Mean Square Displacement Research Articles

Quantum transport under oscillatory drive with disordered amplitude.

We investigate the dynamics of non-interacting particles in a one-dimensional tight-binding chain in the presence of an electric field with random amplitude drawn from a Gaussian distribution, and explicitly focus on the nature of quantum transport. We derive an exact expression for the probability propagator and the mean-squared displacement in the clean limit and generalize it for the disordered case using the Liouville operator method. Our analysis reveals that in the presence a random static field, the system follows diffusive transport; however, an increase in the field strength causes a suppression in the transport and thus asymptotically leads towards localization. We further extend the analysis for a time-dependent disordered electric field and show that the dynamics of mean-squared-displacement deviates from the parabolic path as the field strength increases, unlike the clean limit where ballistic transport occurs.

Mechanism and Characterization of Bicomponent-Filler-Reinforced Natural Rubber Latex Composites: Experiment and Molecular Dynamics (MD)

The incorporation of reinforcing fillers into natural rubber latex (NR) to achieve superior elasticity and mechanical properties has been widely applied across various fields. However, the tendency of reinforcing fillers to agglomerate within NR limits their potential applications. In this study, multi-walled carbon nanotube (MWCNT)–silica (SiO2)/NR composites were prepared using a solution blending method, aiming to enhance the performance of NR composites through the synergistic effects of dual-component fillers. The mechanical properties, dispersion behavior, and Payne effect of three types of composites—SiO2/NR (SNR), MWCNT/NR (MNR), and MWCNT-SiO2/NR (MSNR)—were investigated. In addition, the mean square displacement (MSD), fractional free volume (FFV), and binding energy of the three composites were simulated using molecular dynamics (MD) models. The results showed that the addition of a two-component filler increased the tensile strength, elongation at break, and Young’s modulus of NR composites by 56.4%, 72.41%, and 34.44%, respectively. The Payne effect of MSNR was reduced by 4.5% compared to MNR and SNR. In addition, the MD simulation results showed that the MSD and FFV of MSNR were reduced by 21% and 17.44%, respectively, and the binding energy was increased by 69 times, which was in agreement with the experimental results. The underlying mechanisms between the dual-component fillers were elucidated through dynamic mechanical analysis (DMA), a rubber process analyzer (RPA), and field emission scanning electron microscopy (SEM). This study provides an effective reference for broadening the application fields of NR.

Tower cranes mixed H2/H∞ under wind load research on active vibration control

The offshore platform tower crane is affected by sea breeze, which causes severe vibration, which causes damage to the crane structure and reduces the working efficiency and performance of the crane. Therefore, the vibration suppression under the complex environment of sea breeze has become a key problem to be solved in practical engineering. Firstly, a pulsating wind excitation model with wind speed changing at any time is established to simulate the time sequence of downwind and crosswise wind loads, which is used to reflect the actual wind speed fluctuations. Secondly, the pulsating wind model is applied to the crane finite element model, and the structural vibration characteristics are explored by analyzing the dynamics of the tower crane under wind excitation，the dynamic model of tower crane system under pulsating wind excitation is established, and the system state equation is constructed. Considering the vibration displacement performance of tower crane structure, a hybrid [Formula: see text] optimal performance vibration active control strategy is proposed. Finally, simulation experiments are carried out. The results show that, compared with the AA controller under the same conditions, the root mean square of displacement under downwind excitation decreases by 65.77%, 64.31% and 63.13%, respectively, and the root mean square of displacement under transverse wind excitation decreases by 62.8%, 65.51% and 62.6%, respectively. The results show that the proposed control method can effectively suppress tower crane vibration.

Cordycepin and Its Structural Derivatives Effectively Suppress the High Expression of Epidermal Growth Factor Receptor (EGFR) Tyrosine Kinase in Breast Carcinomas: A Computational Drug Development Approach.

