Kinetic and thermodynamic-based studies on the interaction mechanism of novel R. roxburghii seed peptides against pancreatic lipase and cholesterol esterase

Pancreatic lipase (PL) and cholesterol esterase (CE) are vital digestive enzymes that regulate lipid digestion. Three bioactive peptides (LFCMH, RIPAGSPF, YFRPR), possessing enzyme inhibitory activities, were identified in the seed proteins of R. roxburghii. It is hypothesized that these peptides could inhibit the activities of these enzymes by binding to their active sites or altering their conformation. The results showed that LFCMH exhibited superior inhibitory activity against these enzymes compared to the other peptides. The inhibition mechanisms of the three peptides were identified as either competitive or mixed, according to inhibition models. Further studies have shown that peptides could bind to the active sites of enzymes, thus affecting their spatial conformation and restricting substrate entry into the active site. Molecular simulation further proved that hydrogen bonds and hydrophobic interactions played a vital role in the binding of peptides to enzymes. This study enriches our understanding of interaction mechanisms of peptides on PL and CE.

Luminescent carbon dots-rooted polysaccharide crosslinked hydrogel adsorbent for sensitive determination and efficient removal of Cu2

In this study, a novel luminescent carbon dot-rooted polysaccharide hydrogel (CDs@CCP hydrogel) was prepared by crosslinking cellulose, chitosan (CS), and polyvinyl alcohol (PVA) for simultaneous fluorescent sensing and adsorption of Cu2+. The crosslinking of these low-cost, polysaccharide polymers greatly enhance the mechanical strength of the composite hydrogel while making the polysaccharide-based adsorbent easy to reuse. This composite hydrogel exhibited an excellent adsorption capacity (124.7 mg∙g-1) for residual Cu2+ in water, as well as a sensitive and selective fluorescence response towards Cu2+ with a good linear relationship (R2 > 0.97) and a low detection limit (LOD) of 0.02 μM. The adsorption isotherms, adsorption kinetics, and thermodynamics studies were also conducted to investigate the adsorption mechanism. This composite hydrogel offers an efficient tool for simultaneous monitoring and treatment of Cu2+ from wastewater.

Insight into the promoted recrystallization and water distribution of bread by removing starch granule - surface and - associated proteins during storage

The influence of starch granule surface proteins (SGSPs) and starch granule-associated proteins (SGAPs) on bread retrogradation was investigated in a reconstituted dough system. The removal of both SGSPs and SGAPs resulted in poor bread qualities, decreasing specific volume and crumb porosity, leading to more baking loss and compact crumb structure. Particularly, removing SGSPs was effective in promoting the bread retrogradation. After 7 days of storage, the hardness of bread without SGSPs showed an increase of 353.34 g than the bread without SGAPs. Proton population and relaxation times exhibited that the absence of SGSPs significantly decreased the content of bound water from 11.51 % to 7.03 %, indicating lower water-holding capacity due to the loosen gelling structure. Compared to the control group, bread without SGSPs accelerated the starch recrystallinity by a reduction in soluble starch content, thereby increasing the retrogradation enthalpy and relative crystallinity through promoting the molecular reassociation in starch.

In vitro investigation of the binding characteristics of dacomitinib to human α 1-acid glycoprotein: Multispectral and computational modeling

