RMSD Research Articles

Strengths and limitations of web servers for the modeling of TCRpMHC complexes

Cellular immunity relies on the ability of a T-cell receptor (TCR) to recognize a peptide (p) presented by a class I major histocompatibility complex (MHC) receptor on the surface of a cell. The TCR-peptide-MHC (TCRpMHC) interaction is a crucial step in activating T-cells, and the structural characteristics of these molecules play a significant role in determining the specificity and affinity of this interaction. Hence, obtaining 3D structures of TCRpMHC complexes offers valuable insights into various aspects of cellular immunity and can facilitate the development of T-cell-based immunotherapies. Here, we aimed to compare three popular web servers for modeling the structures of TCRpMHC complexes, namely ImmuneScape (IS), TCRpMHCmodels, and TCRmodel2, to examine their strengths and limitations. Each method employs a different modeling strategy, including docking, homology modeling, and deep learning. The accuracy of each method was evaluated by reproducing the 3D structures of a dataset of 87 TCRpMHC complexes with experimentally determined crystal structures available on the Protein Data Bank (PDB). All selected structures were limited to human MHC alleles, presenting a diverse set of peptide ligands. A detailed analysis of produced models was conducted using multiple metrics, including Root Mean Square Deviation (RMSD) and standardized assessments from CAPRI and DockQ. Special attention was given to the complementarity-determining region (CDR) loops of the TCRs and to the peptide ligands, which define most of the unique features and specificity of a given TCRpMHC interaction. Our study provides an optimistic view of the current state-of-the-art for TCRpMHC modeling but highlights some remaining challenges that must be addressed in order to support the future application of these tools for TCR engineering and computer-aided design of TCR-based immunotherapies.

High throughput virtual screening and validation of Plant-Based EGFR L858R kinase inhibitors against Non-Small cell lung Cancer: An integrated approach Utilizing GC–MS, network Pharmacology, Docking, and molecular dynamics

Lung cancer ranks as the 2nd most common cancer globally. It’s the most prevalent cancer in men and the 2nd most common in women. The prominent events in EGFR-mutated non-small-cell lung cancer (NSCLC) include the emergence of the L858R mutation within EGFR exon 21. Despite the promising efficacy of EGFR inhibitors in managing lung cancer, the development of acquired resistance poses a significant hurdle. In the current investigation, we focused on the screening of two phytochemicals, namely Dehydrocostus lactone and Mokkolactone, derived from the Saussurea lappa plant, as potential inhibitors targeting EGFR L858R mutant lung cancer. The chloroform and ethanol extract of the plant demonstrated anti-proliferative activity through the Resazurin chemosensitivity assay, exhibiting an IC50 value of 37.90 ± 0.29 µg/ml with selectivity index 2.4. Through a GC–MS study, we identified 11 phytochemicals for further insilico analysis. These compounds underwent ADMET assessment followed by drug likeliness analysis before being subjected to molecular docking against EGFR L858R, identified through protein–protein interaction network analysis. All phytochemicals exhibited binding energy scores ranging from −6.9 to −8.1 kcal/mol. Dehydrocostus lactone and Mokkolactone were specifically identified for their binding profile. Findings from 100 ns molecular dynamics simulations demonstrated their enhanced stability compared to the reference ligand DJK. This was evident in the root mean square deviation (RMSD) values, ranging from 0.23 ± 0.01 nm to 0.30 ± 0.05 nm, the radius of gyration values, from 1.71 ± 0.01 nm to 1.72 ± 0.01 nm, and the solvent accessible surface area values, from 155.39 ± 2.40 nm2 to 159.32 ± 2.14 nm2. Additionally, favourable characteristics were observed in terms of hydrogen bonding, principal component analysis, and free energy landscape analysis. Examination of their electronic structure via density functional theory revealed efficient properties, with the highest occupied molecular orbital-least unoccupied molecular orbital energy gap values ranging from −3.984 eV to −6.547 eV. Further, in vivo analysis is required to gain a more comprehensive understanding and efficacy of these identified phytochemicals against lung cancer.