Breast cancer is a frequently diagnosed malignant disease and the primary cause of mortality among women with cancer worldwide. The therapy options are influenced by the molecular subtype due to the intricate nature of the condition, which consists of various subtypes. By focusing on the activation of receptors, Epidermal Growth Factor Receptor (EGFR) tyrosine kinase can be utilized as an effective drug target for therapeutic purposes of breast cancer. The objective of this study is to compare the underlying pharmacological properties of several modified agents to the parental Cordycepin to target and inhibit the EGFR tyrosine kinase high expression, and to discover the inhibitor with the highest affinity for this drug target to treat the breast cancer patients. The Maestro Application of Schrödinger Suite Paid Software was initially employed for conducting extra precision (XP) structure-based virtual screening to evaluate the binding affinity of the Cordycepin and its 500 structural derivatives with the EGFR tyrosine kinase protein structure. In addition, the anti-breast cancer activity of the chosen compounds was assessed by looking at their drug-likeness and ADMET characteristics using Lipinski's rule of five along with Quantitative structure- activity relationship (QSAR) validation, the prediction of cell line anti-cancer, as well as anti- breast cancer activity of top docked scored compounds. Subsequently, the Desmond paid software- based molecular dynamics simulations (MDS) were conducted for a duration of 100 nanoseconds on the promising candidates followed by the binding free energy estimation was performed utilizing MM-GBSA analysis. To determine the stability of the protein-ligand complex, root-meansquare deviation (RMSD), root-mean-square fluctuation (RMSF), protein-ligand interactions, and other necessary parameters were evaluated from the 100 ns MDS Trajectory. Based on the overall analysis of our study, N (6)-octylamine adenosine (CID-194932) reported the optimum inhibitory potential against the EGFR tyrosine kinase protein, followed by Adenosine 5-monophosphate (CID-83862) and Cordycepin (CID-6303), which compared favorably to the control drug Vandetanib (CID-3081361). Consequently, these derivative compounds Cordycepin have the potential to be utilized as lead molecules in the development of highly effective and potent EGFR tyrosine kinase inhibitors for the treatment of breast cancer patients.

Integrating machine learning and structure-based approaches for repurposing potent tyrosine protein kinase Src inhibitors to treat inflammatory disorders

Tyrosine-protein kinase Src plays a key role in cell proliferation and growth under favorable conditions, but its overexpression and genetic mutations can lead to the progression of various inflammatory diseases. Due to the specificity and selectivity problems of previously discovered inhibitors like dasatinib and bosutinib, we employed an integrated machine learning and structure-based drug repurposing strategy to find novel, targeted, and non-toxic Src kinase inhibitors. Different machine learning models including random forest (RF), k-nearest neighbors (K-NN), decision tree, and support vector machine (SVM), were trained using already available bioactivity data of Src kinase targeting compounds. The performance evaluation of these models demonstrated SVM as the best model, which was further utilized to shortlist 51 highly potent compounds by screening an FDA-approved library of 1040 drugs. Molecular docking and molecular dynamic simulation were subsequently employed to evaluate the binding affinity and stability of the proposed compounds. Orlistat, acarbose and afatinib were identified as the potent leads, demonstrating stable conformations and stronger interactions, validated by root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (RoG), and hydrogen bond analyses. Molecular Mechanics/Generalized Born Surface Area (MMGBSA) analysis validated their binding affinities by providing comparably lower binding free energies for orlistat (− 33.4743 ± 3.8908), acarbose (− 19.5455 ± 5.4702), and afatinib (− 36.4944 ± 5.4929) than the control, dasatinib (− 13.7785 ± 5.8058). Finally, toxicity analysis revealed orlistat and acarbose as the possible safer therapeutics by eliminating afatinib as it showed significant toxicity concerns. Our investigation supports the advance computational methods utilization in the field of drug discovery and suggest further experimental validation of proposed inhibitors of Src kinase for their safer use against inflammatory diseases. The ultimate aim of this study is to advance the development of effective treatments for inflammatory diseases, linked with Src overexpression.

