RMSD Research Articles

Quantum mechanical approaches and Molecular Docking studies of Platinum based anticancer drugs Satraplatin and Picoplatin structures.

The structures of the anticancer drug compounds satraplatin and picoplatin are optimized, and theoretical studies done by Density Functional Theory (DFT) to investigate the optical properties, global chemical descriptors, and band gap were calculated using the obtained Frontier molecular orbital values. The theoretically obtained data of the platinum drugs found to possess low energy gap values and low chemical hardness with high chemical softness describe that the compounds are chemically stable and most reactive. Using the molecular docking process, the binding mechanisms of the satraplatin and picoplatin drugs with cancer DNA structures were studied, and the results were analyzed. For the two drug compounds examined, satraplatin has shown the least binding energies at -5.06 kcal/mol compared to picoplatin at -2.69 kcal/mol, and conformations with root mean square deviation (RMSD) values by DNA structures are less than or equal to 2.00Å in targeting the specific DNA target residues of the reference bound ligand. The grid box was generated using the selective grid parameters using the Autodock 4.2 program. The drug molecules found to have the least binding energy value with the lowest inhibition constant implicate the good docking score towards the DNA structure by the docking studies. DFT studies also show good structural properties and chemical reactivity. From the obtained results, satraplatin and picoplatin were found to have good chemical descriptors and good binding affinity and are best suited for biological molecular targets.

Identifying the minimal sets of distance restraints for FRET-assisted protein structural modeling.

Proteins naturally occur in crowded cellular environments and interact with other proteins, nucleic acids, and organelles. Since most previous experimental protein structure determination techniques require that proteins occur in idealized, non-physiological environments, the effects of realistic cellular environments on protein structure are largely unexplored. Recently, Förster resonance energy transfer (FRET) has been shown to be an effective experimental method for investigating protein structure in vivo. Inter-residue distances measured in vivo can be incorporated as restraints in molecular dynamics (MD) simulations to model protein structural dynamics in vivo. Since most FRET studies only obtain inter-residue separations for a small number of amino acid pairs, it is important to determine the minimum number of restraints in the MD simulations that are required to achieve a given root-mean-square deviation (RMSD) from the experimental structural ensemble. Further, what is the optimal method for selecting these inter-residue restraints? Here, we implement several methods for selecting the most important FRET pairs and determine the number of pairs that are needed to induce conformational changes in proteins between two experimentally determined structures. We find that enforcing only a small fraction of restraints, , where is the number of amino acids, can induce the conformational changes. These results establish the efficacy of FRET-assisted MD simulations for atomic scale structural modeling of proteins in vivo.

Discovery of α-amylase and α-glucosidase dual inhibitors from NPASS database for management of Type 2 Diabetes Mellitus: A chemoinformatic approach.

Postprandial hyperglycemia, typical manifestation of Type 2 Diabetes Mellitus (T2DM), is associated with notable global morbidity and mortality. Preventing the advancement of this condition by delaying the rate of glucose absorption through inhibition of α-amylase and α-glucosidase enzymatic activities is of utmost importance. Finding a safe antidiabetic drug is essential since those that are currently on the market have drawbacks like unpleasant side effects. The current study utilized computer-aided drug design (CADD), as a quick and affordable method to find a substitute drug template that can be used to control postprandial hyperglycemia by modulating the activity of α-amylase and α-glucosidase enzymes. The Natural Products Activity and Species database (NPASS) (30,926 compounds) was screened in silico, with a focus on evaluating drug-likeness, toxicity profiles and ability to bind on a target protein. Two molecules NPC204580 (Chrotacumine C) and NPC137813 (1-O-(2-Methoxy-4-Acetylphenyl)-6-O-(E-Cinnamoyl)-Beta-D-Glucopyranoside) were identified as potential dual inhibitors for α-amylase and α-glucosidase with free binding energies of -14.46 kcal/mol and -12.58 kcal/mol for α-amylase, and -8.42 kcal/mol and -8.76 kcal/mol for α-glucosidase, respectively. The molecules showed ionic, H-bonding and hydrophobic interactions with critical amino acid residues of both enzymes. Moreover, 100 ns molecular dynamic simulations showed that both molecules are stable on the receptors' active sites based on root mean square deviation (RMSD), root mean square fluctuation (RMSF), and the Generalized Born surface area (GBSA) energy calculated. The two compounds are thus promising therapeutic agents for T2DM that merit further investigation due to their excellent binding energies, encouraging pharmacokinetics, toxicity profiles, and stability as demonstrated in simulated studies.

