AgNPs) and gold nanoparticles (AuNPs) were synthesized using extracts of Catharanthus roseus flowers and Juglans regia shells, respectively. The characterization of AgNPs and AuNPs was performed using UV–Vis spectroscopy, X-ray diffraction (XRD), and Fourier transform infrared spectroscopy (FTIR). Their conjugation with the drugs Irinotecan, Imatinib, and Methotrexate was also confirmed using UV–Vis, FTIR, and Dynamic light scattering (DLS). The top screened drugs were ZINC1098661 and 3 FDA-approved drugs (Irinotecan, Imatinib, and Methotrexate). Docking studies revealed that Irinotecan had the highest binding affinity towards PDL1 when conjugated with silver nanoparticles (AgNPs) and gold nanoparticles (AuNPs). The Irinotecan-PDL1 complex was confirmed as the most stable through molecular dynamics simulations. The result of the methylthiazol tetrazolium (MTT) assay showed that conjugated AgNPs and AuNPs with Irinotecan had a higher toxic effect on the A549 cancer cell line than AgNPs and AuNPs conjugated with Imatinib. This study provides a promising avenue for further investigation and development of nanoparticle-drug conjugates as a potential cancer immunotherapy strategy. [ABSTRACT FROM AUTHOR]

Materials data science and machine learning (ML) are pivotal in advancing cancer treatment strategies beyond traditional methods like chemotherapy. Nanotherapeutics, which merge nanotechnology with targeted drug delivery, exemplify this advancement by offering improved precision and reduced side effects in cancer therapy. The development of these nanotherapeutic agents depends critically on understanding nanoparticle (NP) properties and their biological interactions, often analyzed through molecular dynamics (MD) simulations. This study enhances these analyses by integrating ML with MD simulations, significantly improving both prediction accuracy and computational efficiency. We introduce a comprehensive three-stage methodology for predicting the solvent-accessible surface area (SASA) of NPs, which is crucial for their therapeutic efficacy. The process involves training an ML model to forecast the many-body tensor representation (MBTR) for future time steps, applying data augmentation to increase dataset realism, and refining the SASA predictor with both augmented and original data. Results demonstrate that our methodology can predict SASA values 299 time steps ahead with a 40-fold speed improvement and a 25% accuracy increase over existing methods. Importantly, it provides a 300-fold increase in computational speed compared to traditional simulation techniques, offering substantial cost and time savings for nanotherapeutic research and development. [ABSTRACT FROM AUTHOR]

Background: Type 2 diabetes mellitus is a metabolic disease categorized by hyperglycemia, resistance to insulin, and ß-cell dysfunction. Around the globe, approximately 422 million people have diabetes, out of which 1.5 million die annually. In spite of innovative advancements in the treatment of diabetes, no biological drug has been known to successfully cure and avert its progression. Thereupon, natural drugs derived from plants are emerging as a novel therapeutic strategy to combat diseases like diabetes. Objective: The current study aims to investigate the antidiabetic potential of natural compounds of Oryza sativa L. indica (black rice) in disease treatment. Methods: Antioxidant activity and alpha amylase assays were performed to evaluate the therapeutic potential of the extract of Oryza sativa L. indica. Gas chromatography–mass spectrometry (GC–MS) was used for identification of constituents from the ethanol extract. ADMET profiling (absorption, distribution, metabolism, excretion, and toxicity), network pharmacology, and molecular dynamics simulation were employed in order to uncover the active ingredients and their therapeutic targets in O. sativa L. indica against type 2 diabetes mellitus. Results: GC–MS of the plant extract provided a list of 184 compounds. Lipinski filter and toxicity parameters screened out 18 compounds. The topological parameters of the protein–protein interaction (PPI) were used to shortlist the nine key proteins (STAT3, HSP90AA1, AKT1, SRC, ESR1, MAPK1, NFKB1, EP300, and CREBBP) in the type 2 diabetes mellitus pathways. Later, molecular docking analysis and simulations showed that C14 (1H-purine-8-propanoic acid,.alpha.-amino-2, 3, 6, 7-tetrahydro-1,3,7-trimethyl-2,6-dioxo-) and C18 (cyclohexane-carboxamide, N-furfuryl) bind with AKT1 and ESR1 with a binding energy of 8.1, 6.9, 7.3, and 7.2 kcal/mol, respectively. RMSD (root-mean-square deviation) and RMSF (root-mean-square fluctuation) values for AKT1 and ESR1 have shown very little fluctuation, indicating that proteins were stabilized after ligand docking. Conclusion: This study suggests therapeutic drug candidates against AKT1 and ESR1 to treat type 2 diabetes mellitus. However, further wet-lab analysis is required to discover the best remedy for type 2 diabetes mellitus. [ABSTRACT FROM AUTHOR]

In photosynthesis, light energy is absorbed and transferred to the reaction center, ultimately leading to the reduction of quinone molecules through the electron transfer chain. The oxidation and reduction of quinones generate an electrochemical potential difference used for adenosine triphosphate synthesis. The trafficking of quinone/quinol molecules between electron transport components has been a long‐standing question. Here, an atomic‐level investigation into the molecular mechanism of quinol dissociation in the photosynthetic reaction center–light‐harvesting complex 1 (RC–LH1) supercomplexes from Rhodopseudomonas palustris, using classical molecular dynamics (MD) simulations combined with self‐random acceleration MD‐MD simulations and umbrella sampling methods, is conducted. Results reveal a significant increase in the mobility of quinone molecules upon reduction within RC–LH1, which is accompanied by conformational modifications in the local protein environment. Quinol molecules have a tendency to escape from RC–LH1 in a tail‐first mode, exhibiting channel selectivity, with distinct preferred dissociation pathways in the closed and open LH1 rings. Furthermore, comparative analysis of free energy profiles indicates that alternations in the protein environment accelerate the dissociation of quinol molecules through the open LH1 ring. In particular, aromatic amino acids form π‐stacking interactions with the quinol headgroup, resembling the key components in a conveyor belt system. This study provides insights into the molecular mechanisms that govern quinone/quinol exchange in bacterial photosynthesis and lays the framework for tuning electron flow and energy conversion to improve metabolic performance. [ABSTRACT FROM AUTHOR]

The most fruitful prevention and treatment tools for the COVID-19 pandemic have proven to be vaccines and therapeutic antibodies, which have reduced the spread of the disease to manageable proportions. The search for the most effective antibodies against the widest set of COV-2 variants has required a long time and substantial resources. It would be desirable to have a tool that will enable us to understand the structural basis on which mutants escape at least some of the epitope-bound antibodies, a tool that may substantially reduce the time and resources invested in this effort. In this work, we applied a computational-based tool (employed previously by us to understand COV-2 spike binding to its cognate cell receptor) to the study of the effect of Delta and Omicron mutations on the escape tendencies. Our binding energy predictions agree extremely well with the experimentally observed escape tendencies. They have also allowed us to set forth a structural explanation for the results that could be used for the screening of antibodies. Lastly, our results explain the differences in molecular interactions that govern interaction of the spike variants with the receptor as opposed to those with antibodies. [ABSTRACT FROM AUTHOR]