Dacomitinib is a highly selective second-generation tyrosine kinase inhibitor that can irreversibly bind to tyrosine kinase and is mainly used in the treatment of lung cancer. The binding characteristics of dacomitinib with human α 1-acid glycoprotein (HAG) were analyzed by multispectral and computational simulation techniques. The fluorescence spectra showed that dacomitinib can quench the fluorescence of HAG by forming the HAG-dacomitinib complex with a molar ratio of 1:1 (static quenching). At the temperature similar to that of the human body, the affinity of dacomitinib to HAG (8.95 × 106 M-1) was much greater than that to BSA (3.39 × 104 M-1), indicating that dacomitinib will give priority to binding onto HAG. Thermodynamics parameters analysis and driving force competition experiments showed that hydrogen bonding and hydrophobic forces were the major sources for keeping the complex of HAG-dacomitinib stable. The experimental outcomes also showed that the binding of dacomitinib can lead to the loosening of the skeleton structure of HAG, which led to a slight change in the secondary structure, and also reduces the hydrophobicity of the microenvironment of Trp and Tyr residues. The binding sites of dacomitinib on HAG and the contribution of key amino acid residues to the binding reaction were determined by molecular docking and molecular dynamics (MD) simulation. In addition, it was found that there was a synergistic effect between dacomitinib and Mg2+ and Co2+ ions. Mg2+ and Co2+ could increase the Kb of dacomitinib to HAG and prolong the half-life of dacomitinib.

Probing pharmaceutically important amino acids L-isoleucine and L-tyrosine Solubilities: Unraveling the solvation thermodynamics in diverse mixed solvent systems

The study specifically investigates the solubilities of L-isoleucine and L-tyrosine in water-mixed solvent systems (DMF, DMSO, and ACN), exploring the behaviour of amino acids in complex environments. The experimental methods prioritize meticulous solvent purification to ensure reliable results. The work explores solubility data, uncovering temperature-dependent trends and intricate interactions influencing solubility in the chosen mixed solvent systems. The study emphasizes the impact of thermodynamic properties, solvent-solvent interactions, and amino acid structure on solubility patterns. The broader implications highlight the relevance of understanding amino acid behaviour in diverse solvent environments, offering potential applications in cosmetics and pharmaceutical industries. The distinct solubility patterns contribute valuable insights, enhancing on the understanding of the solution stability and interactions of L-isoleucine and L-tyrosine in different solvent systems. In conclusion, work suggests the enhanced utilization of L-isoleucine and L-tyrosine in various industries, driven by a profound understanding of their solubility in mixed solvent systems. The research expands our knowledge of amino acid behaviour, paving the way for advancements in industries relying on protein-based products and technologies.

Thermal characterization and separation of whey proteins by differential scanning calorimetry

Most commercially available whey products contain a mixture of 6-7 whey proteins; however, there is an increased focus on using the individual whey proteins for their unique biological activities. Before extracting individual whey proteins for use, it is important to quantify how much of a particular protein is present in whey mixtures as well as if the protein is still structurally folded. We first characterized the denaturation temperature and enthalpy values for the six purified whey proteins at six pHs (3-9) and under ion chelation using a nano-differential scanning calorimeter (DSC). From the individual protein scans, we determined the optimal condition for detecting all 6 proteins on a single DSC scan was whey in an EDTA MOPs pH 6.7 buffer.

Design and analysis of self-priming extension DNA hairpin probe for miRNA detection based on a unified dynamic programming framework

MicroRNAs (miRNAs) are potential biomarkers for cancer diagnosis and prognosis, methods for detecting miRNAs with high sensitivity, selectivity, and stability are urgently needed. Various nucleic acid probes that have traditionally been for this purpose suffer several drawbacks, including inefficient signal-to-noise ratios and intensities, high cost, and time-consuming method establishment. Computing tools used for investigating the thermodynamics of DNA hybridization reactions can accurately predict the secondary structure of DNA and the interactions between DNA molecules. Herein, NUPACK was used to design a series of nucleic acid probes and develop a phosphorothioated-terminal hairpin formation and self-priming extension (PS-THSP) signal amplification strategy, which enabled the ultrasensitive detection of miR-200a in serum samples. The free and binding energies of the DNA detection probes calculated using NUPACK, as well as the biological experimental results, were considered synthetically to select the best sequence and experimental conditions. A unified dynamic programming framework, NUPACK analysis and the experimental data, were complementary and improved the designed model in all respects. Our study demonstrates the feasibility of using computer technology such as NUPACK to simplify the experimental process and provide intuitive results.

