Simulation Analyses Research Articles

Modelling on How Topcoat/Bond Coat Micro-Rough Interface and Nearby Voids Affect the Stress Distribution in Thermal Barrier Coating Systems in Quenching Process

The distribution of voids in ceramic topcoats (TC) and the micro-roughness of metallic bond coat (BC) interfaces are important for the structure design and coating life of thermal barrier coating (TBC) systems. In this study, finite elemental (FE) models were built by considering those two structural factors to investigate their influence on the stress distribution in TBCs in quenching processes under thermal shock conditions. According to the simulation analyses, the function of the voids in TCs includes the dilution effect of the stress concentration at the macro-scale, the releasing effect of the tensile stress along the vertical direction above the TC peak, and the “stress trapping” effect bringing higher stress at the horizontal tips of the voids on the micro-scale. The micro-roughness of the TC/BC interface did not have much effect on the stress values in the TC, aside from at the TC peak, but had a significant influence on the stress value along the interface due to the “stress trapping” effect. The TBC samples that were experimentally tested under water-cooling thermal shock conditions were also analyzed in this paper to verify the modelling results.

Predaceous and Phytophagous Pentatomidae Insects Exhibit Contrasting Susceptibilities to Imidacloprid

Imidacloprid, a widely used neonicotinoid insecticide, targets insect pests but also affects natural enemies. However, the effects of neonicotinoid insecticides on closely related insects remain unclear. We evaluated the harmful effects of imidacloprid on the phytophagous Halyomorpha halys and predaceous Arma chinensis. Bioassays revealed that imidacloprid was more toxic to H. halys than to A. chinensis and more harmful to the males than to the females of the two insects. A. chinensis adults recovered from imidacloprid-induced knockdown, as evidenced by restored respiratory rates, metabolic rates, and locomotion. Surviving A. chinensis showed reduced fecundity, suggesting a trade-off between detoxification and reproduction. Bioinformatics analysis of nicotinic acetylcholine receptors (nAChRs) and molecular docking simulations indicated a lower diversity of the nAChR gene family in A. chinensis than in H. halys, with weaker binding to imidacloprid, consistent with the relatively low toxicity of the insecticide in this species. This might account for the susceptibility differences to imidacloprid between the species. These findings underscore the efficacy of imidacloprid against H. halys and provide insights into the toxicities of neonicotinoids to target and non-target insects.

Analysis of No‐Load Normal Vibration in Single‐Sided Permanent Magnet Synchronous Linear Motors With Different End Structures

ABSTRACTThis paper investigates the no‐load normal vibrations of single‐sided permanent magnet synchronous linear motors (S‐PMSLMs) with different end structures, addressing a significant gap in the current literature. By employing the Maxwell stress tensor method, analyzes the harmonic orders and frequencies of the no‐load normal force density are analyzed. Comparative research between the comb‐type auxiliary tooth (CTAT) and conventional auxiliary tooth (C‐AT) structures indicates that while the CTAT marginally increases the amplitude of the no‐load normal force density, it significantly reduces harmonic amplitudes and vibration levels. The theoretical and simulation analyses are validated through no‐load vibration experiments, providing valuable insights for the design optimization of S‐PMSLMs, particularly beneficial for high‐speed transportation systems that require high precision and reliability.

Storm water runoff studies in built-up watershed areas using curve number and remote sensing techniques

Within major city confines, floods are a major cause of worry and are mainly due to excessive urbanization encroaching on natural landscapes which would otherwise have served as areas of infiltration. The study combines the growth of urban landscape to estimate the maximum surface runoff and aimed to quantify this runoff generated and peak discharge for better urban management practices. These past five decades, the area experienced erratic expansion along with various changes in its land classification, resulting in several flood events in various parts. Runoff estimation was made using Curve Number method for the watershed. Annual rainfall deviation from mean saw an increase by 16% on an average in the past decade, with more than a 100% deviation from mean in 2017. Topographical maps generated to study the changes contributing to flood situations show a 90% increase in concretization over the past two decades and more than 50% reduction in the amount of natural vegetative cover in that same time period. Statistical analysis shows a good fit of the selected model for runoff estimation and well correlated variables. The model satisfactorily predicted runoff from the simulated data analysis with evaluation criteria NSE = 0.9945, MAE = 5.4121, r = 0.9975, R2 = 0.9949, RMSE = 6.8117 and PBias = 1.1436. The results revealed a steady increase in yearly runoff, due to topographical changes and increase in precipitation intensity over time. The study suggests intervention efforts be targeted spatially to ensure suitable flood-control structures and systems.