LociPARSE: A Locality-aware Invariant Point Attention Model for Scoring RNA 3D Structures.

A scoring function that can reliably assess the accuracy of a 3D RNA structural model in the absence of experimental structure is not only important for model evaluation and selection but also useful for scoring-guided conformational sampling. However, high-fidelity RNA scoring has proven to be difficult using conventional knowledge-based statistical potentials and currently available machine learning-based approaches. Here, we present lociPARSE, a locality-aware invariant point attention architecture for scoring RNA 3D structures. Unlike existing machine learning methods that estimate superposition-based root-mean-square deviation (RMSD), lociPARSE estimates Local Distance Difference Test (lDDT) scores capturing the accuracy of each nucleotide and its surrounding local atomic environment in a superposition-free manner, before aggregating information to predict global structural accuracy. Tested on multiple datasets including CASP15, lociPARSE significantly outperforms existing statistical potentials (rsRNASP, cgRNASP, DFIRE-RNA, and RASP) and machine learning methods (ARES and RNA3DCNN) across complementary assessment metrics. lociPARSE is freely available at https://github.com/Bhattacharya-Lab/lociPARSE.

Enlightenment the dynamic behavior of norbornene–modified ’click’ 4–arm polyethylene glycol hydrogel: Delving into framework properties and transport properties through molecular dynamics simulations

The thiol-norbornene cross-linked 4-arm Polyethylene Glycol (PEG) ’click’ hydrogel is a synthetic, sustainable, and bio-inspired polymer extensively used in biomedical applications. Experimental techniques are widely used to study this system, whereas all-atom molecular dynamics simulations, which offer insight into the dynamic behaviours of the system, are never used to study this system. Three models of thiol-norbornene cross-linked PEG ’click’ hydrogel and a base PEG hydrogel are crafted. It is immersed in water to study its structural and transport properties. Structural properties are analysed through root-mean-square deviation (RMSD), radial distribution function (RDF), hydrogen bonds (H-bond), and stress–strain behavior. The RMSD reveals that the norbornene component enhances hydrogel stability compared to the base model. The RDF illustrates interactions between oxygen in PEG chains and water. H-bond results underscore PEG’s strong H-bond acceptance. The norbornene-functionalized crosslinked PEG hydrogel displays maximum H-bonding. It demonstrates a superior swelling ratio attributed to freezing and non-freezing water effects, indicating high stability and potential suitability for applications. Mean square displacements (MSD) unveil the diffusion coefficients of the hydrogel. The base hydrogel model shows the diffusive behavior, and the diffusion coefficient is 1.97 × 10-10 m2/s. The base model has the highest MSD value compared to other systems. The thiol-norbornene crosslinked click hydrogel has the highest Young’s modulus, which signifies the stiffness of the material. Findings illuminate thiol-norbornene ’click’ PEG hydrogel behaviour, emphasising chemical cross-linking’s crucial role in advanced biomedical applications.

Molecular insights from structural dynamics of HER2-inhibitor complexes pave the way for new breast cancer drugs