Advancing siRNA Therapeutics targeting MCT-4: A Multifaceted approach integrating Arithmetical Designing, Screening, and molecular dynamics validation.

Monocarboxylate transporter 4 (MCT-4) is involved in various metabolic processes which are crucial in maintaining cellular pH and energy metabolism, and thus influence the tumor microenvironment. The study is aimed to rationally design effective Small interfering RNA (siRNA) that can silence MCT-4. We utilized a comprehensive workflow integrating multiple tools such as siDirect version 2.0, Oligowalk and i-score designer, to evaluate sequence features and predict target site accessibility, Guanine-Cytosine (GC) content and thermodynamic stability. Five (M1, M2, M3, M4 and M5) siRNAs sequences were retrived and subjected to further scrutiny on the account of off-target elimation, sequence conservation, secondary structure formation, and thermodynamic properties. The M1 demonstrated off targets and the M2 sequence showed secondry conformation and therefore M3, M4 and M5 were considered for further evaluation. Additionally, molecular docking and simulations (50ns) were conducted with human Argonaute 2 protein (h-Arg-2). The post- molecular dynamics (MD) analysis revealed M4 (5'UUGAAGAAGACACUGACGG3') as a most appropriate siRNA candidate agsint MCT-4 on the basis of Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), and H-Bond results. The Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) analysis was also performed to further validate the selected siRNA candidates, which further affirmed M4 (5'UUGAAGAAGACACUGACGG3') as an potential candidate for future in-vitro and in-vivo evaluation.

Exploring the Thermodynamics and Dynamics of CO2 Using Rigid Models

Understanding the behavior of carbon dioxide (CO2) under varying thermodynamic conditions is essential for optimizing processes such as Carbon Capture and Storage (CCS) and supercritical fluid extraction. This study employs molecular dynamics (MD) simulations with the EPM2 and TraPPE-small force fields to examine CO2 phase behavior, structural characteristics, and transport properties across a temperature range of 228–500 K and pressures from 1 to 150 atm. Our findings indicate a good agreement between simulated and experimental liquid–vapor coexistence curves, validating the capability of both force fields to model CO2 accurately in a wide range of thermodynamical conditions. Radial distribution functions (RDFs) reveal distinct interaction patterns in liquid and supercritical phases, while mean squared displacement (MSD) analyses show diffusivity increasing from 5.2×10−9 m2/s at 300 K to 1.8×10−8 m2/s at 500 K. Additionally, response functions such as the heat capacity effectively capture phase transitions. These findings provide quantitative insights into CO2 phase behavior and transport properties, enhancing the predictive reliability of simulations for CCS and related industrial technologies. This work bridges gaps in the CO2 modeling literature and highlights the potential of MD simulations in advancing sustainable applications.

Probing Pregnane‐3,20‐Dione, 17,21‐[(Methylborylene)Bis(Oxy)]‐, (5. Beta)‐ as an Antiviral Candidate Against White Spot Syndrome Virus (WSSV): Molecular Docking and Simulation Approach

White spot syndrome virus (WSSV) is a highly infectious virus that poses an imminent threat to global shrimp aquaculture and is responsible for significant mortality in shrimp farms. There is a lack of targeted drug therapy options for preventing or treating WSSV. Consequently, these envelope proteins have garnered attention as an alternative focus for drug development at the molecular level. In the current study, the binding efficiency of pregnane‐3,20‐dione, 17,21‐[(methylborylene)bis(oxy)]‐, (5. beta)‐, a compound identified from Turbianria ornata, was predicted. The target proteins were major envelope proteins VP28, VP24, and VP110 of WSSV. The ligand structure was generated using PubChem and the SMILES platform. Docking was performed using AutoDock 4.2, employing blind docking to identify the preferred binding sites autonomously. A 100‐nanosecond molecular dynamics (MD) simulation was conducted for each protein–ligand complex using Desmond software to evaluate the stability and dynamics of these interactions. The complexes exhibited minimum values for both docking scores and binding energies. The additional data on root mean square deviation (RMSD) and root mean square fluctuation (RMSF) further supported the stability of proteins and ligands. The compound’s strong and stable binding with multiple WSSV envelope proteins suggests its potential as a broad‐spectrum antiviral agent against WSSV. The study found that the compound pregnane‐3,20‐dione, 17,21‐[(methylborylene)bis(oxy)]‐, (5. beta)‐ demonstrated high binding affinity Turbinaria ornata and stability with the viral envelope proteins VP28, VP24, and VP110, indicating strong antiviral potential against WSSV.