Comparative analysis of PI and fuzzy logic controller for grid connected wind turbine under normal and fault conditions

This research is dedicated to improving the control system of wind turbines (WT) to ensure optimal efficiency and rapid responsiveness. To achieve this, the fuzzy logic control (FLC) method is implemented to control the converter in the rotor side (RSC) of a doubly fed induction generator (DFIG) and its performance is compared with an optimized proportional integral (PI) controller. The study demonstrated an enhancement in the performance of the DFIG through the utilization of the proposed FLC, effectively overcoming limitations and deficiencies observed in the conventional controllers, this approach significantly improved the performance of the wind turbine. Additionally, the selected membership functions were found to be highly compatible with the unique characteristics of wind energy. The optimization process is implemented for the controllers of both the grid side converter (GSC) and RSC. Through simulated analyses conducted using MATLAB/Simulink software, comprehensive assessments are carried out. The robustness of the FLC is evaluated compared to the optimized controllers across various wind profiles and challenging fault conditions. The results demonstrate satisfactory performance of the FLC in terms of steady-state time, stability, and precision under diverse wind speed profiles. The FLC achieves a significantly better settling time than the enhanced PI, improving by approximately 14–70% under normal conditions and 40–70% under various fault conditions. Additionally, the FLC outperforms the enhanced PI in fault conditions by reducing peak-to-peak oscillations by about 30–65%. It also delivers a smaller steady-state error, with improvements of around 2–4% under both normal conditions and most fault scenarios.

Efficient continuous SF6/N2 separation using low-cost and robust metal-organic frameworks composites

Physisorption presents a promising alternative to cryogenic distillation for capturing the most potent greenhouse gas, SF6, but existing adsorbents face challenges in meeting diverse chemical and engineering concerns. Herein, with insights into in-pore chemistry and industrial process design, we report a systematic investigation that constructed two low-cost composites pellets (Al(fum)@2%HPC and Al(fum)@5%Kaolin) coupled with an innovative two-stage Vacuum Temperature Swing Adsorption (VTSA) process for the ultra-efficient recovery of low-concentration SF6 from N2. Record-high selectivities (> 2×104) and SF6 dynamic capacities (~ 2.7 mmol/g) were achieved, while exceptional SF6 productivities (~ 58.7 L/kg), yields (~ 96.8%), and recyclability (~ 1000 cycles) were demonstrated in fixed-bed adsorption-desorption experiments under mild regeneration conditions. 2D solid-state NMR/in-situ FTIR, DFT-D binding/diffusion simulation analyses revealed the multi-site binding mode and the ultra-fast diffusion of SF6 within the channels. The proposed VTSA processes successfully met the dual stringent requirements of both environmental protection and electricity equipment operation: the SF6 recovery of 99.91% accompanied with a SF6 purity/working capacity of 99.91%/2.1 mmol/g, which significantly outperformed the industrial employed adsorbent zeolite 13X and showed only 18.7% the energy consumption of the cryogenic distillation.

Application of Evolutionary Game to Analyze Dual-Channel Decisions: Taking Consumer Loss Aversion into Consideration

Manufacturers and consumers are boundedly rational and ultimately seek evolutionarily stable strategies through trial and error, imitation, and learning. It is important to study the pricing strategies of manufacturers and the purchasing channel decisions of consumers in the context of increasingly fierce competition in online channels, in addition to consumers’ loss aversion due to increasingly confusing promotional strategies; accordingly, in this paper, an evolutionary game including both parties is constructed, and the loss aversion factor from prospect theory is introduced. Based on data from Chinese media reports on the cosmetics industry, simulation and sensitivity analyses were conducted using Matlab R2024a. The results indicate that—in addition to channel services affecting the evolutionarily stable strategy for purchasing channel selection—a decrease in consumer loss aversion will help consumers reach the evolutionarily stable strategy faster. For manufacturers, channel services do not affect their evolution to a unified pricing strategy; however, when consumer loss aversion increases, manufacturers’ evolutionarily stable strategy will shift from a unified pricing strategy to a differentiated pricing strategy.