As a widely used plastic, the aging and degradation of polyethylene (PE) are inevitable problems, whether the goal is to prolong the life of PE products or address the issue of white pollution. Molecular simulation is a vital scientific tool in elucidating the mechanisms and processes of chemical reactions. To obtain the distribution and evolution process of PE's thermal oxidation products, this work employs the self-consistent charge–density functional tight binding (SCC-DFTB) method to perform molecular simulations of the thermal oxidation of PE with different crystallinity and branched structures. We discovered that crystallinity does not affect the thermal oxidation mechanism of PE, but higher crystallinity makes PE more susceptible to cross-linking and carbon chain growth, reducing the degree of PE carbon chain breakage. The branched structure of PE results in differences in free volumes between the carbon chains, with larger pores leading to a concentrated distribution of O2 and chemical defects subsequently formed. The breakdown of PE is slowed down when chemical defects are localized in low-density regions of the carbon chain. The specifics and mechanism of PE's thermal oxidation are clearly revealed in this paper, which is essential for understanding the process in depth and for the development of anti-aging PE products. [ABSTRACT FROM AUTHOR]

Glutaminases are a unique group of hydrolytic enzymes with potential applications in cancer therapy and the food industry. This study focused on the biological characterisation and computational conformation dynamics of YLaM, a potential L-glutaminase from a halothermotolerant Bacillus licheniformis. The research integrated experimental and computational methods to identify the YLaM sequence and predict its structural and functional properties. YLaM was amplified using a polymerase chain reaction, cloned into a pET-22b(+) vector, sequenced, and submitted to BankIt/NCBI. This experimental work was complemented by in silico analysis, including structural modelling via the I-TASSER web server, molecular docking using AutoDockTools 1.5.6, and molecular dynamic (MD) simulations using GROMACS v5.1.4. The study resulted in the development of a high-quality 3D model of YLaM through homology modelling and structural refinement, enabling a detailed exploration of its binding affinity with L-glutamine (L-Gln). By identifying active site residues using the homotetramer structure of human glutaminase, crucial interactions with L-Gln were validated, confirming its catalytic function. Finally, MD simulations confirmed the stability of the complex under physiological conditions. This combined molecular identification and functional simulations provide comprehensive insights into the physicochemical properties of the enzyme, offering valuable information for subsequent assessments, including recombinant production and biological impact evaluations. [ABSTRACT FROM AUTHOR]

The monoclonal IgE antibody SPE‐7 was originally raised against a 2,4‐dinitrophenyl (DNP) target. Through its ability to adopt multiple conformations, the antibody is capable of binding to a diverse range of small haptens and large proteins. The present study examines a dataset of experimentally determined crystal structures of the SPE‐7 antibody to gain insight into the mechanisms that contribute to its multispecificity. With the emergence of more and more therapeutic antibodies against a huge repertoire of different targets, our research could be of great interest for future drug development. We are able to discriminate between the different paratope‐binding states in the conformational ensembles obtained by enhanced sampling molecular dynamics simulations, and to calculate their transition timescales and state probabilities. Furthermore, we describe the key residues responsible for discriminating between the different binding capacities and identify a tryptophan in a central position of the CDR L3 loop as the residue of greatest interest. The overall dynamics of the paratope appear to be mainly influenced by the CDR L3 and CDR L1 loops. [ABSTRACT FROM AUTHOR]

Inulin, a widely recognized prebiotic, has diverse applications across various industrial sectors. Although inulin is primarily produced through plant extraction, there is growing interest in enzymatic synthesis as an alternative. The enzymatic production of inulin from sucrose, which yields polymers with degrees of polymerization similar to those of plant-derived inulin, shows potential as a viable replacement for traditional extraction methods. In this study, an inulosucrase from Neobacillus bataviensis was identified, demonstrating a non-processive mechanism specifically tailored for synthesizing inulin with polymerization degrees ranging from 3 to approximately 40. The enzyme exhibited optimal activity at pH 6.5 and 55 °C, efficiently producing inulin with a yield of 50.6%. Ca2+ can improve the activity and thermostability of this enzyme. To enhance catalytic total activity, site-directed and truncated mutagenesis techniques were applied, resulting in the identification of a mutant, T149S, displaying a significant 57% increase in catalytic total activity. Molecular dynamics simulations unveiled that the heightened flexibility observed in three surface regions positively influenced enzymatic activity. This study not only contributes to the theoretical foundation for inulosucrase engineering but also presents a potential avenue for the production of inulin. [ABSTRACT FROM AUTHOR]

Encapsulating natural enzymes in metal—organic frameworks (MOFs) can maintain the original biological functions of enzymes in harsh environments. However, the nature of interfacial interactions between a MOF and enzyme is currently unclear, rendering effective regulation of the biocatalytic activity of the enzyme@MOF composite difficult. Differences in the hydrophilicity of MOF carriers are closely related to the conformational changes and catalytic properties of the enzyme. In this study, the catalytic activity, stability, and conformational changes of alkaline phosphatase (ALP) encapsulated in hydrophilic zeolite imidazolate framework-90 (ZIF-90) and hydrophobic ZIF-8 were systematically investigated using experimental methods and molecular dynamics simulations. The results demonstrated that hydrophilic ZIF-90-encapsulated ALP exhibited superior stability and was 2.22-fold more retained catalytically active than hydrophobic ALP@ZIF-8 after 20 cycles of utilization. Moreover, the hydrophilic interface provided by ZIF-90 effectively regulated the structure of ALP to maintain the optimal catalytic conformation of its active center. The practical application of highly bioactive ALP@ZIF-90 was demonstrated by employing it in a self-calibrated colorimetric/fluorescence dual-mode sensing method for the efficient, reliable, and accurate detection of methyl paraoxon. This study provides new insights for improving enzyme immobilization strategies and promoting the rapid development of enzyme@MOF composites for catalytic and sensing applications. [ABSTRACT FROM AUTHOR]

The MC1R protein is a receptor found in melanocytes that plays a role in melanin synthesis. Mutations in this protein can impact hair color, skin tone, tanning ability, and increase the risk of skin cancer. The MC1R protein is activated by the alpha‐melanocyte‐stimulating hormone (α‐MSH). Previous studies have shown that mutations affect the interaction between MC1R and α‐MSH; however, the mechanism behind this process is poorly understood. Our study aims to shed light on this mechanism using molecular dynamics (MD) simulations to analyze the Asp84Glu and Asp294His variants. We simulated both the wild‐type (WT) protein and the mutants with and without ligand. Our results reveal that mutations induce unique conformations during state transitions, hindering the switch between active and inactive states and decreasing cellular levels of cAMP. Interestingly, Asp294His showed increased ligand affinity but decreased protein activity, highlighting that tighter binding does not always lead to increased activation. Our study provides insights into the molecular mechanisms underlying the impact of MC1R mutations on protein activity. [ABSTRACT FROM AUTHOR]