Synthesis, Antileishmanial Activity, and in silico Study of 2-Hydroxy-3-(1,2,3-triazolyl)propyl Vanillin Derivatives

This study details the preparation, antileishmanial assay, and in silico analysis of twenty 2-hydroxy-3-(1,2,3-triazolyl)propyl vanillin derivatives. These compounds were synthesized in three steps and evaluated against Leishmania infantum, Leishmania amazonensis, and Leishmania braziliensis promastigotes. Compounds 3s and 3t were the most effective, showing good activity against all Leishmania species tested. Molecular docking indicated that all compounds bind favorably to the sterol 14α-demethylase (CYP51) enzyme from L. infantum. ADMET (absorption, distribution, metabolism, excretion and toxicity) analysis indicated good oral bioavailability, non-blood-brain barrier penetration, and high gastrointestinal absorption. Posaconazole and compounds 3e, 3s, and 3t remained stable in the CYP51 binding region during 100 ns molecular dynamics (MD) simulations. Root mean square deviation (RMSD) and root mean square fluctuations (RMSF) analyses from the MD trajectory revealed significant conformational fluctuations of the CYP51 N-terminal, suggesting occasional expulsion of 3e, potentially explaining its higher IC50 (half-maximal inhibitory concentration) values. Pairwise decomposition analyses from molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) calculations highlighted the importance of hydrophobic residues in interacting with the synthesized derivatives.

Molecular Docking and Dynamics Reveal the Potential of Limonene and β‐Linalool as Natural Fungicides against Mango Anthracnose Pathogen

AbstractThe growing demand for sustainable mango production has driven the search for eco‐friendly alternatives to synthetic chemicals, leading to the exploration of plant‐based biofungicides. This study aims to identify and evaluate these biofungicides, focusing on their in‐silico toxicity and docking properties against the cutinase protein of Colletotrichum gloeosporioides, the pathogen responsible for mango anthracnose. Using computational methods, the potential toxicity of 14 phytochemical compounds was assessed to ensure their safety for nontarget organisms and the environment. Molecular docking simulations were conducted to examine the interaction between the compounds and the cutinase enzyme, a critical factor in the pathogenicity of C. gloeosporioides. The results highlight the potential of certain phytochemicals, particularly limonene and β‐linalool as biofungicides, while also identifying areas for improvement in terms of toxicity and environmental impact. Molecular dynamics simulations were also conducted to examine the behavior of the protein–ligand complexes over time, revealing several key insights. The radius of gyration (Rg) analysis showed a slight increase in Rg values for both limonene and β‐linalool, suggesting moderate destabilization of the protein structure and indicating their potential as inhibitors. Additionally, the root mean square deviation (RMSD) and root mean square fluctuation (RMSF) analyses confirmed that while the compounds led to slight fluctuations in the protein structure, the overall stability, and integrity of the protein were maintained. The binding free energy calculations supported the hypothesis of these compounds acting as effective inhibitors of cutinase, potentially offering an eco‐friendly solution for controlling mango anthracnose.

Dynamics of nanoparticle tracers in supercooled nanoparticle matrices.