Structural Health Monitoring of Aerospace Structures using Guided Electromagnetic Waves in a Dielectric Waveguide at Ka-Band

Dielectric waveguide (DW) antenna systems are widely used in millimeter-wave radar applications. A large and innovative field is structural health monitoring (SHM) of aerospace structures. The proposed research aims at detecting structural changes and potential defects at Ka-band on metallic structures that represents the device under test (DUT). To determine the severity of damage compared to the pristine structural state, dimensionless damage indicators (DI) are calculated after the root-mean-square deviation (RMSD) and root-mean-square correlation coefficient (RMSCC) methods. The geometric design of the DW is crucial for a barrier-free propagation of guided electromagnetic waves (GEW) of a specific frequency bandwidth inside the dielectric material. In the presented approach, a rectangular DW is designed through numerical modelling which contains a polypropylene matrix, aluminum walls and two K-connectors, each with an inserted pin. The DW is attached to the surface of a nearly plane DUT connected externally to a vector network analyzer (VNA) that measures the elements of the scattering matrix (S-parameters). The reflection and transmission behavior, reflected in the S-parameters, provide information about the interaction of the GEW with a damage. It was found that damage sizes of 2mm can be detected reliably with the proposed methodology. The full paper will present and discuss numerical simulations with CST Microwave Studio with a comparison to experimental measurements.

Contextualizing Polarimetric Retrievals of Boundary Layer Height Using State-of-the-Art Boundary Layer Profiling

Abstract Knowledge about the depth of the planetary boundary layer (PBL) is crucial for a variety of applications, but direct observations of PBL depth are spatiotemporally sparse. Recent studies have proposed using operational dual-polarization weather radars to observe the evolution of PBL depth by capitalizing on unique differential reflectivity (ZDR) signatures of Bragg scatter at the top of the PBL. While this approach appears promising and cost-effective, uncertainties remain about the representativeness of these estimates and how its efficacy may vary by geography and climatology. To address these outstanding uncertainties, this study compares collocated observations collected from two WSR-88D radars and two state-of-the-art mobile boundary layer profiling systems and evaluates the proposed methodology over the full diurnal cycle. Results indicate good overall correspondence between the profiling- and radar-based PBL depth estimates, with an abrupt divergence during the early evening transition and large discrepancies overnight. Relatively large root-mean-square-deviations (RMSDs) coupled with small biases match expectations when comparing spatially averaged data with point observations during PBL growth, which capture frequent fluctuations. A qualitative examination of the radar data reveals signatures of elevated residual layers, clouds, and ground clutter, all of which can obfuscate the desired surface-based PBL signal but which may have their own utility. The prominence of the Bragg scatter signal is found to be correlated with the observed moisture gradient at the top of the PBL, reflecting climatological variability that should be considered. These findings motivate further work to improve the automated detection of Bragg scatter layers from polarimetric radar data. Significance Statement Knowledge of the height of the planetary boundary layer matters for weather forecasting, air quality, and renewable energy production. Currently, boundary layer height measurements are taken at select locations twice a day. However, a method to use the existing national network of polarimetric weather radars for this purpose has been proposed. This work evaluates this method against specialized boundary layer measurements. The results show that the method is generally reliable during the daytime and could be used for a variety of applications including climatologies and model evaluation. There remain a number of situational caveats, including residual turbulence, clouds/precipitation, ground clutter, and certain meteorological environments, that may require modification of the approach and need to be considered in future work.

In Silico Studies of a Novel Scaffold of Acetylsalicylic Acid Derivatives Against EGFR by Molecular Docking and Molecular Dynamics Simulations

In this study, a molecular docking study was performed to propose the acetylsalicylic acid derivative 2-[(4-Acetylphenyl)carbamoyl]phenyl acetate (AMPBS) as a potential cancer candidate targeting the Epidermal Growth Factor Receptor (EGFR). The native ligand erlotinib was used as a control compound. The calculated docking score of -7.4 kcal/mol compared to the native ligand erlotinib of -7.0 kcal/mol of AMPBS compound revealed a promising anticancer activity. The stability of the complex was interpreted by careful analysis of the root mean square deviation (RMSD), root mean square fluctuations (RMSF) and mean hydrogen bond number (Hb) plots obtained from the MD trajectories. The ADMET properties of AMPBS were evaluated using relevant online tools. Drug-likeness studies showed that AMPBS is suitable for use in living organisms. It was predicted that AMPBS in the active pocket could be a promising inhibitor due to its high binding energy, interaction mechanism and retention in the active pocket.