The blood protein von Willebrand factor (VWF) is a large multimeric protein that, when activated, binds to blood platelets, tethering them to the site of vascular injury and initiating blood coagulation. This process is critical for the normal hemostatic response, but especially under inflammatory conditions, it is thought to be a major player in pathological thrombus formation. For this reason, VWF has been the target for the development of anti‐thrombotic therapeutics. However, it is challenging to prevent pathological thrombus formation while still allowing normal physiological blood coagulation, as currently available anti‐thrombotic therapeutics are known to cause unwanted bleeding, in particular intracranial hemorrhage. This work explores the possibility of inhibiting VWF selectively under the inflammatory conditions present during pathological thrombus formation. In particular, the A2 domain of VWF is known to inhibit the neighboring A1 domain from binding to the platelet surface receptor GpIbα, and this auto‐inhibitory mechanism has been shown to be removed by oxidizing agents released during inflammation. Hence, finding drug molecules that bind at the interface between A1 and A2 only under oxidizing conditions could restore such an auto‐inhibitory mechanism. Here, by using a combination of computational docking, molecular dynamics simulations, and free energy perturbation calculations, a ligand from the ZINC15 database was identified that binds at the A1A2 interface, with the interaction being stronger under oxidizing conditions. The results provide a framework for the discovery of drug molecules that bind to a protein selectively in the presence of inflammatory conditions. [ABSTRACT FROM AUTHOR]

In this study, the interaction of antimicrobial peptide Maximin 3 (Max3) with three different lipid bilayer models was investigated to gain insight into its mechanism of action and membrane specificity. Bilayer perturbation assays using liposome calcein leakage dose–response curves revealed that Max3 is a selective membrane‐active peptide. Dynamic light scattering recordings suggest that the peptide incorporates into the liposomal structure without producing a detergent effect. Langmuir monolayer compression assays confirmed the membrane inserting capacity of the peptide. Attenuated total reflection‐Fourier transform infrared spectroscopy showed that the fingerprint signals of lipid phospholipid hydrophilic head groups and hydrophobic acyl chains are altered due to Max3‐membrane interaction. On the other hand, all‐atom molecular dynamics simulations (MDS) of the initial interaction with the membrane surface corroborated peptide‐membrane selectivity. Peptide transmembrane MDS shed light on how the peptide differentially modifies lipid bilayer properties. Molecular mechanics Poisson–Boltzmann surface area calculations revealed a specific electrostatic interaction fingerprint of the peptide for each membrane model with which they were tested. The data generated from the in silico approach could account for some of the differences observed experimentally in the activity and selectivity of Max3. [ABSTRACT FROM AUTHOR]

Influenza poses a significant threat to global health, with the ability to cause severe epidemics and pandemics. The polymerase basic protein 2 (PB2) of the influenza virus plays a crucial role in the viral replication process, making the CAP-binding domain of PB2 an attractive target for antiviral drug development. This study aimed to identify and evaluate potential inhibitors of the influenza polymerase PB2 CAP-binding domain using computational drug discovery methods. We employed a comprehensive computational approach involving virtual screening, molecular docking, and 500 ns molecular dynamics (MD) simulations. Compounds were selected from the Diverse lib database and assessed for their binding affinity and stability in interaction with the PB2 CAP-binding domain. The study utilized the generalized amber force field (GAFF) for MD simulations to further evaluate the dynamic behaviour and stability of the interactions. Among the screened compounds, compounds 1, 3, and 4 showed promising binding affinities. Compound 4 demonstrated the highest binding stability and the most favourable free energy profile, indicating strong and consistent interaction with the target domain. Compound 3 displayed moderate stability with dynamic conformational changes, while Compound 1 maintained robust interactions throughout the simulations. Comparative analyses of these compounds against a control compound highlighted their potential efficacy. Compound 4 emerged as the most promising inhibitor, with substantial stability and strong binding affinity to the PB2 CAP-binding domain. These findings suggest that compound 4, along with compounds 1 and 3, holds the potential for further development into effective antiviral agents against influenza. Future studies should focus on experimental validation of these compounds and exploration of resistance mechanisms to enhance their therapeutic utility. [ABSTRACT FROM AUTHOR]

AbstractThe high viscosity and poor fluidity of heavy oil challenge its extraction, leading to the widespread use of surfactant emulsification to reduce viscosity and enhance recovery. This study evaluated three surfactants—sodium dodecyl sulfate (SDS), sodium oleate (SO), and APG0810—for their suitability and effectiveness in the X reservoir. The solution properties of these surfactants were analyzed and their micro-mechanisms investigated using MD calculations. The effects of surfactant concentration and additives on emulsification and viscosity reduction were elucidated. Ultimately, a system for emulsification and viscosity reduction was selected for physical simulation research. The experimental findings show that SO reduces interfacial tension from 52.4 mN/m to 0.0027 mN/m, transforming lipophilic surfaces into hydrophilic. MD analyses reveal SO’s optimal interfacial properties, with the lowest interfacial energy of −6423.4 kcal/mol, maximum interfacial thickness of 2.56 nm, and minimal diffusion coefficient of 0.4087 Å2/ps at the oil-water interface. These findings align with experimental results, confirming SO’s superior interfacial properties. Combining 0.3% SO with 0.5% n-pentanol results in a 98% viscosity reduction. The emulsion shows excellent stability, with no water separation in the first 30 min and only 8.3% after 2 h. Physical simulation results show that in high(low) permeability core displacement experiments, system flooding and subsequent water flooding can increase recovery rates by 15.39%(36.91%), indicating the system’s potential for enhancing oil recovery in Reservoir X. The findings suggest that the implementation of this system not only boosts the crude oil recovery rate but also carries economic significance in the industry. [ABSTRACT FROM AUTHOR]