Investigating the binding interaction of quinoline yellow with bovine serum albumin and anti-amyloidogenic behavior of ferulic acid on QY-induced BSA fibrils

Protein aggregation induces profound changes in the structure along with the conformation of the protein, and is responsible for the pathogenesis of a number of neurodegenerative conditions such as Huntington's, Creutzfeldt-Jacob, Type II diabetes mellitus, Alzheimer's, etc. Numerous multi-spectroscopic approaches and in-silico experiments were utilized to investigate BSA's biomolecular interaction and aggregation in the presence of quinoline yellow. The present research investigation evaluated the interaction of BSA with the food colorant (QY) at two different pH (7.4 and 2.0). The development of the BSA-QY complex was established with UV visible and fluorescence spectroscopy. The quenching of fluorescence upon the interaction of BSA with QY revealed the static nature of quenching mechanism. The Kb value obtained from our result is 4. 54 × 10-4 M-1. The results from the competitive site marker study infer that quinoline yellow is binding with the sub-domain IB of bovine serum albumin, specifically on site III. Three-dimensional fluorescence and synchronous fluorescence spectroscopy were applied for monitoring the alterations in the microenvironment of BSA upon the addition of quinoline yellow. The results from turbidity and RLS studies showed that higher concentrations of QY (80-400 µM) triggered bovine serum albumin (BSA) aggregation at pH 2.0. At pH 7.4, QY couldn't manage to trigger bovine serum albumin aggregation, perhaps because of the repulsion between negatively charged dye (QY) and anionic bovine serum albumin. The results from far-UV CD, Congo Red, and scanning electron microscopy implicate that the QY-induced aggregates exhibit amyloid fibril-like structures. Molecular docking results revealed that hydrophobic interactions, hydrogen bonding, and Pi-Sulfur interactions contribute to QY-induced aggregation of BSA. Further, the amyloid inhibitory potential of ferulic acid (FA), a phenolic acid on QY-induced aggregation of BSA, has also been assessed. The QY-induced amyloid fibrils are FA-soluble, as confirmed by turbidity, RLS, and far-UV CD studies. Far-UV CD results showed that FA retains α helix and inhibits cross β sheet formation when the BSA samples were pre-incubated with increasing concentrations of FA (0-500 µM). Our findings conclude that QY dye successfully stimulates BSA aggregation, but ferulic acid inhibits QY-induced aggregation of BSA. Thus, FA can serve as a therapeutic agent and can help in the treatment of various amyloid-related conditions.

Thermodynamic and functional changes of alpha-chymotrypsin after interaction with gallic acid

In the present study, the interaction mechanism between gallic acid (GA) and α-Chymotrypsin (α-CT) was investigated by employing a series ofspectroscopic methods, computational docking and molecular dynamic (MD) simulation. Fluorescence spectra analysis indicated the formation of a stable complex between GA and α-CT, where the quenching of the fluorescence emission was predominantly characterized by a static mechanism. TheCA obtained binding constants for the α-CT-GA complex were in the order of 103 M-1, indicating the moderate binding affinity of GA for α-CT. The corresponding CD findings showed that the interaction between GA and α-CT resulted in an alteration of the protein's secondary structure. The findings of the enzyme activity investigation clearly showed that the presence of GA led to a notable decline in the enzymatic activity of α-CT, highlighting GA's function as an effective inhibitor for α-CT. The molecular docking simulations revealed the optimal binding site for the GA molecule within the α-CT structure and MD simulations confirmed the stability of the α-CT-GA complex. This research expands our comprehension regarding the behavior of enzymes in the presence of small-molecule ligands and opens avenues for food safety.