Variable selection and basis learning for ordinal classification

We propose a method for variable selection and basis learning for high-dimensional classification with ordinal responses. The proposed method extends sparse multiclass linear discriminant analysis, with the aim of identifying not only the variables relevant to discrimination but also the variables that are order-concordant with the responses. For this purpose, we compute for each variable an ordinal weight, where larger weights are given to variables with ordered group-means, and penalize the variables with smaller weights more severely. A two-step construction for ordinal weights is developed, and we show that the ordinal weights correctly separate ordinal variables from non-ordinal variables with high probability. The resulting sparse ordinal basis learning method is shown to consistently select either the discriminant variables or the ordinal and discriminant variables, depending on the choice of a tunable parameter. Such asymptotic guarantees are given under a high-dimensional asymptotic regime where the dimension grows much faster than the sample size. We also discuss a two-step procedure of post-screening ordinal variables among the selected discriminant variables. Simulated and real data analyses confirm that the proposed basis learning provides sparse and interpretable basis, as it mostly consists of ordinal variables. Supplementary materials for this article are available online.

Exploring Inhibition Mechanisms in Wildtype and T315I BCR-ABL1: An In Silico Approach Integrating Virtual Screening, MD Simulations, and MM-GBSA Analysis.

The BCR-ABL tyrosine kinase which is responsible for the pathogenesis of chronic myeloid leukemia (CML), has emerged as a promising therapeutic target. To address this issue, we employed a comprehensive computational approach integrating virtual screening, molecular dynamics (MD) simulations, and MM-GBSA (Molecular Mechanics/Generalized Born Surface Area) analysis to identify potential inhibitors and elucidate their binding mechanisms. Initially, virtual screening was conducted on 994 compounds from the ZINC database and, these compounds were docked against wildtype and T315I mutant ABL1 for the Type I and Type II ABL1 kinase inhibition mechanisms. In our molecular docking analysis for Type I inhibition, compound 911 demonstrated notable affinity towards the wildtype ABL1, with a binding energy of -14.91 kcal/mol, while compound 972 showed significant binding affinity towards the mutant ABL1, with a binding energy of -14.27 kcal/mol. In the Type II inhibition mechanism, the compounds with the highest binding affinity were compound 261 in wildtype ABL1 with -17.05 kcal/mol binding energy and compound 966 to the mutant ABL1 with a binding energy of -16.29 kcal/mol. Furthermore, analyses of MD simulations and MM/GBSA binding free energy (ΔG) were performed for target proteins with compounds, that exhibited the most favorable binding affinities with target proteins. The selected hit compounds showed ΔG scores ranging from -118.09 to -74.85 kJ/mol in both wildtype and mutant ABL1. Considering all in silico studies performed, it can be inferred that the identified molecules hold promise as potential candidates for drug design aimed at targeting CML.

Enhancing throughput in labor intensive assembly lines

This study aims to optimize Company X’s labor-intensive production line to significantly improve operational efficiency and reduce long-standing bottlenecks. Company X is located in Perak State, Malaysia. Comprehensive time studies and simulation analyses were conducted to identify key issues and specific bottlenecks affecting throughput and workflow. The research utilized the Single-Minute Exchange of Dies (SMED) method and simulation software to predict the next improvement solutions, significantly reducing changeover times. Simulations indicated that implementing SMED could enhance production efficiency by approximately 17%. Additionally, the study found that adding heat press (HP) pallets and upgrading various workstations could increase overall efficiency to 121%, making this approach more cost-effective and saving more resources compared to establishing a new system, which would only achieve a 100% increase in efficiency. The study recommends that Company X focus on incremental upgrades to the existing production line rather than investing in a new system. This strategy ensures a smoother transition and better leverages existing investments. Simulation software played a crucial role in evaluating proposed solutions, helping to mitigate the risks and costs associated with trial-and-error methods. Such strategic evaluations allow for more confident implementation of changes, optimizing productivity and efficiency.