Monoamine oxidase B (MAO-B) plays a pivotal role in the deamination process of monoamines, encompassing crucial neurotransmitters like dopamine and norepinephrine. The heightened interest in MAO-B inhibitors emerged after the revelation that this enzyme could potentially catalyze the formation of neurotoxic compounds from endogenous and exogenous sources. Computational screening methodologies serve as valuable tools in the quest for novel inhibitors, enhancing the efficiency of this pursuit. In this study, 43 acefylline derivatives were docked against the MAO-B enzyme for their chemotherapeutic potential and binding affinities that yielded GOLD fitness scores ranging from 33.21 to 75.22. Among them, five acefylline derivatives, namely, MAO-B14, MAO-B15, MAO-B16, MAO-B20, and MAO-B21, displayed binding affinities comparable to the both standards istradefylline and safinamide. These derivatives exhibited hydrogen-bonding interactions with key amino acids Phe167 and Ile197/198, suggesting their strong potential as MAO-B inhibitors. Finally, molecular dynamics (MD) simulations were conducted to evaluate the stability of the examined acefylline derivatives over time. The simulations demonstrated that among the examined acefylline derivatives and standards, MAO-B21 stands out as the most stable candidate. Density functional theory (DFT) studies were also performed to optimize the geometries of the ligands, and molecular docking was conducted to predict the orientations of the ligands within the binding cavity of the protein and evaluate their molecular interactions. These results were also validated by simulation-based binding free energies via the molecular mechanics energies combined with generalized Born and surface area solvation (MM-GBSA) method. However, it is necessary to conduct in vitro and in vivo experiments to confirm and validate these findings in future studies. [ABSTRACT FROM AUTHOR]

Graph neural networks (GNN) offer an alternative approach to boost the screening effectiveness in drug discovery. However, their efficacy is often hindered by limited datasets. To address this limitation, we introduced a robust GNN training framework, applied to various chemical databases to identify potent non‐nucleoside reverse transcriptase inhibitors (NNRTIs) against the challenging K103N‐mutated HIV‐1 RT. Leveraging self‐supervised learning (SSL) pre‐training to tackle data scarcity, we screened 1,824,367 compounds, using multi‐step approach that incorporated machine learning (ML)‐based screening, analysis of absorption, distribution, metabolism, and excretion (ADME) prediction, drug‐likeness properties, and molecular docking. Ultimately, 45 compounds were left as potential candidates with 17 of the compounds were previously identified as NNRTIs, exemplifying the model's efficacy. The remaining 28 compounds are anticipated to be repurposed for new uses. Molecular dynamics (MD) simulations on repurposed candidates unveiled two promising preclinical drugs: one designed against Plasmodium falciparum and the other serving as an antibacterial agent. Both have superior binding affinity compared to anti‐HIV drugs. This conceptual framework could be adapted for other disease‐specific therapeutics, facilitating the identification of potent compounds effective against both WT and mutants while revealing novel scaffolds for drug design and discovery. [ABSTRACT FROM AUTHOR]

The widespread use of pesticides poses significant threats to both environmental and human health, primarily due to their potential toxic effects. The study investigated the cardiovascular toxicity of selected pesticides, focusing on their interactions with Toll-like receptor 4 (TLR4), an important part of the innate immune system. Using computational tools such as molecular docking, molecular dynamics (MD) simulations, principal component analysis (PCA), density functional theory (DFT) calculations, and ADME analysis, this study identified C160 as having the lowest binding affinity (-8.2 kcal/mol), followed by C107 and C165 (-8.0 kcal/mol). RMSD, RMSF, Rg, and hydrogen bond metrics indicated the formation of stable complexes between specific pesticides and TLR4. PCA revealed significant structural changes upon ligand binding, affecting stability and flexibility, while DFT calculations provided information about the stability, reactivity, and polarity of the compounds. ADME studies highlighted the solubility, permeability, and metabolic stability of C107, C160, and C165, suggesting their potential for bioavailability and impact on cardiovascular toxicity. C107 and C165 exhibit higher bioactivity scores, indicating favourable absorption, metabolism, and distribution properties. C165 also violated rule where molecular weight is greater than 500 g/mol. Further, DFT and NCI analysis of post MD conformations confirmed the binding of ligands at the binding pocket. The analysis shed light on the molecular mechanisms of pesticide-induced cardiovascular toxicity, aiding in the development of strategies to mitigate their harmful effects on human health. [ABSTRACT FROM AUTHOR]

Magnesium hydroxide, Mg(OH)2, is recognized as a promising material for medium-temperature heat storage, but its low thermal conductivity limits its full potential application. In this study, thermal enhancement of a developed magnesium hydroxide-potassium nitrate (Mg(OH)2-KNO3) material was carried out with aluminum oxide (Al2O3) nanomaterials. The theoretical results obtained through a molecular dynamics (MD) simulation approach showed an enhancement of about 12.9% in thermal conductivity with an optimal 15 wt% of Al2O3. There was also close agreement with the experimental results within an error of ≤10%, thus confirming the reliability of the theoretical approach and the potential of the developed Mg(OH)2-KNO3 as a medium heat storage material. Further investigation is, however, encouraged to establish the long-term recyclability of the material towards achieving a more efficient energy storage process. [ABSTRACT FROM AUTHOR]

Control of thermal transport in colloidal crystals plays an important role in modern technologies. A deeper understanding of the governing heat transport processes in various systems, such as polydisperse colloidal crystals, is required. This study shows how strongly the particle size polydispersity of a model colloidal crystal influences the thermal conductivity. The thermal conductivity of model colloidal crystals has been calculated using molecular dynamics simulations. The model crystals created by particles interacting through Yukawa (screened-Coulomb) interaction are assumed to have a face-centered cubic structure. The influence of the Debye screening length, contact potential, and particle size polydispersity on the thermal conductivity of Yukawa crystals was investigated. It was found that an increase in particle size polydispersity causes a strong—almost fivefold—decrease in the thermal conductivity of Yukawa crystals. In addition, the obtained results showed that the effect of the particle size polydispersity on reducing the thermal conductivity of Yukawa crystals is stronger than changes in values of the Debye screening length or the contact potential. [ABSTRACT FROM AUTHOR]

Electrocatalytic carbon dioxide reduction reaction (CO2RR) has been recognized as a promising route to convert carbon emissions to high‐value chemicals and fuels. Significant breakthroughs are usually inseparable from deeper understanding of reaction mechanisms. To this end, molecular dynamics (MD) simulations have been invaluable in providing detailed insights into elucidation of complex reaction pathways and prediction of overall electrochemical performance, thus bridging macroscopic experimental observations and microscopic explanatory mechanisms. Directed by MD simulations, tremendous efforts have been devoted toward enhancing the CO2RR with rational design of electrocatalyst and efficient construction of electrode/electrolyte interface. Herein, a comprehensive review of applications of MD simulations in CO2RR is emerged. To begin with, specific fundamentals along with familiar methods such as algorithm and force fields of various MD simulations have been summed up. Followed, employment of MD simulations in optimization of CO2RR is introduced, encompassing interpretation of electrocatalyst activity, explanation of electrolyte effect, and investigation of electrode microenvironment. Definitively, imminent challenges and avenues for optimization in future MD simulations are contemplated, envisioning this review as a guiding beacon for future endeavors aimed at harnessing MD simulations to propel CO2RR toward a realm of heightened efficiency, economic viability, and practical utility. [ABSTRACT FROM AUTHOR]

