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The complex dielectric permittivity of L-Proline in water and ethanol solutions with molar concentrations ranging from 0.025 M to 0.15 M was measured by open-ended coaxial probe technique. The measurements were carried out across a frequency span of 0.02 < ν/GHz < 20 and temperatures varying from 298.15 K to 323.15 K. The densities (ρ) and refractive index (nD) of the L-proline in aqueous and ethanol solutions were also determined to provide insights into the solute-solvent interactions in the system. The Havriliak-Negami equation was employed to compute the dielectric relaxation time of the mixtures. The relaxation time of L-Proline in an ethanol medium was found to be higher than that of L-Proline in an aqueous medium due to the greater degree of self-association of ethanol molecules. Additionally, the relaxation time of the mixtures lengthened with rising molar concentration, which is attributed to the presence of hydrogen bonds among L-Proline and aqueous/ethanol molecules. The strength of the hydrogen bond interaction of L-Proline in both mediums was calculated using single-point energy calculations employing IEFPCM/PCM solvation models through DFT/B3LYP and MP2 approaches with a 6-311 G ++ (d, p) basis set. The results were correlated with the hydrogen bond strength, Gibbs' free energy of activation parameter, and dipole-dipole interactions.Communicated by Ramaswamy H. Sarma.

Study of molecular interaction between L-proline with water/ethanol mixturesDielectric relaxation studies of aqueous and ethanol mixtures at various temperaturesCorrelate relaxation time in terms of contribution of hydration/bound waterEffect of H- bonding on dipole moment, relaxation and polarizability valuesPositive values of ΔG* confirms the presence of multimers in the solution.

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Most cancer cells exhibit high glycolysis rates under conditions of abundant oxygen. Maintaining a stable glycolytic rate is critical for cancer cell growth as it ensures sufficient conversion of glucose carbons to energy, biosynthesis, and redox balance. Here we deciphered the interaction between PKM2 and the thermodynamic properties of the glycolytic pathway. Knocking down or knocking out PKM2 induced a thermodynamic equilibration in the glycolytic pathway, characterized by the reciprocal changes of the Gibbs free energy (ΔG) of the reactions catalyzed by PFK1 and PK, leading to a less exergonic PFK1-catalyzed reaction and a more exergonic PK-catalyzed reaction. The changes in the ΔGs of the two reactions cause the accumulation of intermediates, including the substrate PEP (the substrate of PK), in the segment between PFK1 and PK. The increased concentration of PEP in turn increased PK activity in the glycolytic pathway. Thus, the interaction between PKM2 and the thermodynamic properties of the glycolytic pathway maintains the reciprocal relationship between PK concentration and its substrate PEP concentration, by which, PK activity in the glycolytic pathway can be stabilized and effectively counteracts the effect of PKM2 KD or KO on glycolytic rate. In line with our previous reports, this study further validates the roles of the thermodynamics of the glycolytic pathway in stabilizing glycolysis in cancer cells. Deciphering the interaction between glycolytic enzymes and the thermodynamics of the glycolytic pathway will promote a better understanding of the flux control of glycolysis in cancer cells.

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Cilostazol is a phosphodiesterase III inhibitor characterized by poor solubility. This limitation can be overcome by using a drug carrier capable of delivering the drug to the target site. Cyclodextrins are essential as drug carriers because of their outstanding complexation abilities and their capacity to improve drug bioavailability. This study comprises two stages: The first involves verifying different cyclodextrins and their complexation abilities towards cilostazol. This was accomplished using molecular docking simulations (MDS) and density functional theory (DFT). Both techniques indicate that the largest Sulfobutyl Ether-ß-Cyclodextrin forms the most stable complex with cilostazol. Additionally, other important parameters of the complex are described, including binding sites, dominant interactions, and thermodynamic parameters such as complexation enthalpy, Gibbs free energy, and Gibbs free energy of solvation. The second stage involves a binding study between cilostazol and Phosphodiesterse3 (PDE3). This study was conducted using molecular docking simulations, and the most important energetic parameters are detailed. This is the first such report, and we believe that the results of our predictions will pave the way for future drug development efforts using cyclodextrin-cilostazol complexes as potential therapeutics.