We investigate the dynamics of tracer nanoparticles in bulk supercooled nanoparticle matrices using confocal microscopy. We mix fluorescent (tracer) and undyed (matrix) charged-stabilized polystyrene nanoparticles with tracer-to-matrix particle size ratios δ = 0.34, 0.36, 0.45, 0.71 at various matrix volume fractions ϕ. Single-particle and collective dynamics were obtained from particle-tracking algorithms and differential dynamic microscopy (DDM), respectively. The long-time behavior of the tracer mean-square displacement (MSD) and the shape of the distributions of particle displacements depend on δ and ϕ. At sufficiently large ϕ, small tracers (δ ≤ 0.36) remain mobile and subdiffusive but large tracers (δ ≥ 0.45) are dynamically arrested. The relaxation times determined from the intermediate scattering function (ISF) increase with δ and ϕ. Anomalous logarithmic decays in the ISF are observed for tracers of size δ ≤ 0.36 over a length scale of four to ten matrix particle diameters. These results provide insight into how penetrant size affects the transport of nanoparticles in porous media with soft interparticle interactions.

Accurate Estimation of Diffusion Coefficients and their Uncertainties from Computer Simulation.

Self-diffusion coefficients, D*, are routinely estimated from molecular dynamics simulations by fitting a linear model to the observed mean squared displacements (MSDs) of mobile species. MSDs derived from simulations exhibit statistical noise that causes uncertainty in the resulting estimate of D*. An optimal scheme for estimating D* minimizes this uncertainty, i.e., it will have high statistical efficiency, and also gives an accurate estimate of the uncertainty itself. We present a scheme for estimating D* from a single simulation trajectory with a high statistical efficiency and accurately estimating the uncertainty in the predicted value. The statistical distribution of MSDs observable from a given simulation is modeled as a multivariate normal distribution using an analytical covariance matrix for an equivalent system of freely diffusing particles, which we parametrize from the available simulation data. We use Bayesian regression to sample the distribution of linear models that are compatible with this multivariate normal distribution to obtain a statistically efficient estimate of D* and an accurate estimate of the associated statistical uncertainty.

Molecular Characterization and Potential Inhibitors Prediction of Protein Arginine Methyltransferase-2 in Carcinoma: An Insight from Molecular Docking, ADMET Profiling and Molecular Dynamics Simulation Studies.

To predict and characterize the three-dimensional (3D) structure of protein arginine methyltransferase 2 (PRMT2) using homology modeling, besides, the identification of potent inhibitors for enhanced comprehension of the biological function of this protein arginine methyltransferase (PRMT) family protein in carcinogenesis. An in silico method was employed to predict and characterize the three-dimensional structure. The bulk of PRMTs in the PDB shares just a structurally conserved catalytic core domain. Consequently, it was determined that ligand compounds may be the source of co-crystallized complexes containing additional PRMTs. Possible PRMT2 inhibitor compounds are found by using S-adenosyl methionine (SAM), a methyl group donor, as a positive control. Protein arginine methyltransferases are associated with a range of physiological processes, including as splicing, proliferation, regulation of the cell cycle, differentiation, and signaling of DNA damage. These functional capacities are also related to carcinogenesis and metastasis-several forms of PRMT have been cited in the literature. These include PRMT-1, PRMT-2, and PRMT-5. Among these, the role of PRMT-2 has been shown in breast cancer and hepatocellular carcinoma. To gain more insights into the role of PRMT2 in cancer pathogenesis, we opted to characterize tertiary structure utilizing an in silico approach. The majority of PRMTs in the PDB have a structurally conserved catalytic core domain. Thus, ligand compounds were identified as a possible source of co-crystallized complexes of other PRMTs. The SAM, a methyl group donor, is used as a positive control in order to identify potential inhibitor compounds of PRMT2 by the virtual screening method. We hypothesized that an inhibitor for other PRMTs could alter PRMT2 activities. Out of 45 inhibitor compounds, we ultimately identified three potential inhibitor compounds based on the results of the pharmacokinetics and binding affinity studies. These compounds are identified as 3BQ (PubChem CID: 77620540), 6DX (PubChem CID: 124222721), and TDU (PubChem CID: 53346504). Their binding affinities are -8.5 kcal/mol, -8.1 kcal/mol, and -8.8 kcal/mol, respectively. These compounds will be further investigated to determine the binding stability and compactness using molecular dynamics simulations on a 100 ns time scale. In vitro and in vivo studies may be conducted with these three compounds, and we think that focusing on them might lead to the creation of a PRMT2 inhibitor. Three strong inhibitory compounds that were non-carcinogenic also have drug-like properties. By using desirable parameters in root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), solvent accessible surface area (SASA), molecular surface area (MolSA), and intermolecular hydrogen bonding, complexes verified structural stability and compactness over the 100 ns time frame. Hossen MS, Islam MN, Pramanik MEA et al. Molecular Characterization and Potential Inhibitors Prediction of Protein Arginine Methyltransferase-2 (PRMT2) in Carcinoma: An Insight from Molecular Docking, ADMET Profiling and Molecular Dynamics Simulation Studies. Euroasian J Hepato-Gastroenterol 2024;14(2):160-171.