Modulation of protein-ligand interactions in the presence of ZIF-8: Spectroscopy and molecular dynamics simulation

In this paper, we investigated the protein-ligand interactions in the presence of ZIF-8 using multi-spectroscopic approaches and molecular dynamics simulation. Fluorescence experiments and molecular docking results showed that ZIF-8 did not change the type of quenching and interaction force between ciprofloxacin (CIP) and human serum albumin (HSA), but made the binding constant of HSA-CIP to be smaller, suggesting that ZIF-8 maybe accelerate the dissociation of CIP from HSA-CIP complex. Moreover, the effect of ZIF-8 on the physiological function of HSA was explored. Multi-spectroscopic methods revealed that ZIF-8 did not significantly alter the microenvironment of amino acid groups, but cause a slight decrease in the content of α-helical conformation, and a sparse and flexible structure of the protein backbone. These peculiarities might lead to the diminution of HSA's ability to control drugs. In short, ZIF-8 might enhance drug effect due to affecting the binding of drugs to proteins. However, the present study is only a preliminary investigation of the suitability of ZIF-8 as a drug carrier in vitro, and subsequent in vivo experimental studies will be required to further confirm the idea.

Acid insoluble lignin material production by chemical activation of olive endocarps for an efficient furfural adsorption-removal from aqueous solutions

The present work describes a protocol of chemical activation, with acid catalyst, of olive endocarps to obtain acid insoluble lignin-rich materials with high capacities for the adsorption of furfural present in aqueous media. During biomass activation, factors such as acid concentration, reaction time and temperature, solid/liquid ratio and the presence of water extractives strongly affected both the surface characteristics of the treated bioadsorbents and their capacities for furfural retention (percentage increase close to 600% with respect to the crude biomass). Once a treated solid with good adsorbent properties was obtained, the optimal conditions for adsorption were found: stirring speed 80 rpm, temperature 303 K and adsorbent load 7.5 g solid/50 cm3. Kinetic study indicated the pseudo-second order model provided the best fit of the experimental data. At 303 K, the equilibrium adsorption capacities values ranged from 2.27 mg g-1 to 29.29 mg g-1, for initial furfural concentrations between 0.49 g dm-3 and 12.88 g dm-3. Freundlich model presented the best isotherm (R2 = 0.996 and SE = 4.7%) providing KF and n values of 0.115 (mg g-1) (mg dm-3)-n and 0.610, respectively. Since physical interactions predominate in the adsorption of furfural on chemically activated olive endocarps, the furfural removal process could have occurred reversibly on the heterogeneous surface of the bioadsorbents.

Biophysical and structural characterization of tetramethrin serum protein complex and its toxicological implications

Tetramethrin (TMT) is a commonly used insecticide and has a carcinogenic and neurodegenerative effect on humans. The binding mechanism and toxicological implications of TMT to human serum albumin (HSA) were examined in this study employing a combination of biophysical and computational methods indicating moderate binding affinity and potential hepato and renal toxicity. Fluorescence quenching experiments showed that TMT binds to HSA with a moderate affinity, and the binding process was spontaneous and predominantly enthalpy-driven. Circular dichroism spectroscopy revealed that TMT binding did not induce any significant conformational changes in HSA, resulting in no changes in its alpha-helix content. The binding site and modalities of TMT interactions with HSA as computed by molecular docking and molecular dynamics simulations revealed that it binds to Sudlow site II of HSA via hydrophobic interactions through its dimethylcyclopropane carboxylate methyl propanyl group. The structural dynamics of TMT induce proper fit into the binding site creating increased and stabilizing interactions. Additionally, molecular mechanics-Poisson Boltzmann surface area calculations also indicated that non-polar and van der Waals were found to be the major contributors to the high binding free energy of the complex. Quantum mechanics (QM) revealed the conformational energies of the binding confirmation and the degree of deviation from the global minimum energy conformation of TMT. The results of this study provide a comprehensive understanding of the binding mechanism of TMT with HSA, which is important for evaluating the toxicity of this insecticide in humans.