Asphyxiation effect analysis in confined space of B-type oil pool fire under pressure changes of nitrogen extinguishing system

Nitrogen fire extinguishing system has a significant development space in the modern fire extinguishing because of its environmental protection, energy saving and high stability. To further broaden the application field of nitrogen fire extinguishing system, a shrinkage size model was used as the research object and combined with simulated flow field analysis. A nitrogen fire extinguishing system was characterized for alcohol fire extinguishing efficacy using nitrogen injection pressure as a variable and parameters such as extinguishing time, oxygen concentration, and temperature. Furthermore, diesel and gasoline were used as combustible materials to verify the extinguishing efficacy of pressure variation on Class B oil pool fires. It was found that with the increase of nitrogen injection pressure, the nitrogen extinguishing efficacy gradually increased and then leveled off in the confined space. The asphyxiating oxygen concentrations were 12.2%, 13.2% and 13.7% for ethanol, diesel and gasoline, respectively. Asphyxiating oxygen concentration above limiting oxygen concentration (LOC). For extinguishing Class B oil pool fires in confined spaces, nitrogen extinguishing systems showed excellent fire extinguishing efficiency. They had specific application prospects in the field of protecting underground oil storage tanks and oil wells.

Synthesis, in silico and bio-evaluation studies of new isothiocyanate derivatives with respect to COX inhibition and H2S release profiles.

The development of H2S-donating derivatives of non-steroidal anti-inflammatory drugs (NSAIDs) is considered important to reduce or overcome their gastrointestinal side effects. Sulforaphane, one of the most extensively studied isothiocyanates (ITCs), effectively releases H2S at a slow rate. Thus, we rationally designed, synthesized, and characterized new ITC derivatives (I1-3 and I1a-e) inspired by the natural compound sulforaphane. The anti-inflammatory properties of these compounds were evaluated by their inhibitory activities against cyclooxygenase targets COX-1 and COX-2. Additionally, the cytotoxicity of the compounds was tested using the MTT assay on LPS-induced RAW 264.7 cells, revealing no cytotoxic effects at low doses. Notably, compounds I1 and fluorine-containing ester derivative I1c emerged as the most potent and selective COX-2 inhibitors, with selectivity indexes of 2611.5 and 2582.4, respectively. The H2S-releasing capacities of ITC derivatives were investigated and compared with that of sulforaphane, showing that while compounds I1-3 exhibit slow and similar H2S release to sulforaphane, the release from compounds I1a-e was not as pronounced as that of the standard. Physics-based molecular modeling studies including molecular docking and molecular dynamics (MD) simulations, binding free energy calculations and absorption, distribution, metabolism, and excretion (ADME) analyses were also conducted. MD simulations analysis underscored the crucial amino acids such as Tyr385, Trp387, Phe518, Val523, and Ser530 in the interactions between I1c hit compound and COX-2. The combined in silico and in vitro findings suggest that compounds I1 and I1c are promising NSAID candidates against selective COX-2 inhibition.

Identification of novel tau positron emission tomography tracers for chronic traumatic encephalopathy by comprehensive in silico screening and molecular dynamics simulation.

Chronic traumatic encephalopathy (CTE), a neurodegenerative disease associated with repetitive mild traumatic brain injury, is characterized neuropathologically by abnormal hyperphosphorylated tau accumulation. Early detection of tau deposition in the brain is crucial for the prevention and evaluation of CTE. Positron emission tomography (PET) tracers can image specific proteins, while the optimal PET tracer for CTE tau fibrils remains unidentified. In this study, structure-based virtual screening and CNS PET MPO algorithms were utilized to identify candidates for novel tau PET tracers from 23 000 compounds in the ChemDiv CNS BBB library. A total of 8 μs molecular dynamics simulations were then employed to evaluate their binding affinity and atomic-level interaction with CTE tau protofibrils. The results indicate that V017-7820 (CNS-4), S776-0061 (CNS-12), S567-0465 (CNS-18), and T828-0465 (CNS-25) exhibit higher docking scores and binding free energies with CTE tau protofibrils while also satisfying the fundamental physicochemical properties of PET tracers. Further simulation analyses reveal that CNS-4 has the strongest binding affinity to tau protofibrils among the four compounds. Hydrophobic, π-π stacking, and hydrogen bonding interactions are the primary driving forces for the binding of these compounds to CTE tau protofibrils. In particular, CNS-12 and CNS-25 exhibit more intense hydrophobic and π-π stacking interactions, whereas CNS-4 and CNS-25 exhibit stronger hydrogen bonding interactions. This study identifies promising lead compounds for tau PET tracers and highlights their mechanism of binding to CTE tau protofibrils, which provides new insights for further screening and development of novel PET tracers for CTE diagnosis.