Generalized Langevin equation for a tagged monomer in a Gaussian semiflexible polymer.

In this study, we present a comprehensive analysis of the motion of a tagged monomer within a Gaussian semiflexible polymer model. We carefully derived the generalized Langevin equation (GLE) that governs the motion of a tagged central monomer. This derivation involves integrating out all the other degrees of freedom within the polymer chain, thereby yielding an effective description of the viscoelastic motion of the tagged monomer. A critical component of our analysis is the memory kernel that appears in the GLE. By examining this kernel, we characterized the impact of bending rigidity on the non-Markovian diffusion dynamics of the tagged monomer. Furthermore, we calculated the mean-squared displacement of the tagged monomer using the derived GLE. Our theoretical findings were corroborated by the Langevin dynamics simulation and scaling theory. Our results not only show remarkable agreement with previously known results in certain limiting cases but also provide dynamic features over the entire timescale.

Numerical Study of Neutral and Charged Microgel Suspensions: From Single-Particle to Collective Behavior

We perform extensive molecular dynamics simulations of an ensemble of realistic microgel particles in swollen conditions in a wide range of packing fractions ζ. We compare neutral and charged microgels, where we consider charge distribution adherent to experimental conditions. Through a detailed analysis of single-particle behavior, we are able to identify the different regimes occurring upon increasing concentration: from shrinking to deformation and interpenetration, always connecting our findings with available experimental observations. We then link these single-particle features with the collective behavior of the suspension, finding evidence of a structural reentrance that has no counterpart in the dynamics. Hence, while the maximum of the radial distribution function displays a nonmonotonic behavior with increasing ζ, the dynamics, quantified by the microgels’ mean-squared displacement, always slows down. This behavior, at odds with the simple Hertzian model, can be described by a phenomenological multi-Hertzian model, which takes into account the enhanced internal stiffness of the core. However, also this model fails when deformation enters into play, whereby more realistic many-body models are required. Thanks to our analysis, we are able to unveil the key physical mechanisms, shrinking and deformation, giving rise to the structural reentrance that holds up to very large packing fractions. We further identify key similarities and differences between neutral and charged microgels, for which we detect at high enough ζ the fusion of charged shells, previously invoked to explain key experimental findings, and responsible for the structural reentrance. Overall, our study establishes a powerful framework to uncover the physics of microgel suspensions, paving the way to tackle different regimes, e.g., high temperature, and internal architectures, such as for hollow and ultralow-cross-linked microgels, where experimental evidence is still limited. Published by the American Physical Society 2024

The Effects of Lamium garganicum L. Subsp. lasioclades (Stapf.) R. Mill Plant Against Fibroblast (U2OS Cell), Acetylcholinesterase, Glutathione S-Transferase: An In Vitro, In Silico Biological Activity Screening Study.