Solubility measurement, calculation, solvent effect and preferential solvation of fenoxaprop-p-ethyl in four cosolvent mixtures at (278.15–323.15) K

Equilibrium solubility of fenoxaprop-p-ethyl in aqueous mixtures of ethanol/ isopropanol/acetonitrile/N,N-dimethylformamide (DMF) plus water was experimentally measured by isothermal saturation method at (278.15–323.15) K under 101.2 kPa. The order of solubility (mole fraction) of fenoxaprop-p-ethyl in pure solvents from high to low is 4.233 × 10−2 (DMF 323.15 K) > 2.044 × 10−2 (acetonitrile 323.15 K) > 7.727 × 10−3 (isopropanol 323.15 K) > 5.338 × 10−3 (ethanol 323.15 K) > 2.565 × 10−5 (water 323.15 K). Experimental results showed that temperature and type of solvent were decisive factors for the dissolution of fenoxaprop-p-ethyl. The content of fenoxaprop-p-ethyl in solution was analyzed by high-performance liquid chromatography (HPLC). Moreover, the solubility data of fenoxaprop-p-ethyl was correlated by Jouyban-Acree (J-A) model, van’t Hoff-Jouyban-Acree (V-J-A) model and Apelblat-Jouyban-Acree (A-J-A) model. The maximum of root-mean-square deviation (RMSD), relative average deviation (RAD) and standard deviation (SD) predicted via three thermodynamic models were 1.78 × 10−4 (DMF + water), 2.87 × 10−2 (acetonitrile + water) and 1.79 × 10−4 (DMF + water), respectively. In addition, the solvent effect between solute and solvent molecules was deduced by KAT-LSER model and the result showed that dipolarity/polarizability interactions between the solute and solvent molecules were dominant in the dissolution of fenoxaprop-p-ethyl. Finally, the preferential solvation parameter (δx1,3) of fenoxaprop-p-ethyl was obtained by the method of inverse Kirkwood–Buff integrals and the preferential dissolution behavior of each concentration range is explained by Lewis acid base theory. However, for acetonitrile + water, the change of δx1,3 may be due to the polarization effects and it is not suitable for Lewis acid base theory.

Molecular dynamics simulations of betaine-based deep eutectic solvents with varying iso-alcohols chain lengths

Betaine-based (BET) deep eutectic solvents (DESs) made with isoalcohols of varying hydrocarbon chain lengths, namely triethylene glycol (TEG), 1, 2-ethanediol (ETD), 1, 3-propanediol (PPD) and 1, 4-butanediol (BTD) were modelled. Their effects on the formation of aqueous biphasic system (ABS) with 2 M dipotassium phosphate salt solution (K2HPO4) as well as the extraction of common protein molecules, bovine serum albumin (BSA), were studied using molecular dynamics simulation with Gromacs. Qualitative and quantitative data were used to analyse the simulation results, which includes visualised systems in equilibrium, root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration, radial distribution function (RDF) and hydrogen bond analysis. The results and literature research validated the role of inorganic salt solute concentration on the phase forming ability as well as the possibility of protein denaturation. More investigations and experimental works are to be carried out regarding the type of inorganic salt and its concentration for aqueous biphasic systems (ABSs) with alcohol based DESs and salt solution for protein extraction purposes. This is essential to understand the salting-out effect and to isolate the effects of hydrocarbon chain length from other influencing factors. Additionally, inorganic salt concentrations, presence of ether linkage as well as hydrocarbon chain length is shown to play a role in the dynamics between molecules in the system as well as protein conformational changes. Overall, ABS incorporating PPD and BTD with hydrocarbon chain lengths not exceeding C4 demonstrated superior performance in terms of protein conformational stability and interaction, showcasing their efficacy in protein extraction process.