Impacts of the Russia–Ukraine war on the welfare of palm oil producers and consumers in Indonesia

PurposeThis research aims to examine the impacts of the Russia–Ukraine war on the welfare of Indonesian palm oil producers and consumers.Design/methodology/approachThe research uses time-series data from 2000 to 2022 for inferential, simulation and descriptive analyses, coupled with a world vegetable oil trade model in the form of a system of simultaneous equations. The two-stage least squares (2SLS) estimation method with 37 simultaneous equations is used to estimate parameters.FindingsThe research results show that the Russia–Ukraine conflict has led to an increase in the welfare of Indonesian palm oil producers and consumers. A combination of domestic policies (replanting, export tax, mandatory biodiesel and domestic market obligation) has boosted Indonesian palm oil industry players welfare after the Russia–Ukraine conflict. On the other hand, it caused a decline in the welfare of cooking oil and biodiesel producers due to the surge in raw material costs and the decline in selling prices. Therefore, market differentiation for palm oil derivative products into international markets is required.Originality/valueThe results of this research provide empirical evidence to the Indonesian government regarding the importance of policies that regulate the demand and supply of palm oil compared to increasing palm oil production. The results of this research emphasize that the development of policies for the Indonesian palm oil industry needs to pay attention to global vegetable oil market fluctuations as well as the importance of holistic policies from the on-farm to the off-farm level to ensure that producers and consumers experience increased welfare.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-06-2024-0531

Designing a multi-epitope subunit vaccine against Orf virus using molecular docking and molecular dynamics

ABSTRACT Orf virus (ORFV) is an acute contact, epitheliotropic, zoonotic, and double-stranded DNA virus that causes significant economic losses in the livestock industry. The objective of this study is to design an immunoinformatics-based multi-epitope subunit vaccine against ORFV. Various immunodominant cytotoxic T lymphocytes (CTL), helper T lymphocytes (HTL), and B-cell epitopes from the B2L, F1L, and 080 protein of ORFV were selected and linked by short connectors to construct a multi-epitope subunit vaccine. Immunogenicity was enhanced by adding an adjuvant β-defensin to the N-terminal of the vaccine using the EAAAK linker. The vaccine exhibited a significant degree of antigenicity and solubility, without allergenicity or toxicity. The 3D formation of the vaccine was subsequently anticipated, improved, and verified. The optimized model exhibited a lower Z-score of −4.33, indicating higher quality. Molecular docking results demonstrated that the vaccine strongly binds to TLR2 and TLR4. Molecular dynamics results indicated that the docked vaccine-TLR complexes were stable. Immune simulation analyses further confirmed that the vaccine can induce a marked increase in IgG and IgM antibody titers, and elevated levels of IFN-γ and IL-2. Finally, the optimized DNA sequence of the vaccine was cloned into the vector pET28a (+) for high expression in the E.coli expression system. Overall, the designed multi-epitope subunit vaccine is highly stable and can induce robust humoral and cellular immunity, making it a promising vaccine candidate against ORFV

Fusion of transfer learning with nature-inspired dandelion algorithm for autism spectrum disorder detection and classification using facial features