In this study, some biological activities of extracts of Lamium garganicum subsp. lasioclades (Lgl) have been evaluated as well as identified the phenolic composition. Concentration ranges of 31.25, 62.5, 125, 250, and 500µg/mL were applied to determine the extract's anticancer properties. Significant results were obtained against the osteosarcoma cell line (U2OS cell) compared to normal human umbilical vein endothelial cells (HUVEC). To determine the antioxidant activities, ABTS, DPPH, FRAP, and CUPRAC methods were studied in vitro. Enzyme inhibition effects of methanol extract against the glutathione S-transferase (GST) and acetylcholinesterase (AChE) enzymes were investigated. IC50 values were calculated as 12.96µL/mL for AChE and 13.02µg/mL for GST, respectively. The phenolic contents of the plant extract were analyzed by HPLC. The interaction mechanisms of protein-ligand complexes formed by AChE and GST receptors with gallic acid and rutin were investigated by molecular docking studies. The stability of the complexes formed between receptors and ligands was confirmed by root mean square deviation (RMSD), root mean square fluctuation (RMSF), number of average hydrogen bonding interactions (Hb), and radius of gyration (Rg) analyses obtained from 100ns molecular dynamics simulations.

Random walks of intermittently self-propelled particles

Motivated by various recent experimental findings, we propose a dynamical model of intermittently self-propelled particles: active particles that recurrently switch between two modes of motion, namely an active run state and a turn state, in which self-propulsion is absent. The durations of these motility modes are drawn from arbitrary waiting-time distributions. We derive the expressions for exact forms of transport characteristics like mean-square displacements and diffusion coefficients to describe such processes. Furthermore, the conditions for the emergence of sub- and superdiffusion in the long-time limit are presented. We give examples of some important processes that occur as limiting cases of our system, including run-and-tumble motion of bacteria, Lévy walks, hop-and-trap dynamics, intermittent diffusion and continuous-time random walks. Published by the American Physical Society 2024

Understanding Acute Hemolytic Anemia Severity Through Computational Analysis of G6PDChatham Variant: Designing a New Activator as a Potential Drug

Glucose-6-phosphate dehydrogenase deficiency (G6PDD) is a major enzymatic disease affecting human red blood cells (RBCs), causing hemolytic anemia due to the diminish of the nicotinamide adenine dinucleotide phosphate hydrogen (NADPH) synthesis and altered redox balance within erythrocytes. This study sought to correct the defect in G6PDChatham (Ala355Thr) caused by the loss of interactions (hydrogen bonds and salt bridges) by docking the AG1 molecule at the dimer interface, thus restoring these lost interactions. The enzyme conformation was then analyzed before and after AG1 binding using molecular dynamics simulation (MDS). The reasons behind the severity of acute hemolytic anemia (AHA) were explained using several parameters, such as root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), hydrogen bonds, salt bridges, radius of gyration (Rg), solvent accessible surface area (SASA), and covariance matrix analysis. Structural alterations in G6PDChatham, including the absence of interactions in a key region of the variant structure, can significantly impact protein stability and function, subsequently contributing to disease severity. Upon AG1 binding, these missing interactions were resorted to correct the structural defect of the variant. This restoration improves dimer stability and restores G6PD function. To develop new G6PD activators, several new analogues (SY7, SY8, SY9, and SY10) were rationally developed by substituting the linker region of the AG1 structure with other functional groups using the Avogadro software. These compounds were successfully synthesized and docked with G6PDChatham where the best binding affinity ranged between -8.0 and -9.1 kcal/mol. SY8, a promising G6PD activator, is predicted to be easily metabolized and excreted, making it less likely to cause toxicity. Its high drug score, drug-likeness, and favorable safety profile make it a strong candidate for synthesis and cellular testing. The toxicity risk assessment supported the overall drug score, increasing confidence in finding additional small-molecule activators for G6PDD disorder. Amidst the absence of effective treatments, such discovery hopes to improve the lives of those with AHA by assisting the development of appropriate pharmaceuticals for G6PDD.