In Silico Comparative Analysis of Ivermectin and Nirmatrelvir Inhibitors Interacting with the SARS-CoV-2 Main Protease.

Exploring therapeutic options is crucial in the ongoing COVID-19 pandemic caused by SARS-CoV-2. Nirmatrelvir, which is a potent inhibitor that targets the SARS-CoV-2 Mpro, shows promise as an antiviral treatment. Additionally, Ivermectin, which is a broad-spectrum antiparasitic drug, has demonstrated effectiveness against the virus in laboratory settings. However, its clinical implications are still debated. Using computational methods, such as molecular docking and 100 ns molecular dynamics simulations, we investigated how Nirmatrelvir and Ivermectin interacted with SARS-CoV-2 Mpro(A). Calculations using density functional theory were instrumental in elucidating the behavior of isolated molecules, primarily by analyzing the frontier molecular orbitals. Our analysis revealed distinct binding patterns: Nirmatrelvir formed strong interactions with amino acids, like MET49, MET165, HIS41, HIS163, HIS164, PHE140, CYS145, GLU166, and ASN142, showing stable binding, with a root-mean-square deviation (RMSD) of around 2.0 Å. On the other hand, Ivermectin interacted with THR237, THR239, LEU271, LEU272, and LEU287, displaying an RMSD of 1.87 Å, indicating enduring interactions. Both ligands stabilized Mpro(A), with Ivermectin showing stability and persistent interactions despite forming fewer hydrogen bonds. These findings offer detailed insights into how Nirmatrelvir and Ivermectin bind to the SARS-CoV-2 main protease, providing valuable information for potential therapeutic strategies against COVID-19.

Unveiling the potential of Traditional Chinese Medicines in combating NorA-mediated S. aureus drug resistance. A molecular dynamic study

Drug resistance Staphylococcus aureus (S. aureus) is emerging as the most prevalent pathogen in surgical wounds and orthopedic surgeries. NorA is a major drug efflux involved in the drug resistance, reducing intracellular drug concentrations. The current study aimed to find some potential inhibitors derived from Traditional Chinese Medicines (TCM) against NorA using a 500 ns molecular dynamics (MD) simulation. Root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), free energy, dynamic cross correlation matrix (DCCM) was computed. Three TCM compound; piperine, demethoxycurcumin, and tectorigenin exhibited many hydrogen bonds and hydrophobic contacts in the binding pockets residues, especially with ionizable residues (Arg 98, Glu 222, Asp 307, and Arg 310), forming two different charged regions. RMSD of NorA-piperine complex shows some initial instability at 5.2 Å, followed by an increase to 5.8 Å at 222 ns, and a further rise to 6.1 Å by the end of the 500 ns simulation period. Similarly, the NorA-demethoxycurcumin and NorA-tectorigenin complexes display elevated terminal RMSD of 7.9 Å each. RMSF, Rg, free energy, and binding pockets interactions confirmed the potential activity of piperine, demethoxycurcumin, and tectorigenin against NorA inhibition. In apo state NorA exhibited dispersion within the ranges of −100 to 150 (PC1: 39.97 %) while in complexed with ligands it illustrated significant differences in the motion patterns on PC1 (46.04 % and 27.39 %). The NorA-ligands DCCM displayed predominantly negatively correlated motions, particularly at the terminal resides from 200 to 370 where the pocket binding residues are located. These compounds also demonstrated drug-likeness and fitting best in the binding pocket residues (Asn > 137, Phe > 140, Phe > 303 Asp > 307, Arg > 310, Glu > 222, and Thr > 223). In conclusion, the compounds piperine, demethoxycurcumin, and tectorigenin interact with NorA through hydrogen bonds and hydrophobic contacts, particularly with key ionizable residues, Arg 98, Glu 222, Asp 307, and Arg 310, indicating potential inhibitory activity.