Single-and double transition metal atoms anchored C2N as a high-activity catalyst for CO oxidation: A first-principles study

The oxidation of CO has attracted great interest in recent years due to its important role in enhancing the catalyst durability in fuel cells and solving the growing environmental problems caused by CO emissions. Consequently, the catalytic oxidation of CO at double non-noble metal atoms anchored C2N is investigated using density functional theory (DFT) computations. All the screened Ti@C2N and Ti2@C2N are thermodynamically stable based on their binding energy calculations. The electronic characteristics, the natural bond orbital analyses (NBO), Frontier orbital, statistical thermodynamics, projected densities of states (PDOS) characteristics, non-covalent interactions (NCI), and quantum theory of atoms in molecules (QTAIM) descriptors of these systems have been examined to analyze the interaction process. Our comparative study suggested that the newly predicted double-atom catalyst (Ti2@C2N) is highly active for CO oxidation, which is a useful guideline for further development. The calculated static first-order hyperpolarizability (βo) illustrated that the double-atom catalyst under investigation can be considered a potential candidate for non-linear optical behavior and could be used for NLO applications. CO oxidation on Ti2@C2N along the Eley-Rideal (ER) mechanism with a low energy barrier of 0.16 eV, which is smaller than the maximum energy barrier (0.73 eV) of CO oxidation along the Langmuir-Hinshelwood (LH) mechanism. Consequently, the ER mechanism is more favorable both thermodynamically and dynamically. This work can provide useful insights and guidelines for future theoretical and experimental investigations to promote the design and development of highly effective and low-cost non-precious-metal Ti2@C2N nanocatalysts towards CO oxidation at ambient temperature.

Responses to commentaries on "The gene: An appraisal"

The central conclusions of "The Gene: An Appraisal" are that genetic variance does not underpin biological evolution, and, therefore, that genes are not Mendel's units of inheritance. In this response, I will address the criticisms I have received via commentaries on that paper by defending the following statements: 1. Epistasis does not explain the power-law fitness profile of the Long-Term Evolution Experiment (LTEE). The data from the evolution of natural systems displays the power-law form ubiquitously. Epistasis plays no role in evolution. 2. The common characteristics of living things (natural systems) are described by the principle of least action in de Maupertuis's original form, which is synonymous with the 2nd law of thermodynamics and Newton's 2nd law of motion in its complete form, i.e., F = dp/dt. Organisms strive to achieve free energy balance with their environments. 3. Based on an appraisal of the scientific environment between 1880 and 1911, I conclude that Johannsen's genotype conception was perhaps, the only option available to him. 4. The power-law fitness profile of the LTEE falsifies Fisher's Genetical Theory of Natural Selection, Johannsen's genotype conception, and the idea that 'Darwinian evolution' is an exception to the generic thermodynamic process of evolution in natural systems. 5. The use of the technique of genome-wide association to identify the causes and the likelihoods of inherited common diseases and behavioural traits is a 'wild goose chase' because genes are not the units of inheritance.

Integrating biosorption and machine learning for efficient remazol red removal by algae-bacteria co-culture and comparative analysis of predicted models

This research investigates into the efficacy of algae and algae-bacteria symbiosis (ABS) in efficiently decolorizing Remazol Red 5B, a prevalent dye pollutant. The investigation encompasses an exploration of the biosorption isotherm and kinetics governing the dye removal process. Additionally, various machine learning models are employed to predict the efficiency of dye removal within a co-culture system. The results demonstrate that both Desmodesmus abundans and a composite of Desmodesmus abundans and Rhodococcus pyridinivorans exhibit significant dye removal percentages of 75 ± 1% and 78 ± 1%, respectively, after 40 min. The biosorption isotherm analysis reveals a significant interaction between the adsorbate and the biosorbent, and it indicates that the Temkin model best matches the experimental data. Moreover, the Langmuir model indicates a relatively high biosorption capacity, further highlighting the potential of the algae-bacteria composite as an efficient adsorbent. Decision Trees, Random Forest, Support Vector Regression, and Artificial Neural Networks are evaluated for predicting dye removal efficiency. The Random Forest model emerges as the most accurate, exhibiting an R2 value of 0.98, while Support Vector Regression and Artificial Neural Networks also demonstrate robust predictive capabilities. This study contributes to the advancement of sustainable dye removal strategies and encourages future exploration of hybrid approaches to further enhance predictive accuracy and efficiency in wastewater treatment processes.