Dynamic mutations in some human genes containing trinucleotide repeats are associated with severe neurodegenerative and neuromuscular disorders—known as Trinucleotide (or Triplet) Repeat Expansion Diseases (TREDs)—which arise when the repeat number of triplets expands beyond a critical threshold. While the mechanisms causing the DNA triplet expansion are complex and remain largely unknown, it is now recognized that the expandable repeats lead to the formation of nucleotide configurations with atypical structural characteristics that play a crucial role in TREDs. These nonstandard nucleic acid forms include single-stranded hairpins, Z-DNA, triplex structures, G-quartets and slipped-stranded duplexes. Of these, hairpin structures are the most prolific and are associated with the largest number of TREDs and have therefore been the focus of recent single-molecule FRET experiments and molecular dynamics investigations. Here, we review the structural and dynamical properties of nucleic acid hairpins that have emerged from these studies and the implications for repeat expansion mechanisms. The focus will be on CAG, GAC, CTG and GTC hairpins and their stems, their atomistic structures, their stability, and the important role played by structural interrupts. [ABSTRACT FROM AUTHOR]

Polycyclic aromatic compounds (PACs) exhibit rat aryl hydrocarbon receptor (rAhR) activities, leading to diverse biological or toxic effects. In this study, the key amino residues and molecular interactions that govern the rAhR activity of PACs were investigated using in silico strategies. The homology model of rAhR was first docked with 90 PACs to yield complexes, and the results of the molecular dynamics simulations of 16 typical complexes showed that the binding energies of the complexes range from −7.37 to −26.39 kcal/mol. The major contribution to the molecular interaction comes from van der Waals forces, and Pro295 and Arg316 become the key residues involved in most complexes. Two QSAR models were further developed to predict the rAhR activity of PACs (in terms of log IEQ for PACs without halogen substitutions and log%-TCDD-max for halogenated PACs). Both models have good predictive ability, robustness, and extrapolation ability. Molecular polarizability, electronegativity, size, and nucleophilicity are identified as the important factors affecting the rAhR activity of PACs. The developed models could be employed to predict the rAhR activity of other reactive PACs. This work provides insight into the mechanisms and interactions of the rAhR activity of PACs and assists in the assessment of their fate and risk in organisms. [ABSTRACT FROM AUTHOR]

c-Src is involved in multiple signaling pathways and serves as a critical target in various cancers. Growing evidence suggests that prolonging a drug's residence time (RT) can enhance its efficacy and selectivity. Thus, the development of c-Src antagonists with longer residence time could potentially improve therapeutic outcomes. In this study, we employed molecular dynamics simulations to explore the binding modes and dissociation processes of c-Src with antagonists characterized by either long or short RTs. Our results reveal that the long RT compound DAS-DFGO-I (DFGO) occupies an allosteric site, forming hydrogen bonds with residues E310 and D404 and engaging in hydrophobic interactions with residues such as L322 and V377. These interactions significantly contribute to the long RT of DFGO. However, the hydrogen bonds between the amide group of DFGO and residues E310 and D404 are unstable. Substituting the amide group with a sulfonamide yielded a new compound, DFOGS, which exhibited more stable hydrogen bonds with E310 and D404, thereby increasing its binding stability with c-Src. These results provide theoretical guidance for the rational design of long residence time c-Src inhibitors to improve selectivity and efficacy. [ABSTRACT FROM AUTHOR]

The glucose‐regulated protein 78 (GRP78) is pivotal in endoplasmic reticulum protein homeostasis and the unfolded protein response during cellular stress. Experimental validation has shown its role in SARS‐CoV‐2 attachment and entry. Here, the full GRP78 sequence, adding carbohydrate sugars to nucleotide‐binding domain sites, and conduct molecular dynamics simulations is modeled. Utilizing DeepPurpose virtual screening on the COCONUT database, followed by blind structure‐based screening with AutoDock Vina, top interacting binders is identified. Molecular dynamic simulations with MM/GBSA reveal stable binding of CNP0339053 and CNP0400762 to GRP78 (free energies of −43.5 ±6.7 and −34.8 ±3.9 kcal mol−1, respectively). These compounds hold promise as safe antiviral treatments for COVID‐19. [ABSTRACT FROM AUTHOR]

AbstractThe quantum chemical properties of 5-hydroxymethylfurfural were investigated using Density Functional Theory alongside vibrational spectroscopy. Key outcomes included optimizing molecular structure, vibrational frequencies, and various molecular parameters. By comparing DFT results with experimental infrared spectra, molecular motion was clarified. Reactive sites were identified through Molecular Electrostatic Potential and Fukui function analyses. Hirshfeld surface analysis revealed insights into the crystal structure’s intermolecular interactions and hydrogen bonding. Time-dependent Density Functional Theory combined with the Polarizable Continuum Model provided Ultraviolet spectra, highlighting charge transfer between the highest occupied and lowest unoccupied molecular orbitals. The compound’s electronegativity (4.7239) and electron affinity were assessed. Biological studies, including drug-likeness evaluations and molecular docking, also demonstrated potential physiological benefits, mainly through the compound’s low binding energy. A 100-nanosecond molecular dynamics simulation of the 5-HMF-4LB4 complex revealed its stability and dynamic behavior through analyses of Root Mean Square Deviation, Root Mean Square Fluctuation, hydrogen bonding, Solvent Accessible Surface Area, and radius of gyration. [ABSTRACT FROM AUTHOR]

Toll‐like receptors (TLRs) are major players in the innate immune system—recognizing pathogens and differentiating self/non‐self components of immunity. These proteins are present either on the plasma membrane or endosome and recognize pathogens at their extracellular domains. They are characterized by a single transmembrane helix and an intracellular toll‐interleukin‐1 receptor (TIR) domain. Few TIRs directly invoke downstream signaling, while others require other TIR domains of adaptors like TIR domain‐containing adaptor‐inducing interferon‐β (TRIF) and TRIF‐related adaptor molecule (TRAM). On recognizing pathogenic lipopolysaccharides, TLR4 dimerises and interacts with the intracellular TRAM dimer through the TIR domain to recruit a downstream signaling adaptor (TRIF). We have performed an in‐depth study of the structural effect of two mutations (P116H and C117H) at the dimeric interface of the adaptor TRAM, which are known to abrogate downstream signaling. We modeled the structure and performed molecular dynamics studies in order to decipher the structural basis of this effect. We observed that these mutations led to an increased radius of gyration of the complex and resulted in several changes to the interaction energy values when compared against the wild type (WT) and positive control mutants. We identified highly interacting residues as hubs in the WT dimer, and a few such hubs that were lost in the mutant dimers. Changes in the protein residue path, hampering the information flow between the crucial A86/E87/D88/D89 and T155/S156 sites, were observed for the mutants. Overall, we show that such residue changes can have subtle but long‐distance effects, impacting the signaling path allosterically. [ABSTRACT FROM AUTHOR]