Integrated quantum chemical calculations, predictive toxicity assessment, absorption, distribution, metabolism, excretion and toxicity profiling and molecular docking analysis to unveil the therapeutic potential of non‐oxovanadium(IV) and organotin(IV) complexes targeting breast cancer cells

AbstractIn this work, theoretical calculations of o‐phenylphenol‐based non‐oxovanadium(IV) and organotin(IV) complexes, previously prepared and reported by our group, have been carried out by density functional theory (DFT). Density functional theory quantum chemical computations were used to explore the structural and spectroscopic characteristics of the complexes in this study. The inhibitory nature of complexes were revealed via molecular docking research, which were performed against selected breast cancer cell proteins, 5NWH and 3HB5. The optimization and stability of complexes 1–6, were conducted using optimized DFT/B3LYP/6–311++G (d, p) level. Simulated computations of the molecular electrostatic potential surface were also performed to analyze the reactive behavior of the non‐oxovanadium(IV) and organotin(IV) complexes. The stability and molecular reactivity of the molecules were computed using the HOMO‐LUMO energies, energy gap, chemical potential (μ), electronegativity (χ), hardness (η), and softness (S) values. In silico analysis through molecular docking, ADMET properties and toxicity evaluation was used to assess its anticancer activity, drug‐likeness property and toxicity. The binding constant value, evaluated from molecular docking, was found to be very promising, −10.1 kcal mol−1 observed for vanadium complex 5 and the complexes were found to exhibit inhibition constant as low as 0.0378 μMol. Root‐mean‐square deviation (RMSD) has been carried out to validate molecular docking studies, which have been found to be below 2.0 Å for the complexes, indicating successful docking of the ligand‐protein complex by the program. The complexes, evaluated for their toxicity behavior in terms of Lethal Dose, based on Globally Harmonized System (GHS), have been found to be chemical safe falling under the category III and V and hence can find use as future metallo‐based drugs.

Resilient and Sustainable Structures through EMI-Based SHM Evaluation of an Innovative C-FRP Rope Strengthening Technique

Reinforced Concrete (RC) members in existing RC structures are susceptible to shear-critical due to their under-reinforced design. Thus, implementing a retrofitting technique is essential to eliminate the casualties that could arise from sudden and catastrophic collapses due to these members’ brittleness. Among other proposed techniques, using Carbon-Fiber Reinforced Polymers (C-FRP) ropes to increase the shear strength of RC structural elements has proved to be a promising reinforcement application. Moreover, an Electro-Mechanical Impedance (EMI-based) method using Lead Zirconate Titanate (PZT-enabled) was employed to assess the efficiency of the strengthening scheme. Initially, the proposed technique was applied to C-FRP rope under the subjection of pullout testing. Thus, a correlation of the rope’s tensile strength with the EMI responses of the PZT patch was achieved using the Root Mean Square Deviation (RMSD) metric index. Thereafter, the method was implemented to the experimentally acquired data of C-FRP ropes, used as shear reinforcement in a rectangular deep beam. The ropes were installed using the Embedded Through Section (ETS) scheme. Furthermore, an approach to evaluate the residual shear-bearing capacity based on the EMI responses acquired by being embedded in and bonded to the ropes’ PZTs was attempted, demonstrating promising results and good precision compared to the analytical prediction of the C-FRP ropes’ shear resistance contribution.

Determination and intermolecular interaction analysis of the liquid–liquid equilibrium of water, acrylic acid, and different solvents at 303.2 K and 101.3 kPa

To explore the effective treatment of acrylic acid wastewater, liquid–liquid equilibrium (LLE) data for water + acrylic acid + {n-propyl acetate, n-butyl acetate, n-heptanol, and methyl isopropyl ketone} at 303.2 K and 101.3 kPa were measured respectively. The distribution coefficients (D) and separation factors (S) were used to assess the extraction effectiveness of the four solvents, and the results of the comparative data showed that the D values of the four solvents for acrylic acid ranged from 1.6 to 4.4, and the S values were in the order of n-butyl acetate > n-propyl acetate > MIPK > n-heptanol. The experimental data were fitted by NRTL and UNIQUAC models to obtain the binary interaction parameters, and the obtained above parameters were proved reliable by the mixing surface analysis method of Gibbs energy topology. The root mean square deviation (RMSD) of both models did not exceed 0.0051 and 0.0077, respectively. In addition, σ-profiles analyzed the differences in extraction effects in terms of interactions and interaction energies, and the analyses showed that the theoretical calculations are in agreement with the experimental results.