Sequence-dependent and -independent information in a combined random energy model for protein folding and coding

Random energy models (REMs) provide a simple description of the energy landscapes that guide protein folding and evolution. The requirement of a large energy gap between the native structure and unfolded conformations, considered necessary for cooperative, protein-like, folding behavior, indicates that proteins differ markedly from random heteropolymers. It has been suggested, therefore, that natural selection might have acted to choose nonrandom amino acid sequences satisfying this particular condition, implying that a large fraction of possible, unselected random sequences, would not fold to any structure. From an informational perspective, however, this scenario could indicate that protein structures, regarded as messages to be transmitted through a communication channel, would not be efficiently encoded in amino acid sequences, regarded as the communication channel for this transmission, since a large fraction of possible channel states would not be used. Here, we use a combined REM for conformations and sequences, with previously estimated parameters for natural proteins, to explore an alternative possibility in which the appropriate shape of the landscape results mainly from the deviation from randomness of possible native structures instead of sequences. We observe that this situation emerges naturally if the distribution of conformational energies happens to arise from two independent contributions corresponding to sequence-dependent and -independent terms. This construction is consistent with the hypothesis of a protein burial folding code, with native structures being determined by a modest amount of sequence-dependent atomic burial information with sequence-independent constraints imposed by unspecific hydrogen bond formation. More generally, an appropriate combination of sequence-dependent and -independent information accommodates the possibility of an efficient structural encoding with the main physical requirement for folding, providing possible insight not only on the folding process but also on several aspects sequence evolution such as neutral networks, conformational coverage, and de novo gene emergence.

Microalgae derived honeycomb structured mesoporous diatom biosilica for adsorption of malachite green: Process optimization and modeling

The present study investigated the removal of malachite green dye from aquifers by means of microalgae-derived mesoporous diatom biosilica. The various process variables (dye concentration, pH, and adsorbent dose) influencing the removal of the dye were optimized and their interactive effects on the removal efficiency were explored by response surface methodology. The pH of the solution (pH = 5.26) was found to be the most dominating among other tested variables. The Langmuir isotherm (R2 = 0.995) best fitted the equilibrium adsorption data with an adsorption capacity of 40.7 mg/g at 323 K and pseudo-second-order model (R2 = 0.983) best elucidated the rate of dye removal (10.6 mg/g). The underlying mechanism of adsorption was investigated by Weber-Morris and Boyd models and results revealed that the film diffusion governed the overall adsorption process. The theoretical investigations on the dye structure using DFT-based chemical reactivity descriptors indicated that malachite green cations are electrophilic, reactive and possess the ability to accept electrons, and are strongly adsorbed on the surface of diatom biosilica. Also, the Fukui function analysis proposed the favorable adsorption sites available on the adsorbent surface.

Probing the toxic effect of chlorpyrifos as an environmental pollutant on the structure and biological activity of lysozyme under physiological conditions

The pervasive use of pesticides like chlorpyrifos (CPY) has been associated with deleterious effects on biomolecules, posing significant risks to environmental integrity, public health, and overall ecosystem equilibrium. Accordingly, in this study, we investigated the potential binding interaction between the well-conserved enzyme, lysozyme (LSZ), and CPY through various spectroscopic techniques and molecular modeling. The UV-vis absorption and fluorescence experiments confirmed the complex formation and static quenching of the intrinsic fluorescence intensity. LSZ revealed a singular binding site for CPY, with binding constants around 105 M-1 across different temperature ranges. Analysis of thermodynamic parameters showed the spontaneous nature of the complexation process, while also revealing the pivotal role of hydrophobic interactions in stabilizing the LSZ-CPY system. According to circular dichroism and Fourier transform infrared studies, CPY binding changed the secondary structure of LSZ by boosting α-helix presence and reducing the levels of β-sheet and β-turn content. Further, CPY decreased the stability and activity of LSZ. Computational docking delineated the specific and highly preferred binding site of CPY within the structure of LSZ. Molecular dynamic simulation indicated the enduring stability of the LSZ/CPY complex and revealed structural modifications in the LSZ after binding with CPY. This research provides a detailed understanding of the intermolecular dynamics between CPY and LSZ, concurrently elucidating the molecular-level implications for the potential hazards of pesticides in the natural environment.