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Ponatinib and tofacitinib, established kinase inhibitors and FDA-approved for chronic myeloid leukemia and rheumatoid arthritis, are recently undergoing investigation in diverse clinical trials for potential repurposing. The aryl hydrocarbon receptor (AhR), a transcription factor influencing a spectrum of physiological and pathophysiological activities, stands as a therapeutic target for numerous diseases. This study employs molecular modelling tools and in vitro assays to identify ponatinib and tofacitinib as AhR ligands, elucidating their binding and molecular interactions in the AhR PAS-B domain. Molecular docking analyses revealed that ponatinib and tofacitinib occupy the central pocket within the primary cavity, similar to AhR agonists 2,3,7,8-tetrachlorodibenzodioxin (TCDD) and (benzo[a]pyrene) B[a]P. Our simulations also showed that these compounds exhibit good stability, stabilizing many hot spots within the PAS-B domain, including the Dα-Eα loop, which serves as a regulatory element for the binding pocket. Binding energy calculations highlighted ponatinib's superior predicted affinity, revealing F295 as a crucial residue in maintaining strong interaction with the two compounds. Our in vitro data suggest that ponatinib functions as an AhR antagonist, blocking the downstream signaling of AhR pathway induced by TCDD and B[a]P. Additionally, both tofacitinib and ponatinib cause impairment in AhR-regulated CYP1A1 enzyme activity induced by potent AhR agonists. This study unveils ponatinib and tofacitinib as potential modulators of AhR, providing valuable insights into their therapeutic roles in AhR-associated diseases and enhancing our understanding of the intricate relationship between kinase inhibitors and AhR.

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We hypothesize that a "Faustian bargain"-the trading of increased SARS-CoV2 viral infection with a concurrent potential for prevention of life-threatening lower lung infection explains the previous and future morbidity and mortality from COVID-19. Further, this trade-off is made feasible by fundamental principles of thermodynamics and receptor affinity.

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The first step in comprehending the properties of Au10 clusters is understanding the lowest energy structure at low and high temperatures. Functional materials operate at finite temperatures; however, energy computations employing density functional theory (DFT) methodology are typically carried out at zero temperature, leaving many properties unexplored. This study explored the potential and free energy surface of the neutral Au10 nanocluster at a finite temperature, employing a genetic algorithm coupled with DFT and nanothermodynamics. Furthermore, we computed the thermal population and infrared Boltzmann spectrum at a finite temperature and compared it with the validated experimental data. Moreover, we performed the chemical bonding analysis using the quantum theory of atoms in molecules (QTAIM) approach and the adaptive natural density partitioning method (AdNDP) to shed light on the bonding of Au atoms in the low-energy structures. In the calculations, we take into consideration the relativistic effects through the zero-order regular approximation (ZORA), the dispersion through Grimme's dispersion with Becke-Johnson damping (D3BJ), and we employed nanothermodynamics to consider temperature contributions. Small Au clusters prefer the planar shape, and the transition from 2D to 3D could take place at atomic clusters consisting of ten atoms, which could be affected by temperature, relativistic effects, and dispersion. We analyzed the energetic ordering of structures calculated using DFT with ZORA and single-point energy calculation employing the DLPNO-CCSD(T) methodology. Our findings indicate that the planar lowest energy structure computed with DFT is not the lowest energy structure computed at the DLPN0-CCSD(T) level of theory. The computed thermal population indicates that the 2D elongated hexagon configuration strongly dominates at a temperature range of 50-800 K. Based on the thermal population, at a temperature of 100 K, the computed IR Boltzmann spectrum agrees with the experimental IR spectrum. The chemical bonding analysis on the lowest energy structure indicates that the cluster bond is due only to the electrons of the 6 s orbital, and the Au d orbitals do not participate in the bonding of this system.