Autism spectrum disorder (ASD) is a neurologic disorder considered to cause discrepancies in physical activities, social skills, and cognition. There is no specific medicine for treating this disorder; early intervention is critical to improving brain function. Additionally, the lack of a clinical test for detecting ASD makes diagnosis challenging. To regulate identification, physicians entertain the children’s activities and growing histories. The human face is employed as a biological signature as it has the potential reflections of the brain. It is utilized as a simpler and more helpful tool for early detection. Artificial intelligence (AI) algorithms in medicinal rehabilitation and diagnosis can help specialists identify various illnesses more successfully. However, owing to its particular heterogeneous symptoms and complex nature, diagnosis of ASD remains to be challenging for investigators. This work presents a Fusion of Transfer Learning (TL) with the Dandelion Algorithm for Accurate Autism Spectrum Disorder Detection and Classification (FTLDA-AASDDC) method. The FTLDA-AASDDC technique detects and classifies autism and non-autism samples using facial images. To accomplish this, the FTLDA-AASDDC technique utilizes a bilateral filter (BF) approach for noise elimination. Next, the FTLDA-AASDDC technique employs a fusion-based TL process comprising three models, namely MobileNetV2, DenseNet201, and ResNet50. Moreover, the attention-based bi-directional long short-term memory (A-BiLSTM) method is used to classify and recognize ASD. Finally, the Dandelion Algorithm (DA) is employed to optimize the parameter tuning process, improving the efficacy of the A-BiLSTM technique. A wide range of simulation analyses is performed to highlight the ASD classification performance of the FTLDA-AASDDC technique. The experimental validation of the FTLDA-AASDDC technique portrayed a superior accuracy value of 97.50% over existing techniques.

Application of artificial intelligence based on state grid ESG platform in clean energy scheduling optimization

The randomness and volatility of existing clean energy sources have increased the complexity of grid scheduling. To address this issue, this work proposes an artificial intelligence (AI) empowered method based on the Environmental, Social, and Governance (ESG) big data platform, focusing on multi-objective scheduling optimization for clean energy. This work employs a combination of Particle Swarm Optimization (PSO) and Deep Q-Network (DQN) to enhance grid scheduling efficiency and clean energy utilization. First, the work analyzes the complexity and uncertainty challenges faced in clean energy scheduling within the current power system, highlighting the limitations of traditional methods in handling multi-objective optimization and real-time response. Consequently, the work introduces the ESG big data platform and leverages its abundant data resources and computational power to improve the scheduling decision-making process. Next, PSO is used for initial scheduling optimization and a mathematical model for clean energy scheduling optimization is constructed. To further enhance the dynamic response capability of the scheduling process, this work designs a dual-layer architecture. Among this architecture, DQN is responsible for adjusting the PSO initially optimized scheduling plan based on real-time data during the actual scheduling process, adapting it to the instantaneous change demand and renewable energy output characteristics. Finally, to verify the model’s effectiveness, this work conducts simulation analyses using real data from the state grid ESG big data platform. The results indicate that this method significantly improves clean energy utilization, increasing it from 62.4% (with traditional methods) to 87.7%, while reducing scheduling costs by 22%. With the increase in communication delay, the generation time of scheduling instruction increases significantly. This is because the algorithm needs to wait longer to receive the complete grid status information, which affects the generation speed of scheduling instructions. Moreover, this method demonstrates good adaptability and stability under different load demands and clean energy supply conditions. This work introduces a novel and efficient method for clean energy scheduling optimization by integrating PSO and DQN. It contributes to the overall improvement of clean energy utilization within the State Grid and provides a theoretical and empirical foundation for further research in related fields.

A remote motion analysis of mass casualty incident simulations

BackgroundRegular training for mass casualty incidents at physical simulation events is vital for emergency services. The preparation and execution of these simulations consume huge amounts of time, personnel, and money. It is therefore important to gather as much information as possible from each simulation while minimizing any influence on the participants, so as to keep the simulation as realistic as possible. In this paper, an analysis of GPS-based remote motion measurements of participants in a mass casualty incident simulation is presented. A combination of different evaluation methods is used to analyze the data. This could reduce the potential bias of the measurement methods.MethodsMovement patterns of participants of mass casualty incident simulations, measured by GPS loggers, were analyzed. The timeline of the simulation was segmented into event sections, based on movement patterns of participants entering or leaving defined areas. Movement patterns of participants working closely together were correlated to analyze their cooperation. Written logs created by observers on the ground were used to reconstruct the events of the simulation, to provide a comparative reference to validate the motion analysis.ResultsRecorded motion patterns of the participants were found to be qualitatively related to observer logs and triage allocations, allowing a partial reconstruction of the behavior of the participants during the simulation. By analyzing the times the simulation patients left the site of events some possible misjudgments in the triage decisions were indicated.ConclusionsAnalysis of movement patterns from GPS loggers and comparison with observations made on the ground showed that accurate information about the events during the simulation can be automatically delivered. Although the records of observers on the ground are vital to assess details, delegation of the automated analysis of individual and group motion could perhaps allow observers to concentrate on more specific tasks. The partially automated motion analysis methods presented should simplify the process of analyzing mass casualty incident simulations.