Oxidation pathways and kinetics of the 1,1,2,3-tetrafluoropropene (CF2CF-CH2F) reaction with Cl-atoms and subsequent aerial degradation of its product radicals in the presence of NO

To give a comprehensive account of the environmental acceptability of 1,1,2,3-tetrafluoropropene (CF2CF-CH2F) in the troposphere, we have examined the oxidation reaction pathways and kinetics of CF2CF-CH2F initiated by Cl-atoms using the second-order Møller-Plesset perturbation (MP2) theory along with the 6-31+G(d,p) basis set. We also performed single-point energy calculations to further refine the energies at the CCSD(T) level along with the basis sets 6-31+G(d,p) and 6-311++G(d,p). The estimation of the relative energies and thermodynamic parameters of the CF2CF-CH2F + Cl reaction clearly shows that Cl-atom addition reaction pathways are more dominant compared to H-abstraction reaction pathways. The value of the rate coefficient for each reaction channel is calculated using the conventional transition state theory (TST) over the temperature range of 200-1000 K at 1 atm. The estimated overall rate coefficients for the title reaction are found to be 1.10 × 10-12, 1.21 × 10-10, and 1.13 × 10-8 cm3 per molecule per s via the respective calculation methods viz. MP2/6-31+G(d,p), CCSD(T)//MP2/6-31+G(d,p), and CCSD(T)/6-311++G(d,p)//MP2/6-31+G(d,p), at 298.15 K. Moreover, the calculated rate coefficients and percentage branching ratio values suggest that the Cl-atom addition reaction at the β-carbon atom is more preferable to that of the α-carbon addition to CF2CF-CH2F. Based on the rate coefficient values calculated by the three different methods, the atmospheric lifetime for the title reaction at 298.15 K is estimated. The radiative efficiency (RE) and Global Warming Potential (GWP) results of the title molecule show that its GWP would be negligible. Further, we have explored the degradation of its product radicals in the presence of O2 and NO. From the degradation results, we have found that CF2(Cl)COF, FCOCH2F, FCFO and FCOCl are formed as stable end products along with various radicals such as ˙CF2Cl and ˙CH2F. Therefore, these findings of kinetic and mechanistic data can be applied to the development and implementation of a novel CFC replacement.

Sorting drug conformers in enzyme active sites: the XTB way

In the present study, we have used the MEI196 set of interaction energies to investigate low-cost computational chemistry approaches for the calculation of binding between a molecule and its environment. Density functional theory (DFT) methods, when used with the vDZP basis set, yield good agreement with the reference energies. On the other hand, semi-empirical methods are less accurate as expected. By examining different groups of systems within MEI196 that contain species of a similar nature, we find that chemical similarity leads to cancellation of errors in the calculation of relative binding energies. Importantly, the semi-empirical method GFN1-xTB (XTB1) yields reasonable results for this purpose. We have thus further assessed the performance of XTB1 for calculating relative energies of docking poses of substrates in enzyme active sites represented by cluster models or within the ONIOM protocol. The results support the observations on error cancellation. This paves the way for the use of XTB1 in parts of large-scale virtual screening workflows to accelerate the drug discovery process.