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Wet scrubbing technology is an effective emission control technology for marine diesel engines. Nitric oxide (NO) is one of the main component of ship emissions, the sodium persulfate (Na2S2O8) can facilitate the NO mass transfer process to a rapid reaction. Falling film reactors are widely used in rapid gas-liquid reactions, however, the reaction characteristics of denitrification using Na2S2O8 solution in a falling film reactor are not clear, which were investigated in this paper. The factors of NO mass transfer flux were tested with the liquid-gas ratio of 15 L/m3. The effects of solution properties and temperatures on the reaction driving force were studied by calculating the chemical reaction equilibrium constants and Gibbs free energy changes. The results showed that the NO mass transfer flux increased with the increase of temperature, Na2S2O8 concentration, O2 concentration and NO concentration. NO mass transfer flux increased by 41.00% and then decreased by 2.12% as the pH value increased from 7 to 10 and then rising to 12. The Gibbs free energy changes of alkaline solutions were 114.22%-130.99% lower than those of acidic solution at 303-343 K, and the chemical reaction equilibrium constants were higher. Na2S2O8/seawater system has great application potential in marine exhaust gas purification.

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Regioselectivity and the molecular mechanism of the [3+2] cycloaddition reaction between nitro-substituted formonitrile N-oxide 1 and electron-rich alkenes were explored on the basis of the wb97xd/6-311+G(d) (PCM) quantum chemical calculations. It was established that the thermodynamic factors allow for the formation of stable cycloadducts along all considered models. The analysis of the kinetic parameters of the main processes show that all [3+2] cycloadditions should be realized with full regioselectivity. In all cases, the formation of 5-substituted 3-nitro-2-isoxazolidines is clearly preferred. It is interesting that regiodirection is not determined by the local electrophile/nucleophile interactions but by steric effects. From a mechanistic point of view, all considered reactions should be treated as polar, one-step reactions. All attempts to locate the hypothetical zwitterionic intermediates along the cycloaddition paths were, however, not successful.

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Cyclodextrins are macrocyclic rings composed of glucose residues. Due to their remarkable structural properties, they can form host-guest inclusion complexes, which is why they are frequently used in the pharmaceutical, cosmetic, and food industries, as well as in environmental and analytical chemistry. This review presents the reports from 2011 to 2023 on the quantitative structure-activity/property relationship (QSAR/QSPR) approach, which is primarily employed to predict the thermodynamic stability of inclusion complexes. This article extensively discusses the significant developments related to the size of available experimental data, the available sets of descriptors, and the machine learning (ML) algorithms used, such as support vector machines, random forests, artificial neural networks, and gradient boosting. As QSAR/QPR analysis only requires molecular structures of guests and experimental values of stability constants, this approach may be particularly useful for predicting these values for complexes with randomly substituted cyclodextrins, as well as for estimating their dependence on pH. This work proposes solutions on how to effectively use this knowledge, which is especially important for researchers who will deal with this topic in the future. This review also presents other applications of ML in relation to CD complexes, including the prediction of physicochemical properties of CD complexes, the development of analytical methods based on complexation with CDs, and the optimisation of experimental conditions for the preparation of the complexes.

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Introduction: Neuroglioma, a classification encompassing tumors arising from glial cells, exhibits variable aggressiveness and depends on tumor grade and stage. Unraveling the EGFR gene alterations, including amplifications (unaltered), deletions, and missense mutations (altered), is emerging in glioma. However, the precise understanding of emerging EGFR mutations and their role in neuroglioma remains limited. This study aims to identify specific EGFR mutations prevalent in neuroglioma patients and investigate their potential as therapeutic targets using FDA-approved drugs for repurposing approach. Methods: Neuroglioma patient's data were analyzed to identify the various mutations and survival rates. High throughput virtual screening (HTVS) of FDA-approved (1615) drugs using molecular docking and simulation was executed to determine the potential hits. Results: Neuroglioma patient samples (n=4251) analysis reveals 19% EGFR alterations with most missense mutations at V774M in exon 19. The Kaplan-Meier plots show that the overall survival rate was higher in the unaltered group than in the altered group. Docking studies resulted the best hits based on each target's higher docking score, minimum free energy (MMGBSA), minimum kd, ki, and IC50 values. MD simulations and their trajectories show that compounds ZINC000011679756 target unaltered EGFR and ZINC000003978005 targets altered EGFR, whereas ZINC000012503187 (Conivaptan, Benzazepine) and ZINC000068153186 (Dabrafenib, aminopyrimidine) target both the EGFRs. The shortlisted compounds demonstrate favorable residual interactions with their respective targets, forming highly stable complexes. Moreover, these shortlisted compounds have drug- like properties as assessed by ADMET profiling. Conclusion: Therefore, compounds (ZINC000012503187 and ZINC000068153186) can effectively target both the unaltered/altered EGFRs as multi-target therapeutic repurposing drugs towards neuroglioma.