In the present study, a series of isatin bis-imidathiazole hybrids was designed and synthesized to develop a new class of heterocyclic compounds with improved antimicrobial activity against pathogens responsible for hospital- and community-acquired infections. A remarkable inhibitory activity against Staphylococcus aureus was demonstrated for a subset of compounds (range: 13.8–90.1 µM) in the absence of toxicity towards epithelial cells and human red blood cells. The best performing derivative was further investigated to measure its anti-biofilm potential and its effectiveness against methicillin-resistant Staphylococcus aureus strains. A structure–activity relationship study of the synthesized molecules led to the recognition of some important structural requirements for the observed antibacterial activity. Molecular docking followed by molecular dynamics (MD) simulations identified the binding site of the active compound FtsZ, a key protein in bacterial cell division, and the mechanism of action, i.e., the inhibition of its polymerization. The overall results may pave the way for a further rational development of isatin hybrids as FtsZ inhibitors, with a broader spectrum of activity against human pathogens and higher potency. [ABSTRACT FROM AUTHOR]

Endocytosis plays a complex role in pathogen-host interactions. It serves as a pathway for pathogens to enter the host cell and acts as a part of the immune defense mechanism. Endocytosis involves the formation of lipid membrane vesicles and the reshaping of the cell membrane, a task predominantly managed by proteins containing BAR (Bin1/Amphiphysin/yeast RVS167) domains. Insights into how BAR domains can remodel and reshape cell membranes provide crucial information on infections and can aid the development of treatment. Aiming at deciphering the roles of the BAR dimers in lipid membrane bending and remodeling, we conducted extensive all-atom molecular dynamics simulations and discovered that the presence of helix kinks divides the BAR monomer into two segments—the "arm segment" and the "core segment"—which exhibit distinct movement patterns. Contrary to the prior hypothesis of BAR domains working as a rigid scaffold, we found that it functions in an "Arms-Hands" mode. These findings enhance the understanding of endocytosis, potentially advancing research on pathogen-host interactions and aiding in the identification of new treatment strategies targeting BAR domains. [ABSTRACT FROM AUTHOR]

The rise of multidrug‐resistant organisms (MDROs) represents a significant challenge to healthcare, underscoring the need for novel antimicrobial agents. Quinazolinone compounds, noted for their diverse biological activities, particularly at the 2nd and 3rd positions, in conjunction with sulphanilamide and isatin derivatives, present a promising avenue for antibacterial development. This study focuses on the synthesis of novel 2,3‐disubstituted quinazolin‐4(3H)‐one derivatives from Schiff base intermediates GA3A and GA3B. The synthesis involved the reaction of 2‐(substituted phenyl)‐4H‐benzo[d][1,3]‐oxazin‐4‐one with sulphanilamide, and the benzoxazinone intermediates were prepared by reacting anthranilic acid with benzoyl chloride. The antibacterial activities of the Schiff base intermediates and the final Mannich base compounds were evaluated against Staphylococcus aureus, Bacillus cereus, Escherichia coli, and Pseudomonas aeruginosa at concentrations of 50 µg/mL and 100 µg/mL using the agar well diffusion method, with Norfloxacin (50 µg/mL) as the reference standard. While all tested compounds exhibited lower antibacterial activity compared to the standard, GA4A1 showed enhanced efficacy against E. coli, achieving the highest docking score of 78.0352 against the E. coli protein (PDB ID: 1KZN). Molecular dynamics simulations revealed that the GA4A1‐E. coli complex stabilized after 40,000 ps, with root mean square deviation (RMSD) values ranging from 2.5 Å to 5 Å and low root mean square fluctuation (RMSF) values between 0.05 Å and 0.2 Å, indicating the stability of the complex. These findings underscore GA4A1's potential as a potent antimicrobial agent against E. coli. [ABSTRACT FROM AUTHOR]

Developing new therapeutic strategies to target specific molecular pathways has become a primary focus in modern drug discovery science. Fibroblast growth factor receptor 2 (FGFR2) is a critical signaling protein involved in various cellular processes and implicated in numerous diseases, including cancer. Existing FGFR2 inhibitors face limitations like drug resistance and specificity issues. In this study, we present an integrated structure-based bioinformatics analysis to explore the potential of FGFR2 inhibitors-like compounds from the PubChem database with the Tanimoto threshold of 80%. We conducted a structure-based virtual screening approach on a dataset comprising 2336 compounds sourced from the PubChem database. Primarily, the selection of promising compounds was based on several criteria, such as drug-likeness, binding affinities, docking scores, and selectivity. Further, we conducted all-atom molecular dynamics (MD) simulations for 200 ns, followed by an essential dynamics analysis. Finally, a promising FGFR2 inhibitor with PubChem CID:507883 (1-[7-(1H-benzimidazol-2-yl)-4-fluoro-1H-indol-3-yl]-2-(4-benzoylpiperazin-1-yl)ethane-1,2-dione) was screened out from the study. This compound indicates a higher potential for inhibiting FGFR2 than the control inhibitor, Zoligratinib. The identified compound, CID:507883 shows >80% structural similarity with Zoligratinib. ADMET analysis showed promising pharmacokinetic potential of the screened compound. Overall, the findings indicate that the compound CID:507883 may have promising potential to serve as a lead candidate against FGFR2 and could be further exploited in therapeutic development. [ABSTRACT FROM AUTHOR]

Summary G6PD deficiency results from mutations in the X‐linked G6PD gene. More than 200 variants are associated with enzyme deficiency: each one of them may either cause predisposition to haemolytic anaemia triggered by exogenous agents (class B variants), or may cause a chronic haemolytic disorder (class A variants). Genotype–phenotype correlations are subtle. We report a rare G6PD variant, discovered in a baby presenting with severe jaundice and haemolytic anaemia since birth: the mutation of this class A variant was found to be p.(Arg454Pro). Two variants affecting the same codon were already known: G6PD Union, p.(Arg454Cys), and G6PD Andalus, p.(Arg454His). Both these class B variants and our class A variant exhibit severe G6PD deficiency. By molecular dynamics simulations, we performed a comparative analysis of the three mutants and of the wild‐type G6PD. We found that the tetrameric structure of the enzyme is not perturbed in any of the variants; instead, loss of the positively charged Arg residue causes marked variant‐specific rearrangement of hydrogen bonds, and it influences interactions with the substrates G6P and NADP. These findings explain severe deficiency of enzyme activity and may account for p.(Arg454Pro) expressing a more severe clinical phenotype. [ABSTRACT FROM AUTHOR]