Domain Mobility in the ORF2p Complex Revealed by Molecular Dynamics Simulations and Big Data Analysis

ORF2p (open reading frame 2 protein) is a multifunctional multidomain enzyme that demonstrates both reverse transcriptase and endonuclease activities and is associated with the pathophysiology of cancer. The 3D structure of the entire seven-domain ORF2p complex was revealed with the recent achievements in structural studies. The different arrangements of the CTD (carboxy-terminal domain) and tower domains were identified as the “closed-ring” and “open-ring” conformations, which differed by the hairpin position of the tower domain, but the structural diversity of these complexes has the potential to be more extensive. To study this, we performed sub-microsecond all-atom molecular dynamics simulations of the entire ORF2p complex with different starting configurations. The obtained molecular dynamic trajectories frames were assigned to several clusters following the dimension reduction to three principal components of the 1275 distances feature matrix. Five and six clusters were obtained for the “open” and “closed” ring models, respectively. While the fingers–palm–thumb core retains its rigid configuration during the MD (molecular dynamics) simulations, all other domains display the complicated dynamic behavior not observed in the experimental structures. The EN (endonuclease) and CTD domains display significant translations and rotations while their internal structures stay rigid. The CTD domain can either form strong contacts with the tower or be far apart from it for both formal “open” and “closed” ring states because the tower hairpin position is not the only determining factor of the protein complex configuration. While only the “thumb up” conformation is observed in all the trajectories, the active site can be obstructed by the movement of the CTD domain. Thus, molecular modeling and machine learning techniques provide valuable insights into the dynamical behavior of the ORF2p complex, which is hard to uncover with experimental methods, given the complexity and size of the object.

Phytochemistry, Antibacterial and Antioxidant Activities of Grewia lasiocarpa E. Mey. ex Harv. Fungal Endophytes: A Computational and Experimental Validation Study.

The genus Grewia are known for their medicinal properties. We isolated and characterized five endophytic fungi from Grewialasiocarpa for in vitro antibacterial and antioxidant activities. Five [Aspergillus fumigatus (MK243397.1), Aspergillus fumigatus (MK243451.1), Penicillium raistrickii (MK243492.1), Penicillium spinulosum (MK243479.1), Meyerozyma guilliermondii (MK243634.1)] had inhibitory activity (62.5-1000 µg/mL) against methicillin-resistant Staphylococcus aureus (MRSA). Antioxidant responses were 66.5 and 98.4% for 2,2-diphenyl-1-picrylhydrazyl and Ferric Ion Reducing Antioxidant Power, respectively. In silico evaluation of the phytochemicals of the extract (containing majorly n-hexadecanoic acid) was performed against penicillin-binding protein 2a (PBP2a) implicated in the broad clinical resistance of MRSA to conventional beta-lactams. Molecular dynamic simulation analyses revealed that the phytosterol constituents of the extract, especially dehydroergosterol (- 46.28 kcal/mol) had good stability (4.35 Å), and compactness (35.08 Å) with PBP2a relative to the unbound PBP2a and amoxicillin-PBP2a complex during the 100 ns simulation period, reinforcing them as putative leads that may be developed as viable alternatives to beta-lactams against infections caused by MRSA. However, the prediction that dehydroergosterol lacks oral bioavailability with poor water solubility suggests that it could benefit from structural optimisation for improved druggability. Hence, isolating and derivatizing dehydroergosterol for subsequent evaluation against PBP2a in vitro and in vivo, is recommended.