Modeling memory function of entangled photon and classical laser photon-count fluctuations using white noise integral analysis

Abstract This study investigates photon-count fluctuation dynamics of two light sources, namely a spontaneous parametric down-conversion (SPDC) light source and a 780 nm attenuated laser diode (LD). White noise integral with customizable memory function is used to model the mean square displacements (MSDs) and the probability density functions (PDFs) for both light sources. This approach overcomes the limitation of monofractal scaling of fractional Brownian motion (fBm) model characterized by a single Hurst exponent. The memory function used has an exponential-tempered power-law relation, parametrized by μ and β, where β modulates the extent of memory parameter μ. Although optical losses and detector inefficiencies degrade photon statistics to Poissonian at post-detection, our findings reveal notable memory effects at higher mean photon counts, especially in the SPDC source with memory parameter μ ≧ 1.00, compared to the classical LD, which remained relatively constant at μ ≦ 1.00. Both light sources shared similar correction parameters β, which indicates they have identical photon-count fluctuations at short time but diverge significantly at longer time. This work highlights the need for models beyond fBm, capable of capturing complex MSD behaviors of photon-count fluctuations.

Helichrysum populifolium Compounds Inhibit MtrCDE Efflux Pump Transport Protein for the Potential Management of Gonorrhoea Infection.

The progressive development of resistance in Neisseria gonorrhoeae to almost all available antibiotics has made it crucial to develop novel approaches to tackling multi-drug resistance (MDR). One of the primary causes of antibiotic resistance is the over-expression of the MtrCDE efflux pump protein, making this protein a vital target for fighting against antimicrobial resistance (AMR) in N. gonorrhoeae. This study was aimed at evaluating the potential MtrCDE efflux pump inhibitors (EPIs) and their stability in treating gonorrhoea infection. This is significant because finding novel EPIs would allow for the longer maintenance of antibiotics at therapeutic levels, thereby prolonging the susceptibility of currently available antibiotics. A virtual screening of the selected Helichrysum populifolium compounds (4,5-dicaffeoylquinic acid, apigeninin-7-glucoside, and carvacrol) was conducted to evaluate their potential EPI activity. An integrated computational framework consisting of molecular docking (MD), molecular mechanics generalized born, and surface area solvation (MMGBSA) analysis, molecular dynamics simulations (MDS), and absorption, distribution, metabolism, and excretion (ADME) properties calculations were conducted. Of the tested compounds, 4,5-dicaffeoylquinic acid revealed the highest molecular docking binding energies (-8.8 kcal/mol), equivalent MMGBSA binding free energy (-54.82 kcal/mol), indicative of consistent binding affinity with the MtrD protein, reduced deviations and flexibility (root mean square deviation (RMSD) of 5.65 Å) and, given by root mean square fluctuation (RMSF) of 1.877 Å. Carvacrol revealed a docking score of -6.0 kcal/mol and a MMGBSA computed BFE of -16.69 kcal/mol, demonstrating the lowest binding affinity to the MtrD efflux pump compared to the remaining test compounds. However, the average RMSD (4.45 Å) and RMSF (1.638 Å) of carvacrol-bound MtrD protein showed no significant difference from the unbound MtrD protein, except for the reference compounds, implying consistent MtrD conformation throughout simulations and indicates a desirable feature during drug design. Additionally, carvacrol obeyed the Lipinski rule of five which confirmed the compound's drug-likeness properties making it the most promising EPI candidate based on its combined attributes of a reasonable binding affinity, sustained stability during MDS, its obedience to the Lipinski rule of five and compliance with drug-likeness criteria. An in vitro validation of the potential EPI activities of H. populifolium compounds confirmed that 4,5-dicaffeoylquinic acid reduced the expulsion of the bis-benzimide dye by MtrCDE pump, while carvacrol showed low accumulation compared to other compounds. While 4,5-dicaffeoylquinic acid demonstrated the highest binding affinity in computational analysis and an EPI activity in vitro, it showed lower stability compared to the other compounds, as indicated in MDS. This leaves carvacrol, as a better EPI candidate for the management of gonorrhoea infection.