Radiopharmaceuticals are currently a key tool in cancer diagnosis and therapy. Metal-based radiopharmaceuticals are characterized by a radiometal–chelator moiety linked to a bio-vector that binds the biological target (e.g., a protein overexpressed in a particular tumor). The right match between radiometal and chelator influences the stability of the complex and the drug's efficacy. Therefore, the coupling of the radioactive element to the correct chelator requires consideration of several features of the radiometal, such as its oxidation state, ionic radius, and coordination geometry. In this work, we systematically investigated about 120 radiometal–chelator complexes taken from the Cambridge Structural Database. We considered 25 radiometals and about 30 chelators, featuring both cyclic and acyclic geometries. We used quantum mechanics methods at the density functional theoretical level to generate the general AMBER force field parameters and to perform 1 µs-long all-atom molecular dynamics simulations in explicit water solution. From these calculations, we extracted several key molecular descriptors accounting for both electronic- and dynamical-based properties. The whole workflow was carefully validated, and selected test-cases were investigated in detail. Molecular descriptors and force field parameters for the complexes considered in this study are made freely available, thus enabling their use in predictive models, molecular modelling, and molecular dynamics investigations of the interaction of compounds with macromolecular targets. Our work provides new insights in understanding the properties of radiometal–chelator complexes, with a direct impact for rational drug design of this important class of drugs. [ABSTRACT FROM AUTHOR]

This study provides a detailed picture of how a protein (lysozyme) complexes with a poly(acrylic acid) polyelectrolyte (PAA) in water at the atomic level using a combination of all-atom molecular dynamics simulations and experiments. The effect of PAA and temperature on the protein's structure is explored. The simulations reveal that a lysozyme's structure is relatively stable except from local conformational changes induced by the presence of PAA and temperature increase. The effect of a specific thermal treatment on the complexation process is investigated, revealing both structural and energetic changes. Certain types of secondary structures (i.e., α-helix) are found to undergo a partially irreversible shift upon thermal treatment, which aligns qualitatively with experimental observations. This uncovers the origins of thermally induced aggregation of lysozyme with PAA and points to new PAA/lysozyme bonds that are formed and potentially enhance the stability in the complexes. As the temperature changes, distinct amino acids are found to exhibit the closest proximity to PAA, resulting into different PAA/lysozyme interactions; consequently, a different complexation pathway is followed. Energy calculations reveal the dominant role of electrostatic interactions. This detailed information can be useful for designing new biopolymer/protein materials and understanding protein function under immobilization of polyelectrolytes and upon mild denaturation processes. [ABSTRACT FROM AUTHOR]

Liquid-core nanocapsules (NCs) coated with amphiphilic hyaluronic acid (AmHA) have been proposed for the preparation of drug and food formulations. Herein, we focused on the use of ultrasound techniques to (i) optimize the polysaccharide chain length with respect to the properties of NCs stabilized with AmHAs and (ii) form oil-core nanocapsules with a coating composed of AmHAs. The results indicate that sonication is a convenient and effective method that allows for a controlled reduction in HA molecular weight. The initial (H-HA) and degraded (L-HA) polysaccharides were then reacted with dodecylamine to obtain hydrophobic HA derivatives (HA-C12s). Then, NCs were prepared based on HA-C12s using ultrasound-assisted emulsification of glyceryl triacetate oil. The nanocapsules coated with L-HA-C12 showed greater stability compared to the longer-chain polysaccharide. Molecular dynamics (MD) simulations revealed that HA-C12 readily adsorbs at the water–oil interphase, adopting a more compact conformation compared to that in the aqueous phase. The dodecyl groups are immersed in the oil droplet, while the main polysaccharide chain remaining in the aqueous phase forms hydrogen bonds or water bridges with the polar part of the triglycerides, thus increasing the stability of the NC. Our research underscores the usefulness of ultrasound technology in preparing suitable formulations of bioactive substances. [ABSTRACT FROM AUTHOR]

Biofilm formation by Staphylococcus aureus is a major challenge in clinical settings due to its role in persistent infections. The AgrA protein, a key regulator in biofilm development, is a promising target for therapeutic intervention. This study investigates the antibiofilm potential of halogenated phenazine compounds by targeting AgrA and explores their molecular interactions to provide insights for drug development. We employed molecular docking, molecular dynamics simulations, and computational mutagenesis to evaluate the binding of halogenated phenazine compounds (C1 to C7, HP, and HP‐14) to AgrA. Binding free energy analysis was performed to assess the affinity of these compounds for the AgrA‐DNA complex. Additionally, the impact of these compounds on AgrA's structural conformation and salt bridge interactions was examined. The binding‐free energy analysis revealed that all compounds enhance binding affinity compared to the Apo form of AgrA, which has a ΔGbind of −80.75 kcal/mol. The strongest binding affinities were observed with compounds C7 (−113.84 kcal/mol), HP‐14 (−115.23 kcal/mol), and HP (−112.28 kcal/mol), highlighting their effectiveness. Molecular dynamics simulations demonstrated that these compounds bind at the hydrophobic cleft of AgrA, disrupting essential salt bridge interactions between His174‐Glu163 and His174‐Glu226. This disruption led to structural conformational changes and reduced DNA binding affinity, aligning with experimental findings on biofilm inhibition. The halogenated phenazine compounds effectively inhibit biofilm formation by targeting AgrA, disrupting its DNA‐binding function. The study supports the potential of these compounds as antibiofilm agents and provides a foundation for rational drug design targeting the AgrA‐DNA interaction. Future research should focus on further optimizing these lead compounds and exploring additional active sites on AgrA to develop novel treatments for biofilm‐associated infections. [ABSTRACT FROM AUTHOR]

In order to understand the enhancement mechanism of the interaction between functionalised carbon nanotubes and polymer matrix, and to investigate the effects of different functionalised carbon nanotubes on the properties of composites at the microscopic level, the models of pure polyurethane (PUR), carbon nanotubes/polyurethane (CNTs/PUR), hydroxyl-functionalised carbon nanotubes/polyurethane (CNTOH/PUR), and carboxyl-functionalised carbon nanotubes/polyurethane (CNTCOOH/PUR) systems were constructed, and molecular dynamics methods were used to simulate the effects of different functionalised carbon nanotubes on the mechanical properties of polyurethane composite systems. The effects of different functionalised carbon nanotubes on the properties of PUR composite systems were compared by analysing the properties of PUR and its composite systems in terms of free volume fraction (FFV), radial distribution function (RDF), binding energy, and density distribution of PUR around different functionalised carbon nanotubes. The effects of intermolecular interactions on the properties of the composite systems were elucidated at the microscopic level. The final simulation results show that the CNTCOOH/PUR composite system with added carboxylated CNTs has the highest tensile yield strength, compressive properties and modulus; the smallest free volume fraction; the strongest bonding energy between CNTCOOH and PUR matrix, the largest density distribution of PUR matrix around CNTCOOH, and the best interfacial interaction. [ABSTRACT FROM AUTHOR]