The human epidermal growth factor receptor 2 (HER2) is closely associated with the development and progression of breast cancer, making it a critical target for therapeutic interventions. In this study, we employed a comprehensive computational drug discovery strategy to identify potential inhibitors of HER2. Our approach combined virtual screening, re-docking procedures, molecular dynamics (MD) simulations, and free energy landscape analysis using principal component analysis (PCA). From the extensive PubChem library, we initially screened 733 compounds for their binding potential to HER2, using docking scores as a primary filter. These scores ranged notably from −11.172 to −7.028 kcal/mol, indicating substantial binding capacities. Following this screening, we selected four promising compounds (PubChem CID 166029206, 166544027, 21031510, and 11712721) along with a control compound (70I) for in-depth analysis. Utilizing the Amber software suite for MD simulations, we conducted 200-nanosecond simulations to assess the interactions and binding efficiencies of these selected compounds with HER2. We analysed the molecular interactions through various parameters such as root mean square deviation (RMSD), root mean square fluctuation (RMSF), and hydrogen bond formation patterns, free binding energy calculations. The PCA-based free energy landscape analysis revealed that these compounds consistently occupied a distinct low-energy basin, indicating their high stability and strong binding affinity for the HER2. This detailed analysis provided insights into the stability and conformational dynamics of these potential inhibitors when bound to the HER2. Our findings pave the way for further experimental validation and development of these compounds as therapeutic agents in breast cancer treatment.

Improved Polar Current Shell Algorithm for Ocean Current Retrieval from X-Band Radar Data

This paper presents an improved algorithm for retrieving ocean surface currents from X-band marine radar images. The original polar current shell (PCS) method begins with a 3D fast Fourier transform (FFT) of the radar image sequence, followed by the extraction of the dispersion shell from the 3D image spectrum, which is then transformed into a PCS using polar coordinates. Building on this foundation, the improved approach is to analyze all data points corresponding to different wavenumber magnitudes in the PCS domain rather than analyzing each specific wavenumber magnitude separately. In addition, kernel density estimation (KDE) to identify high-density directions, interquartile range filtering to remove outliers, and symmetry-based filtering to further reduce noise by comparing data from opposite directions are also utilized for further improvement. Finally, a single curve fitting is applied to the filtered data rather than conducting multiple curve fittings as in the original method. The algorithm is validated using simulated data and real radar data from both the Decca radar, established in 2008, and the Koden radar, established in 2017. For the 2008 Decca radar data, the improved PCS method reduced the root-mean-square deviation (RMSD) for speed estimation by 0.06 m/s and for direction estimation by 3.8° while improving the correlation coefficients (CCs) for current speed by 0.06 and direction by 0.07 compared to the original PCS method. For the 2017 Koden radar data, the improved PCS method reduced the RMSD for speed by 0.02 m/s and for direction by 4.6°, with CCs being improved for current speed by 0.03 and direction by 0.05 compared to the original PCS method.

In Silico Analysis of novel Phytocompounds targetting Dipeptidyl peptidase 4 Enzyme: A New Link between Prediabetes and its cardio-metabolic complications

BackgroundDPP-4 inhibitors, are widely used clinically for treatment of Diabetes. Increase in circulating DPP4 levels have been well demonstrated in patients with Type 2 Diabetes mellitus. The present study assessed the contribution of DPP-4 axis to the pathophysiology of Prediabetes and its cardiometabolic complications. In addition, in silico analysis of phytocompounds that target DPP 4 enzyme was undertaken to identify novel Phytocompounds with therapeutic potential in Prediabetes. MethodologyA novel experimental model of Prediabetes coexisting with hypertension and dyslipidemia was developed in experimental rats to elucidate the pathophysiological role of DPP-4 in Prediabetes and its resultant cardio-metabolic sequale. In the Prediabetic rats, DPP-4 levels, lipid profile, blood pressure readings, fasting blood sugar values, insulin resistance, beta cell function were measured and compared to Normal Control group. In addition, the DPP-4 Inhibitory activity of Phytocompounds (Arjunetin, Guggulsterone) was studied by molecular docking studies including Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), DPP 4 binding affinity and Protein-ligand simulation between the DPP4 and the Phytocompounds was performed. Results were compared to Vildagliptin, the synthetic DPP-4 inhibitor and metformin (Glucophage), the standard antidiabetic drug used in clinical settings. ResultsA statistically significant increase (p < 0.001) in DPP-4 levels along with a decline in bea cell function with increase in Insulin resistance was observed in Prediabetic rats. A positive correlation (p < 0.001) between serum DPP-4 with HbA1c, lipids, and systolic blood pressure was found. The novel model of Prediabetes co-existing with hypertension and dyslipidemia was successfully developed and validated. The silico active site interaction data from simulation studies demonstrated that Arjunetin and Guggulsterone shows effective and stable binding with DPP-4. Arjunetin maintains its contact with Glu205, Glu206, and Asn710 and by interacting with Val207 and His740, it effectively occupies the flanking residues of the active site area. Based on their protein-ligand contact report, Vildagliptin maintains its connection through Tyr547 and during simulation, Guggulsterone interacts with Ser630. ConclusionThe current study offers experimental evidence of DPP-4′s causal role in the development of cardio-metabolic consequences (dyslipidemia and hypertension) of Prediabetes. Arjunetin and Guggulsterone are two Phytocompounds whose potential as promising DPP-4 inhibitors has been further documented using in silico molecular docking studies.