The Effect of β-Sheet Secondary Structure on All-β Proteins by Molecular Dynamics Simulations.

The effect of β-sheet ratio and chain length on all-β proteins was investigated by MD simulations. Protein samples composed of different repeating units with various β-sheet ratios or a different number of repeating units were simulated under a broad temperature range. The simulation results show that the smaller radius of gyration was achieved by the protein with the higher proportion of β-sheet secondary structure, which had the lower nonbonded energy with more HBs within the protein. The root mean square deviation (RMSD) and the root mean square fluctuation (RMSF) both increased with temperature, especially in the case of a longer chain. The visible period was also shown according to the repeated secondary structure. Several minimum values of RMSF were located on the skeleton of Cα atoms participating in the β-sheet, indicating that it is a kind of stable secondary structure. We also concluded that proteins with a short chain or a lower ratio of β-sheet could easily transform their oriented and compact structures to other ones, such as random coils, turns, and even α-helices. These results clarified the relationship from the primary level to the 3D structure of proteins and potentially predicted protein folding.

Biocomputational screening of natural compounds targeting 15-hydroxyprostaglandin dehydrogenase to improve skeletal muscle during aging.

Skeletal muscle (SM) contains a diverse population of muscle stem (or satellite) cells, which are essential for the maintenance of muscle tissue and positively regulated by prostaglandin E2 (PGE2). However, in aged SM, PGE2 levels are reduced due to increased prostaglandin catabolism by 15-hydroxyprostaglandin dehydrogenase (15-PGDH), a negative regulator of SM tissue repair and regeneration. Screening of a library of 80,617 natural compounds in the ZINC database against 15-PGDH was conducted from PyRx. Further, drug-likeness rules, including those of Lipinski, Ghose, Veber, Egan, and Muegge were performed. The selected complex was forwarded for MD simulations up to 100ns. Based on free energy of binding obtained from docking revealed that ZINC14557836 and ZINC14638400 more potently inhibiting to 15-PGDH than SW033291 (the control and high-affinity inhibitor of 15-PGDH). The free energies of binding obtained from PyRx for 15-PGDH-ZINC14557836, 15-PGDH-ZINC14638400, and 15-PGDH-SW033291 complexes were - 10.30, -9.80, and - 8.0kcal/mol, respectively. Root mean square deviations (RMSDs), root mean square fluctuations (RMSFs), radii of gyration (Rg), solvent-accessible surface areas (SASAs), and H-bond parameters obtained by 100 ns MD simulations predicted ZINC14557836 and ZINC14638400 more stably complexed with 15-PGDH than SW033291. The several parameters, including physicochemical properties and drug-likenesses, were within acceptable limits, and ZINC14557836 and ZINC14638400 also satisfied other drug-likeness rules, including those of Lipinski, Ghose, Veber, Egan, and Muegge. These findings suggest that ZINC14557836 and ZINC14638400 provide starting points for the development of medications that increase SM regeneration and muscle stem (or satellite) cell numbers by inhibiting 15-PGDH.

Computational Analysis of Albaflavenone Interaction with SlMAPK1 for Drought Resistance in Tomato.