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YTHDC1 (YTH domain containing 1), a crucial reader protein of N6-methyladenosine (m6A) mRNA, plays a critical role in various cellular functions and is considered a promising target for therapeutic intervention in acute myeloid leukemia and other cancers. In this study, we identified orthosteric small-molecule ligands for YTHDC1. Using a molecular docking approach, we screened the eMolecules database and recognized 15 top-ranked ligands. Subsequently, molecular dynamics simulations and MM/PBSA analysis were used to assess the stability and binding free energy of these potential hit compounds in complex with YTHDC1. Notably, five compounds with IDs of ZINC82121447, ZINC02170552, ZINC65274016, ZINC10763862, and ZINC02412146 exhibited high binding affinities and favorable binding free energies. The results also showed that these compounds formed strong hydrogen bonds with residues SER378, ASN363, and ASN367 and interacted with the aromatic cage of the YTHDC1 reader protein through TRP377, TRP428, and hydrophobic residue LEU439. To assess their viability as lead compounds, we conducted absorption, distribution, metabolism, excretion, and toxicity (ADMET) studies to reveal promising features for these identified small molecules, shedding light on their pharmacokinetic and safety profiles. [ABSTRACT FROM AUTHOR]

The delineation of protein–lipid interfaces is essential for understanding the mechanisms of various membrane-associated processes crucial to plant development and growth, including signalling, trafficking, and membrane transport. Due to their highly dynamic nature, the precise characterization of lipid–protein interactions by experimental techniques is challenging. Molecular dynamics simulations provide a powerful computational alternative with a spatial–temporal resolution allowing the atomistic-level description. In this review, we aim to introduce plant scientists to molecular dynamics simulations. We describe different steps of performing molecular dynamics simulations and provide a broad survey of molecular dynamics studies investigating plant protein–lipid interfaces. Our aim is also to illustrate that combining molecular dynamics simulations with artificial intelligence-based protein structure determination opens up unprecedented possibilities for future investigations of dynamic plant protein–lipid interfaces. [ABSTRACT FROM AUTHOR]

In the present investigation, a novel thermophilic L-asparaginase (Asn_PA) from Pseudomonas aeruginosa CSPS4 was investigated to explore its structural insights at elevated temperatures. Sequence analysis of Asn_PA depicted three conserved motifs (VVILATGGTIAG, DGIVITHGTDTLEETAYFL, and, LRKQGVQIIRSSHVNAGGF), of them, two motifs exhibit catalytically-important residues i.e., T45 and T125. A homology modelling-based structure model for Asn_PA was generated with 4PGA as the top-matched template. The predicted structure was validated and energy was minimized. Molecular docking was carried out cantered at the active site for asparagine and glutamine as its substrate ligands. The enzyme–substrate interaction analysis showed binding affinities of – 4.8 and – 4.1 kcal/mol for asparagine and glutamine respectively. Molecular dynamics (MD) simulation studies showed a better stability of Asn_PA at temperatures of 60 °C, over 40, 50 and, 80 °C, making this enzyme a novel L-asparaginase from other mesophilic P. aeruginosa strain. The trajectory analysis showed that RMSD, Rg, and, SASA values correlate well with each other in the different tested temperatures during the MD analysis. Thus, the present findings encourage extensive characterization of the Asn_PA using laboratory experiments to understand the structural behavior of the active site loop in an open or closed state with and without the substrate molecules. [ABSTRACT FROM AUTHOR]

The inward rectifier potassium channel Kir2.1 (KCNJ2) is an important regulator of resting membrane potential in both excitable and non-excitable cells. The functions of Kir2.1 channels are dependent on their lipid environment, including the availability of PI(4,5)P2, secondary anionic lipids, cholesterol and long-chain fatty acids acyl coenzyme A (LC-CoA). Endocannabinoids are a class of lipids that are naturally expressed in a variety of cells, including cardiac, neuronal, and immune cells. While these lipids are identified as ligands for cannabinoid receptors there is a growing body of evidence that they can directly regulate the function of numerous ion channels independently of CBRs. Here we examine the effects of a panel of endocannabinoids on Kir2.1 function and demonstrate that a subset of endocannabinoids can alter Kir2.1 conductance to varying degrees independently of CBRs. Using computational and Surface plasmon resonance analysis, endocannabinoid regulation of Kir2.1 channels appears to be the result of altered membrane properties, rather than through direct protein-lipid interactions. Furthermore, differences in endocannabinoid effects on Kir4.1 and Kir7.1 channels, indicating that endocannabinoid regulation is not conserved among Kir family members. These findings may have broader implications on the function of cardiac, neuronal and/or immune cells. [ABSTRACT FROM AUTHOR]

The exchange of carbon dioxide with methane in natural gas hydrates (NGHs) is one of the sustainable approaches for the sequestration of carbon dioxide in NGHs. However, the formation of mixed CH4─CO2 hydrates during CH4─CO2 exchange in NGHs reduces the rate of CH4─CO2 exchange in NGHs. It is reported that molecular level insights into CH4─CO2 exchange in NGHs using quaternary‐gas systems of CH4, CO2, and a mixture of flue (H2S and N2) and noble (Ne, Ar, Kr, and Xe) gases in heterogeneous medium using molecular dynamics simulation techniques. The sequestration of gases other than CH4 in the new hydrate cages besides the interface is the highest in CO2:H2S:Ar (2:1:1) system among all the reported quaternary‐gas systems. The results show that Ar enhances CO2 sequestration in NGHs in the presence of H2S rather than N2. The hydrate growth occurs due to the formation of dual hydrate cages. Among the methane molecules released from the hydrate slab in a binary‐gas (CH4─CO2) system, > 60 % of the released methane molecules reform new cages beside the interface. On the other hand, only ≈ 50 % of the released methane molecules reform new hydrate cages besides the interface in CO2:H2S:Ar (2:1:1) system. [ABSTRACT FROM AUTHOR]

The evolution of CO2 nanobubbles generated by gas–hydrate dissociation is comprehensively studied in this research, employing a synergistic approach that combines laboratory experiments and molecular dynamics simulations. The results show that a higher concentration of nanobubbles can be observed in the early stages of hydrate dissociation, while smaller, thus‐generated, nanobubbles are less stable and prefer to amalgamate into larger bubbles through coalescence or Ostwald ripening. From the high Laplace pressure inside some nanobubbles as well as their higher local densities, they may transform into nanodroplets by densification fluctuations. Thus, the dynamic coexistence of nanobubbles and ‐droplets is confirmed from both experimental and simulation measurements. The number and size of the nanobubbles in the system affects the interaction between water molecules and their movements so that the water molecules diffuse faster upon this condition. The water–water interactions become more pronounced in the presence of nanobubbles and the hydrogen bond network is better preserved in the bulk. This study provides new insights into the microscale mechanisms of gas–hydrate dissociation and highlights the complex interactions between nanobubbles/ ‐droplets, and the aqueous environment after CO2–hydrate dissociation. [ABSTRACT FROM AUTHOR]