The Impact of Drying Techniques on Stabilizing Microencapsulated Astaxanthin From Shrimp Shells: A Comparative Study of Spray Drying Versus Freeze Drying

ABSTRACTThe research aimed to study how different drying methods (spray and freeze drying) affect the release kinetics of microencapsulated astaxanthin in various environmental conditions. Shrimp shell extract containing astaxanthin was encapsulated using different wall components (maltodextrin with different dextrose equivalents and modified starch) via a simplex lattice mixture design. The encapsulated extract was then subjected to storage at different temperatures (25°C ± 2°C and 2°C ± 4°C) and humidity conditions (52% ± 2% and 75% ± 2%), as well as exposure to UV light (four 15 W lamps, 254 nm, for 10 h). The release kinetics of astaxanthin were analyzed using various models (page, Newton Korsmeyer–Peppas model, Modified Henderson and Pabis, Diffusion approach, and Two‐term exponential). The evaluation results of correlation coefficient (R2), root mean square deviation (RMSE), and mean absolute percentage error (MAPE) values of different models showed that the astaxanthin degradation followed a two‐term exponential kinetics in both types of microcapsules. Astaxanthin degradation increased with higher temperatures, humidity, and UV light exposure. However, microcapsules with equal wall compound ratios exhibited better preservation of astaxanthin. The study also emphasized the significance of optimizing storage conditions and wall materials for microencapsulated astaxanthin, as well as the utility of the two‐term exponential model in enhancing stability and shelf life.

Assessment of photon-counting Computed Tomography for quantitative imaging in radiation therapy: PCCT for quantitative imaging in radiotherapy.

To test a first generation clinical PCCT scanner's capabilities to characterize materials in an anthropomorphic head phantom for radiation therapy purposes. A CIRS 731-HN head-and-neck phantom (CIRS/SunNuclear, Norfolk, USA) was scanned on a NAEOTOM Alpha photon-counting CT (PCCT) and a SOMATOM Definition AS+ with single-energy and dual-energy CT techniques (SECT and DECT, respectively), both scanners manufactured by Siemens (Siemens Healthineers, Forscheim, Germany). A method was developed to derive relative electron density (RED) and effective atomic number (EAN) from linear attenuation coefficients (LAC) of virtual mono-energetic images and applied for the PCCT and DECT data. For DECT, Siemens' syngo.via 'Rho/Z'-algorithm was also utilized. Proton stopping-power ratios (SPR) were calculated based on RED/EAN with the Bethe equation. For SECT, a stoichiometric calibration to SPR was used. Nine materials in the phantom were segmented, excluding border pixels. Distributions and root-mean-square deviations (RMSD) within the material regions were evaluated for LAC, RED/EAN and SPR, respectively. Two example ray-projections were also examined for LAC, SPR and water-equivalent thickness (WET), for illustrations of a more treatment-like scenario. There was a tendency towards narrower distributions for PCCT compared to both DECT methods for the investigated quantities, observed across all materials for RED only. Likewise the scored RMSDs showed overall superiority for PCCT with a few exceptions: for water-like materials, EAN and SPR were comparable between the modalities; for titanium the RED and SPR estimates were inferior for PCCT. The PCCT data gave the smallest deviations from theoretic along the more complex example ray profile whereas the more standard projection showed similar results between the modalities. This study shows promising results for tissue characterization in a human-like geometry for radiotherapy purposes using PCCT. The significance of improvements for clinical practice remains to be demonstrated.