As global agricultural challenges intensify, particularly drought stress, the exploration of innovative strategies for crop resilience has become crucial. This study focuses on the role of the microbial endophyte metabolite Albaflavenone in enhancing drought resistance in tomato (Solanum lycopersicum L.) through the activation of the SlMAPK1 protein in the MAPK pathway. To computationally analyze the interaction between Albaflavenone and SlMAPK1 and to elucidate the potential enhancement of drought tolerance in tomato plants through this interaction. We utilized molecular docking, homology modeling, and molecular dynamics simulations to investigate the binding affinities and interaction dynamics between SlMAPK1 and Albaflavenone. Functional network analysis was employed to examine protein-protein interactions within the MAPK pathway, while the MM-GBSA method was used to calculate binding free energies. Our computational analyses revealed that Albaflavenone exhibited a high binding affinity to SlMAPK1 with a binding energy of - 8.9kcal/mol. Molecular dynamics simulations showed this interaction significantly stabilized SlMAPK1, suggesting enhanced activity. Specifically, the root mean square deviation (RMSD) of the Albaflavenone-SlMAPK1 complex stabilized at around 3.1Å, while the root mean square fluctuations (RMSF) indicated consistent amino acid conformations. Additionally, the radius of gyration (Rg) analysis demonstrated minimal variance, suggesting a compact and stable protein-ligand complex. The significant binding affinity between Albaflavenone and SlMAPK1 highlights the potential of leveraging plant-microbe interactions in developing sustainable agricultural practices. These findings also demonstrate the effectiveness of computational methods in dissecting complex biological interactions, contributing to a deeper understanding of plant resilience strategies against environmental stresses.

A hybrid algorithm for estimating total nitrogen from a large eutrophic plateau lake using Orbita hyperspectral (OHS) satellite imagery

Total nitrogen concentration (CTN) enrichment is the primary cause of natural water eutrophication. Accurately estimating CTN and its spatiotemporal dynamics is crucial for formulating monitoring and control measures to alleviate lake eutrophication. A hybrid model was proposed for estimating CTN in optically complex inland waters by incorporating the relationship between CTN and water optical active components for Zhuhai-1 Orbita hyperspectral (OHS) imagery. Compared with other semi-analytical algorithms, the re-adjusted reference wavelength QAA716 shows the best performance in aph(λ) retrieval. The hybrid model for CTN estimation achieves an root mean square deviation (RMSD) of 0.20 mg/L, a mean absolute percentage deviation (MAPD) of 6.96 %, and a unbiased mean absolute percentage deviation (UMAPD) of 6.96 %, with aph(569) accuracy exerting the greatest influence. Ground-satellite synchronous validation demonstrates robust performance, with an RMSD of 0.28 mg/L, a MAPD of 14.49 %, and a UMAPD of 14.92 %. The hybrid model was applied to OHS observations of Lake Dianchi from April 2019 to September 2021. The analysis revealed a generally decreasing trend in CTN during this timeframe. The above results demonstrate that the robustness and applicability of the proposed CTN hybrid model for inland waters with complex optical properties. Furthermore, satellite-based data products provide valuable information for formulating lake management strategies.

Toward high-resolution modeling of small molecule-ion channel interactions.

Ion channels are critical drug targets for a range of pathologies, such as epilepsy, pain, itch, autoimmunity, and cardiac arrhythmias. To develop effective and safe therapeutics, it is necessary to design small molecules with high potency and selectivity for specific ion channel subtypes. There has been increasing implementation of structure-guided drug design for the development of small molecules targeting ion channels. We evaluated the performance of two RosettaLigand docking methods, RosettaLigand and GALigandDock, on the structures of known ligand-cation channel complexes. Ligands were docked to voltage-gated sodium (NaV), voltage-gated calcium (CaV), and transient receptor potential vanilloid (TRPV) channel families. For each test case, RosettaLigand and GALigandDock methods frequently sampled a ligand-binding pose within a root mean square deviation (RMSD) of 1-2Å relative to the experimental ligand coordinates. However, RosettaLigand and GALigandDock scoring functions cannot consistently identify experimental ligand coordinates as top-scoring models. Our study reveals that the proper scoring criteria for RosettaLigand and GALigandDock modeling of ligand-ion channel complexes should be assessed on a case-by-case basis using sufficient ligand and receptor interface sampling, knowledge about state-specific interactions of the ion channel, and inherent receptor site flexibility that could influence ligand binding.