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The term "desolvation inability" is proposed in order to describe the alteration of the original chemical structure of a solute ("decomposition") prior to the solvent's full removal upon the heating of the solvate. This behavior has been sporadically reported; however, it is much more frequent, and it is the basis of various, seemingly unrelated, effects/processes, e.g., the vinegar syndrome of cellulose acetate cinematographic films, in thermal energy storage. An explanation and a criterion/index for the prediction of this behavior are provided based on the comparison of the Gibbs free energies of decomposition and desolvation. A new approach for the expression of the Gibbs free energy of desolvation is proposed by reversing the roles of the solute and solvent and by regarding water as the solute rather than as the solvent, while the solute is treated as a solid solvent. This approach results in lower solvation/desolvation Gibbs free energy values. Based on the above, the experimentally observed thermal behavior of three inorganic hydrates is predicted and explained. Theoretically and experimentally, it is supported that decomposition is possible at sub-zero (°C) temperatures and the regarded simultaneous drying and protection of heat-sensitive substances by freeze-drying, at least in some cases, e.g., for the case of gallic acid, is an unverified myth.

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This study demonstrates the substantial role of bicarbonate within a zero-valent iron (ZVI) system in hydrogen evolution, demonstrating that heightened concentration levels notably enhance hydrogen output. The acetic acid performance production of seven different inocula was examined when exposed to ZVI and CO2 as the sole carbon source, separately. Along the seven inocula, river and constructed wetland sludges show the highest production rates at 300 mg/L day-1 and 269 mg/L day-1, respectively. Acetobacterium levels significantly rose in CO2-enriched environments, particularly in river and wetland sludges. Moreover, bacteria attached to ZVI showed accelerated hydrogen consumption and acetic acid production compared to their freely suspended or ZVI-detached counterparts when hydrogen was primarily added externally. This study highlighted the positive effect of high concentrations of soluble CO2, which acted both as a reactant with ZVI for hydrogen production and as a substrate for homoacetogens, leading to high acetic acid generation.

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The Arrhenius energy of activation of unfolding Ea unfolding and Gibbs free energy of unfolding ΔG unfolding have been calculated utilizing DSC differential scanning calorimetry for 4 mAbs (1 biosimilar) in 3 formulations. DSC derived ΔTm melting temperature changes for each mAb domain (CH2, Fab, CH3) at calorimetric scan rates at 60 °C, 90 °C, 150 °C and 200 °C / hr. were utilized to calculate the kinetic Eaunfolding. The DSC derived Ea trend with observed aggregate formation and can be used to predict%HMW formation post 9-month storage at 5 °C and 40 °C for all formulations analyzed. Additionally, thermodynamic ΔG unfolding energies were also derived (Tm, ΔCp and ΔH measurements) for each mAb at every scan rate to observe scan rate dependence of ΔG and for extrapolation to 0 °C/hr. (to report ΔG at true equilibrium conditions). Both derived thermodynamic ΔG and kinetic Ea energies were combined to build full energetic landscapes for mAb unfolding and aggregation. Statistical multivariate analysis of kinetic (Ea CH2, Ea Fab, Ea CH3) energies, thermodynamic (ΔG5 °C and ΔG40 °C) energies and in-silico modeled surface properties was also performed. Analysis revealed key significant parameters contributing to aggregation. These parameters were utilized to build predictive aggregation models for 25 mg/mL mAb formulations stored 9-months at 5 °C and 40 °C.