Numerical Analysis of Steel Frame Using Electro-Mechanical Impedance Technique

Nowadays, piezoelectric materials are most widely used in structural health monitoring (SHM) system for nondestructive evaluation. In this paper the damage study using electro-mechanical impedance (EMI) technique, which is nondestructive method of SHM. This paper presents a numerical analysis of four-story steel frame and multiple damages were induced in the steel frame by utilizing smart material like Lead Zirconate Titanate (PZT). A PZT-4 piezoelectric material was embedded in the beam of the frame. The type of damping used in steel frame model was Rayleigh damping. The numerical analysis was done in COMSOL MULTIPHYSICS 6.2 and frequency domain analysis was done, from that admittance values were find out for 30-300KHz frequency range. The real part of the admittance gives conductance values. Then, conductance verses frequency graph was created that will indicate the structural health and performance. Moreover, root mean square deviation (RMSD) index was found out for each case that provides a quantitative measure of deviation.

Exploring Electronic and Conformational Attributes of an Organic Donor‐Bridge‐Acceptor Molecular System

AbstractThis research explores the electronic properties and conformational dynamics of the ZnP‐COPV‐ (Zinc Porphyrin ‐ Carbon bridged Oligo Phenylenevinylene ‐ Fullerene) organic semiconductor via specialized Density Functional Theory (DFT) and Molecular Dynamics (MD) computational techniques. First, through DFT calculations, essential HOMO (Highest Occupied Molecular Orbital), LUMO (Lowest Unoccupied Molecular Orbital), and Molecular Electrostatic Potential (MEP) electronic attributes are meticulously dissected providing insights into the intricate charge transfer processes between the constituent donor‐acceptor moieties. Furthermore, MD simulations are employed to unveil the molecular system's multifaceted conformational flexibility and stability. The Root‐mean‐squared deviation (RMSD), end‐to‐end distance, and torsional angles quantitative analysis of conformational attributes support the carbon‐bridged oligo‐phenylenevinylene (COPV) molecular wire's ‐skeleton planar and rigid conformation. This minimal end‐to‐end distance variation (within Å) compared to the initially extended structure and the constrained torsional motion (within for ZnP‐COPV and for COPV‐) after thermalization showcases COPV's ability to maintain structural rigidity over time, aligning with the concept of effective ‐conjugation. Finally, through the lens of time dependent (TD)‐DFT, the dynamic evolution of the HOMO–LUMO energy gap, TD‐DFT excitation energies and oscillator strengths are explored as the molecular structure transforms over time. The observed energy gap variation underscores the molecule's adaptability in the face of structural modifications, hinting at an intriguing connection between structural stability and enhanced electronic properties. The research provides a comprehensive understanding of the intricate interplay between conformational dynamics and electronic attributes in organic semiconductors, providing quantitative insights crucial for designing stable, high‐performance materials for cutting‐edge optoelectronic applications and helping advance the collective understanding of sustainable energy conversion.

Computational Assessment of Clinical Drugs against SARS-CoV-2: Foreseeing Molecular Mechanisms and Potent Mpro Inhibitors.

The emergence of new SARS-CoV-2 variants of concern (VOC) is a propulsion for accelerated potential therapeutic discovery. SARS-CoV-2's main protease (Mpro), essential for host cell viral replication, is a pre-eminent druggable protein target. Here, we perform extensive drug re-profiling of the comprehensive Excelra database, which compiles various under-trial drug candidates for COVID-19 treatment. For mechanistic understanding, the most promising screened-out molecules with targets are subjected to molecular dynamics (MD) simulations. Post-MD analyses demonstrate Darunavir, Ponatinib, and Tomivosertib forming a stable complex with Mpro, characterized by less fluctuation of Cα atoms, smooth and stable root-mean-square deviation (RMSD), and robust contact with the active site residues. Likewise, they all have lower binding free energy with Mpro, demonstrating strong affinity. In free energy landscape profiles, the distances from His41 and Cys145 exhibit a single energy minima basin, implying their preponderance in proximity to Mpro's catalytic dyad. Overall, the computational assessment earmarks promising candidates from the Excelra database, emphasizing on carrying out exhaustive biochemical experiments along with clinical trials. The work lays the foundation for potential therapeutic interventions in treating COVID-19.