Comprehensive empirical modeling of ScAlN/AlGaN/GaN ferroelectric HEMT

This correspondence introduces a concise depiction of a high electron mobility transistor (HEMT) utilizing a model that incorporates the effect of ferroelectric (FE) polarization on the empirical expression for drain current. This is accomplished by modifying the basic Shockley equation (S.E) for MESFET. The model employed in this investigation is particularly geared towards investigating the impact of the counterclockwise hysteresis effect due to the use of dielectric ScAlN on the transfer characteristics ( Id−Vgs ), which is mainly attributed to trapping and de-trapping of charge carriers at the dielectric/semiconductor interface. Modifying the S.E. made it possible to replicate the behavior observed in experimental HEMT accurately. We reported the modified drain current empirical expressions for the ScAlN/AlGaN/GaN HEMT that considers the FE polarization. The results of this study showed good agreement between the Id−Vgs characteristics obtained from the analytical and experimental data, with a root mean square deviation (RMSD) error of 0.97% and a mean squared error error of 0.94%. This analytical drain current model, presented herein for the first time, incorporates a limited number of fitting parameters. Therefore, enhancing the computational efficiency to assess the performance of FE GaN HEMTs for usage in wide-area electronics.

Nirmatrelvir, A COVID‐19 Drug, and Human Serum Albumin: Computational Analysis of Their Molecular Interactions

AbstractUnderstanding the interaction between drugs and human serum albumin (HSA) is crucial for drug development. This study investigates the binding of a COVID‐19 drug, nirmatrelvir (NIR), to HSA through a computational approach. Molecular docking was employed to identify potential NIR binding site on HSA, and revealed distinct clusters at Sites I, II, and III. However, Site III exhibited a preference for NIR based on the lowest binding energy. Molecular dynamics simulation over 100 ns confirmed the stability of the NIR−HSA complex, with consistent binding at Site III. The compactness of HSA was maintained throughout the simulation period, as indicated by the radius of gyration, while structural flexibility analysis revealed typical fluctuations around some residues. Root‐mean‐square deviation (RMSD) patterns illustrated stability of the complex, and hydrogen bond analysis suggested four stable hydrogen bonds between NIR and HSA. These investigations offer insightful information on molecular interactions governing the stability of the NIR−HSA complex.

A Short-Term Air Pollutant Concentration Forecasting Method Based on a Hybrid Neural Network and Metaheuristic Optimization Algorithms

In the contemporary era, global air quality has been adversely affected by technological progress, urban development, population expansion, and the proliferation of industries and power plants. Recognizing the urgency of addressing air pollution consequences, the prediction of the concentration levels of air pollutants has become crucial. This study focuses on the short-term prediction of nitrogen dioxide (NO2) and sulfur dioxide (SO2), prominent air pollutants emitted by the Kerman Combined Cycle Power Plant, from May to September 2019. The proposed method utilizes a new two-step feature selection (FS) process, a hybrid neural network (HNN), and the Coot optimization algorithm (COOT). This combination of FS and COOT selects the most relevant input features while eliminating redundant ones, leading to improved prediction accuracy. The application of HNN for training further enhances the accuracy significantly. To assess the model’s performance, two datasets, including real data from two different parts of Combined Cycle Power Plant in Kerman, Iran, from 1 May 2019 to 30 September 2019 (namely dataset A and B), are utilized. Subsequently, mean square error (MSE), mean absolute error (MAE), root mean square deviation (RMSE), and mean absolute percentage error (MAPE) were employed to obtain the accuracy of FS-HNN-COOT. Experimental results showed MSE of FS-HNN-COOT for NO2 ranged from 0.002 to 0.005, MAE from 0.016 to 0.0492, RMSE from 0.0142 to 0.0736, and MAEP from 4.21% to 8.69%. Also, MSE, MAE, RMSE, and MAPE ranged from 0.0001 to 0.0137, 0.0108 to 0.0908, 0.0137 to 0.1173, and 9.03% to 15.93%, respectively, for SO2.