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Plant viruses are the most abundant destructive agents that exist in every ecosystem, causing severe diseases in multiple crops worldwide. Currently, a major gap is present in computational biology determining plant viruses interaction with its host. We lay out a strategy to extract virus-host protein interactions using various protein binding and interface methods for Geminiviridae, a second largest virus family. Using this approach, transcriptional activator protein (TrAP/C2) encoded by Cotton leaf curl Kokhran virus (CLCuKoV) and Cotton leaf curl Multan virus (CLCuMV) showed strong binding affinity with calmodulin-like (CML) protein of Gossypium hirsutum (Gh-CML11). Higher negative value for the change in Gibbs free energy between TrAP and Gh-CML11 indicated strong binding affinity. Consensus from gene ontology database and in-silico nuclear localization signal (NLS) tools identified subcellular localization of TrAP in the nucleus associated with Gh-CML11 for virus infection. Data based on interaction prediction and docking methods present evidences that full length and truncated C2 strongly binds with Gh-CML11. This computational data was further validated with molecular results collected from yeast two-hybrid, bimolecular fluorescence complementation system and pull down assay. In this work, we also show the outcomes of full length and truncated TrAP on plant machinery. This is a first extensive report to delineate a role of CML protein from cotton with begomoviruses encoded transcription activator protein.

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Resistive random access memory (RRAM) holds great promise for in-memory computing, which is considered the most promising strategy for solving the von Neumann bottleneck. However, there are still significant problems in its application due to the non-uniform performance of RRAM devices. In this work, a bilayer dielectric layer memristor was designed based on the difference in the Gibbs free energy of the oxide. We fabricated Au/Ta2O5/HfO2/Ta/Pt (S3) devices with excellent uniformity. Compared with Au/HfO2/Pt (S1) and Au/Ta2O5/Pt (S2) devices, the S3 device has a low reset voltage fluctuation of 2.44%, and the resistive coefficients of variation are 13.12% and 3.84% in HRS and LRS, respectively, over 200 cycles. Otherwise, the bilayer device has better linearity and more conductance states in multi-state regulation. At the same time, we analyze the physical mechanism of the bilayer device and provide a physical model of ion migration. This work provides a new idea for designing and fabricating resistive devices with stable performance.

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Recent studies have highlighted the effectiveness of using antisense oligonucleotides (ASOs) for cellular RNA regulation, including targets that are considered undruggable; however, manually designing optimal ASO sequences can be labor intensive and time consuming, which potentially limits their broader application. To address this challenge, we introduce a platform, the ASOptimizer, a deep-learning-based framework that efficiently designs ASOs at a low cost. This platform not only selects the most efficient mRNA target sites but also optimizes the chemical modifications for enhanced performance. Indoleamine 2,3-dioxygenase 1 (IDO1) promotes cancer survival by depleting tryptophan and producing kynurenine, leading to immunosuppression through the aryl-hydrocarbon receptor (Ahr) pathway within the tumor microenvironment. We used ASOptimizer to identify ASOs that target IDO1 mRNA as potential cancer therapeutics. Our methodology consists of two stages: sequence engineering and chemical engineering. During the sequence-engineering stage, we optimized and predicted ASO sequences that could target IDO1 mRNA efficiently. In the chemical-engineering stage, we further refined these ASOs to enhance their inhibitory activity while reducing their potential cytotoxicity. In conclusion, our research demonstrates the potential of ASOptimizer for identifying ASOs with improved efficacy and safety.