P70S6K as a Potential Anti-COVID-19 Target: Insights from Wet Bench and In Silico Studies.

The onset of SARS-CoV-2 infection in 2019 sparked a global COVID-19 pandemic. This infection is marked by a significant rise in both viral and host kinase activity. Our primary objective was to identify a pivotal host kinase essential for COVID-19 infection and the associated phenomenon of the cytokine storm, which may lead to long-term COVID-19 complications irrespective of viral genetic variations. To achieve this, our study tracked kinase phosphorylation dynamics in RAW264.7 macrophages following SPIKE transfection over time. Among the kinases surveyed, p70S6K (RPS6KB1) exhibited a 3.5-fold increase in phosphorylation at S418. This significant change prompted the selection of p70S6K for in silico investigation, utilizing its structure bound to M2698 (PDB: 7N93). M2698, an oral dual Akt/p70S6K inhibitor with an IC50 of 1.1 nM, exhibited psychosis side effects in phase I clinical trials, potentially linked to its interaction with Akt2. Our secondary objective was to discover a small-molecule analogue of M2698 that exhibits a distinct binding preference for p70S6K over Akt2 through computational modeling and analysis. The in silico part of our project began with validating the prediction accuracy of the docking algorithm, followed by an OCA analysis pinpointing specific atoms on M2698 that could be modified to enhance selectivity. Subsequently, our investigation led to the identification of an analog of M2698, designated as S34, that showed a superior docking score towards p70S6K compared to Akt2. To further assess the stability of S34 in its protein-ligand (PL) complexes with p70S6K and Akt2, MD simulations were conducted. These simulations suggest that S34, on average, forms two hydrogen bond interactions with p70S6K, whereas it only forms one hydrogen bond interaction with Akt2. This difference in hydrogen bond interactions likely contributed to the observed larger root mean square deviation (RMSD) of 0.3 nm in the S34-Akt2 complex, compared to 0.1 nm in the S34-p70S6K complex. Additionally, we calculated free binding energy to predict the strength of the binding interactions of S34 to p70S6K and Akt2, which showed ~2-fold favorable binding affinity of S34 in the p70S6K binding pocket compared to that in the Akt2 binding pocket. These observations may suggest that the S34-p70S6K complex is more stable than the S34-Akt2 complex. Our work focused on identifying a host kinase target and predicting the binding affinity of a novel small molecule to accelerate the development of effective treatments. The wet bench results specifically highlight p70S6K as a compelling anti-COVID-19 target. Meanwhile, our in silico investigations address the known off-target effects associated with M2698 by identifying a close analog called S34. In conclusion, this study presents novel and intriguing findings that could potentially lead to clinical applications with further investigations.

Can satellite products monitor solar brightening in Europe?

Satellite products provide the best way to monitor the solar radiation reaching the Earth’s surface on a global scale. However, their capability to monitor solar radiation trends needs to be constantly evaluated. This depends on their temporal stability and the accurate representation of all processes driving solar radiation. This study evaluates these aspects by comparing and cross-comparing different solar radiation products (ERA5, CAMS-RAD 4.6, SARAH-3, CLARA-A3, CERES-EBAF 4.2) against in-situ measurements over Europe.All products show a moderate positive bias over Europe but strong differences in their root mean squared deviation (RMSD) related to their different cloud transmittance models. Geostationary-based products (SARAH-3, CAMS-RAD 4.6) provide the smallest RMSD closely followed by CLARA-A3, whereas ERA5 shows a large RMSD due to random errors in cloud transmittance.All products show an increase in surface solar radiation, or brightening, over the last 40 years over Europe, but the magnitude of the trends and their spatiotemporal variability differ between products. Despite finding temporal inhomogeneities in some products, the different trends are mostly due to different aerosol modeling approaches implemented by each product. Both SARAH-3 (+2.3 W/m2/decade, 2001–22) and CERES-EBAF 4.2 (+2.2 W/m2/decade, 2001–22) provide the most consistent trends compared to in-situ data, showing that after stabilizing in the late 2000s, brightening is particularly recovering in Western Europe. In-situ measurements show a reduction of aerosol optical depth from 2001 to 2022 that has been accentuated in the last 10 years, particularly in Western Europe. This would be consistent with the hypothesis that brightening recovery is driven by an aerosol reduction, though other analyses suggest that clouds also play a role in this recovery. More work is needed to understand the contribution of aerosols to solar radiation trends and the exact aerosol effects represented by each solar radiation product.

Identification of optimal laccases for chlorinated phenol degradation: Insights from molecular modelling, docking, dynamics, and free energy calculations

This study aimed to investigate the interactions between twenty different bacterial and fungal laccases and chlorinated phenols (CPs), which are listed as priority pollutants by the United States Environmental Protection Agency (USEPA). Molecular docking was used to identify the optimal laccase for degrading CPs out of twenty selected laccases to save time and reduce the in vitro selection of laccase for CPs degradation. The average binding energies of CPs with bacterial and fungal laccases were − 4.72 kcal/mol and − 5.21 kcal/mol, respectively. Based on the highest average binding energies Trametes versicolor (fungus) and Xanthomonas arboricola (bacteria) laccase apo and bound forms were investigated for stability and interaction dynamics using a 200 ns molecular dynamic simulation. The root mean square deviations (RMSD) of T. versicolor and X. arboricola laccase bound forms were within the 0.25 nm and 0.5 nm limit. The radius of gyration (Rg) for all laccases bound forms remained within the range from 1.88 to 2.24 nm with majority of residues in root mean square fluctuations (RMSF) were within the 0.24 nm and found consistent with the laccase apo values. In an aqueous solution, at ∼300 K and ∼ 1 bar pressure, laccase stability with CPs is facilitated by hydrophobic interactions, hydrogen bonds, and van der Waals forces. The corresponding computational insights will be advantageous for the selection, design and application in engineering of laccases for in vitro CPs degradation.

Phytobioinformatics screening of ayurvedic plants for potential α-glucosidase inhibitors in diabetes management

The enzyme α-glucosidase in the small intestine regulates blood glucose levels and stimulates the hydrolysis of oligosaccharides and polysaccharides, increasing glucose levels in the body. Inhibiting this enzyme slows glucose digestion and absorption and as a result post-prandial blood glucose levels remain low, causing decreased insulin demand. Here, we investigated the ayurvedic antidiabetic plants and virtually screened an in-house library of 478 phytochemicals of these plants against the human α-glucosidase. We identified 11 secondary metabolites, including palmitic acid α-monoglyceride, (+)-(2 R)-6-propionyloxyethyl-4′,5,7-trihydroxyisoflavanone, Abruquinone E, and Aurantiamide Acetate, among others, showed stronger interactions with the receptor than the native ligand N-acetyl cysteine. Surprisingly, except one, all of these metabolites were from Abrus precatorius L. [Fabaceae] affirming its ethnopharmacological use against diabetes. The stability of the interactions between the ligands and receptor protein was evaluated through Molecular Dynamic (MD) simulation trajectories including root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), H bonds, β-factor analysis, and binding energy calculation through MM/GBSA method. The efficacy of top metabolites in inhibiting α-glucosidase is depicted in pharmacophore analysis. A comprehensive pharmacokinetics analysis confirmed the druggability, safety, and efficiency of top drug candidates. Additionally, we predicted the interactions of these top metabolites within the biological system. The medicinal properties described in this study will help develop active drug candidates for therapeutic purposes. Further experiments are recommended to prove the effectiveness of these metabolites in inhibiting the α-glucosidase enzyme for exploring their potential in the treatment of diabetes.