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The Langmuir isotherm, originally developed to study the adsorption of gases, has been modified in this research to investigate the adsorption of solutes in the solution phase. The modification considers the adsorption of solvent molecules and the interactions between adsorbed particles and the species in the solution. Three equations have been obtained to calculate the contribution of these additional effects on the Gibbs free energy, enthalpy, and entropy of solute adsorption based on the new isotherm. The study evaluated the efficiency of the new isotherm in the adsorption of some metal ions in an aqueous solution and found that it is more accurate than the Langmuir isotherm and provides a deeper insight into the adsorption process in the solution phase. PRACTITIONER POINTS: Modification of the Langmuir isotherm for adsorption in solution. Comparison of the efficiency of the Langmuir and modified Langmuir isotherms. Accurate determination of ∆ H ads o $$ \Delta {H}_{ads}^o $$ and ∆ S ads o $$ \Delta {S}_{ads}^o $$ for Pb(II), Cd(II), and Ni(II) adsorption.

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Protein stability is a critical aspect of molecular biology and biochemistry, hinges on an intricate balance of thermodynamic and structural factors. Determining protein stability is crucial for understanding and manipulating biological machineries, as it directly correlated with the protein function. Thus, this study delves into the intricacies of protein stability, highlighting its dependence on various factors, including thermodynamics, thermal conditions, and structural properties. Moreover, a notable focus is placed on the free energy change of unfolding (ΔGunfolding), change in heat capacity (ΔCp) with protein structural transition, melting temperature (Tm) and number of disulfide bonds, which are critical parameters in understanding protein stability. In this study, a machine learning (ML) predictive model was developed to estimate these four parameters using the primary sequence of the protein. The shortfall of available tools for protein stability prediction based on multiple parameters propelled the completion of this study. Convolutional Neural Network (CNN) with multiple layers was adopted to develop a more reliable ML model. Individual predictive models were prepared for each property, and all the prepared models showed results with high accuracy. The R2 (coefficient of determination) of these models were 0.79, 0.78, 0.92 and 0.92, respectively, for ΔG, ΔCp, Tm and disulfide bonds. A case study on stability analysis of two homologous proteins was presented to validate the results predicted through the developed model. The case study included in silico analysis of protein stability using molecular docking and molecular dynamic simulations. This validation study assured the accuracy of each model in predicting the stability associated properties. The alignment of physics-based principles with ML models has provided an opportunity to develop a fast machine learning solution to replace the computationally demanding physics-based calculations used to determine protein stability. Furthermore, this work provided valuable insights into the impact of mutation on protein stability, which has implications for the field of protein engineering. The source codes are available at https://github.com/Growdeatechnology.

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We study Einstein's gravity coupled to nonlinear electrodynamics with two parameters in anti-de Sitter spacetime. Magnetically charged black holes in an extended phase space are investigated. We obtain the mass and metric functions and the asymptotic and corrections to the Reissner-Nordström metric function when the cosmological constant vanishes. The first law of black hole thermodynamics in an extended phase space is formulated and the magnetic potential and the thermodynamic conjugate to the coupling are obtained. We prove the generalized Smarr relation. The heat capacity and the Gibbs free energy are computed and the phase transitions are studied. It is shown that the electric fields of charged objects at the origin and the electrostatic self-energy are finite within the nonlinear electrodynamics proposed.

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Dechlorination is one of the main processes for the natural degradation of polychlorinated biphenyls (PCBs) in an anaerobic environment. However, PCB dechlorination pathways and products vary with PCB congeners, types of functional dechlorinating bacteria, and environmental conditions. The present study develops a novel model for determining dechlorination pathways and fluxes by tracking redox potential variability, transforming the complex dechlorination process into a stepwise sequence. The redox potential is calculated via the Gibbs free energy of formation, PCB concentrations in reactants and products, and environmental conditions. Thus, the continuous change in the PCB congener composition can be tracked during dechlorination processes. The new model is assessed against four measurements from several published studies on PCB dechlorination. The simulation errors in all four measurements are calculated between 2.67 and 35.1% under minimum (n = 0) and maximum (n = 34) numbers of co-eluters, respectively. The dechlorination fluxes for para-dechlorination pathways dominate PCB dechlorination in all measurements. Furthermore, the model also considers multiple-step dechlorination pathways containing intermediate PCB congeners absent in both the reactants and the products. The present study indicates that redox potential might be an appropriate indicator for predicting PCB dechlorination pathways and fluxes even without prior knowledge of the functional dechlorinating bacteria.

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