Modeling and economic analysis of block absorber (RadFrac) performance with stage variations for post-combustion CO2 capture

This study presents a comprehensive modeling and economic evaluation of a Monoethanolamine (MEA)-based post-combustion carbon capture (PCC) absorber unit. The simulation provides detailed insights into the absorber's internal profiles, including temperature gradients and the percentage of CO2 captured, with a particular focus on the effects of varying the number of stages and absorber diameters. Validation against experimental data demonstrated close alignment between simulated and observed values, with an average Root Mean Square Deviation (RMSD) of 0.018 and 4.0129, respectively, confirming the reliability of the simulation in capturing the complex dynamics of the absorber.The economic analysis, conducted using the Aspen Process Economic Analyzer (APEA), simplified the complex relationship between absorber segmentation, capital and operational costs, and absorber diameter. The study revealed that increasing the absorber diameter and the number of stages leads to a significant rise in Total Capital Cost (TCC), Total Operating Cost (TOC), Equipment Cost, and Total Installed Cost (TIC), with the highest costs observed at a stage number of 90 and a diameter of 1.0 m. However, the analysis identified an optimal configuration at an absorber diameter of 0.45 m with either 10 or 20 stages. This setup effectively balances cost and CO2 capture efficiency, offering a more economical solution compared to configurations with larger diameters and higher stage numbers, which substantially increase expenses. The findings highlight the critical trade-offs between operational efficiency and capital expenditure, stressing on the crucial role of absorber diameter in determining the economic feasibility of the absorber block in PCC systems.

Visualising the strength development of FICP-treated sand using impedance spectroscopy

Fungal Induced Calcium Carbonate Precipitation (FICP) is a novel method used in geotechnical engineering that enhances the engineering properties of sand by using the potential of fungal activity. This research is the first attempt to monitor the strength of FICP treated sand using embedded Piezoelectric (PZT) patch based Electromechanical Impedance (EMI) spectroscopy. In the past, the strength of such treated sand has been determined through the destructive methods like Unconfined Compressive Strength (UCS) test. In this study, the sand is mixed with the filamentous fungus Aspergillus Niger and the cementation solution (urea and \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{\ ext{C}\ ext{a}\ ext{C}\ ext{l}}_{2}$$\\end{document} in the ratio of 1:1) is injected after every 24 h. Results recorded from the cost-effective EVAL AD5933 chip indicate that the shifting of frequency impedance signals in each phase is in good alignment with UCS and calcium carbonate content (CCC). Following the 28-day treatment period, the treated sand achieves a maximum UCS of 3.93 MPa, accompanied by a CCC of 15.19%. In order to correlate EMI signals with treatment cycles, UCS, and CCC, various multi linear regression (MLR) equations for statistical metrics like root mean square deviation (RMSD), mean absolute percentage deviation (MAPD), and correlation coefficient deviation (CCD) are developed. Additionally, Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray (EDX) analyses have been conducted to observe the success of the FICP process in the sand.

The Effects of Different Restoration Materials on the Trueness of Intraoral Scanners

Aim: This study aimed to assess how different restoration materials affect the trueness of intraoral scanners. Materials and Methods: Artificial teeth on a typodont model were prepared for crowns and fixed partial dentures (FPDs) using full metal, monolithic zirconia, and porcelain-fused-to-metal (PFM) materials. Each group underwent 10 scans with a Trios intraoral scanner, generating 60 STL files. A reference scanner created a master model using scanning spray to reduce reflection errors. The STL files were aligned with reverse engineering software for comparison and were analyzed in micrometers (μm) using Root Mean Square (RMS) and Mean Distance measurements. The Levene test and two-way ANOVA with Post Hoc analysis were used for statistical evaluation. Results: The RMS deviations for the FPDs were 77.9 ± 15.2 μm (full metal), 84.6 ± 6.9 μm (monolithic zirconia), and 130 ± 19.7 μm (PFM). For the crowns, the RMS values were 76.9 ± 6.5 μm (metal), 71 ± 8.2 μm (monolithic zirconia), and 153 ± 22.4 μm (PFM). The mean distance deviations for the FPDs were 11.4 ± 4.8 μm (metal), 11.2 ± 3.4 μm (monolithic zirconia), and 18.3 ± 2.6 μm (PFM). For the crowns, the mean distances were 8.6 ± 3.4 μm (metal), 10.2 ± 3 μm (monolithic zirconia), and 24.7 ± 3.3 μm (PFM). Significant differences were noted in the PFM groups. Conclusion: Restoration materials notably affected intraoral scanner trueness, especially PFM restorations. The restoration length did not significantly affect the accuracy.