Gaussian accelerated molecular dynamics simulations facilitate prediction of the permeability of cyclic peptides

Despite their widespread use as therapeutics, clinical development of small molecule drugs remains challenging. Among the many parameters that undergo optimization during the drug development process, increasing passive cell permeability (i.e., log(P)) can have some of the largest impact on potency. Cyclic peptides (CPs) have emerged as a viable alternative to small molecules, as they retain many of the advantages of small molecules (oral availability, target specificity) while being highly effective at traversing the plasma membrane. However, the relationship between the dominant conformations that typify CPs in an aqueous versus a membrane environment and cell permeability remain poorly characterized. In this study, we have used Gaussian accelerated molecular dynamics (GaMD) simulations to characterize the effect of solvent on the free energy landscape of lariat peptides, a subset of CPs that have recently shown potential for drug development (Kelly et al., JACS 2021). Differences in the free energy of lariat peptides as a function of solvent can be used to predict permeability of these molecules, and our results show that permeability is most greatly influenced by N-methylation and exposure to solvent. Our approach lays the groundwork for using GaMD as a way to virtually screen large libraries of CPs and drive forward development of CP-based therapeutics.

ESPDHot: An Effective Machine Learning-Based Approach for Predicting Protein-DNA Interaction Hotspots

Protein-DNA interactions are pivotal to various cellular processes. Precise identification of the hotspot residues for protein-DNA interactions holds great significance for revealing the intricate mechanisms in protein-DNA recognition and for providing essential guidance for protein engineering. Aiming at protein-DNA interaction hotspots, this work introduces an effective prediction method, ESPDHot based on a stacked ensemble machine learning framework. Here, the interface residue whose mutation leads to a binding free energy change (ΔΔG) exceeding 2 kcal/mol is defined as a hotspot. To tackle the imbalanced data set issue, the adaptive synthetic sampling (ADASYN), an oversampling technique, is adopted to synthetically generate new minority samples, thereby rectifying data imbalance. As for molecular characteristics, besides traditional features, we introduce three new characteristic types including residue interface preference proposed by us, residue fluctuation dynamics characteristics, and coevolutionary features. Combining the Boruta method with our previously developed Random Grouping strategy, we obtained an optimal set of features. Finally, a stacking classifier is constructed to output prediction results, which integrates three classical predictors, Support Vector Machine (SVM), XGBoost, and Artificial Neural Network (ANN) as the first layer, and Logistic Regression (LR) algorithm as the second one. Notably, ESPDHot outperforms the current state-of-the-art predictors, achieving superior performance on the independent test data set, with F1, MCC, and AUC reaching 0.571, 0.516, and 0.870, respectively.

Modeling Zinc Complexes Using Neural Networks

Understanding the energetic landscapes of large molecules is necessary for the study of chemical and biological systems. Recently, deep learning has greatly accelerated the development of models based on quantum chemistry, making it possible to build potential energy surfaces and explore chemical space. However, most of this work has focused on organic molecules due to the simplicity of their electronic structures as well as the availability of data sets. In this work, we build a deep learning architecture to model the energetics of zinc organometallic complexes. To achieve this, we have compiled a configurationally and conformationally diverse data set of zinc complexes using metadynamics to overcome the limitations of traditional sampling methods. In terms of the neural network potentials, our results indicate that for zinc complexes, partial charges play an important role in modeling the long-range interactions with a neural network. Our developed model outperforms semiempirical methods in predicting the relative energy of zinc conformers, yielding a mean absolute error (MAE) of 1.32 kcal/mol with reference to the double-hybrid PWPB95 method.

Why Does the E1219V Mutation Expand T-Rich PAM Recognition in Cas9 from Streptococcus pyogenes?

Popular RNA-guided DNA endonuclease Cas9 from Streptococcus pyogenes (SpCas9) recognizes the canonical 5'-NGG-3' protospacer adjacent motif (PAM) and triggers double-stranded DNA cleavage activity. Mutations in SpCas9 were demonstrated to expand the PAM readability and hold promise for therapeutic and genome editing applications. However, the energetics of the PAM recognition and its relation to the atomic structure remain unknown. Using the X-ray structure (precatalytic SpCas9:sgRNA:dsDNA) as a template, we calculated the change in the PAM binding affinity in response to SpCas9 mutations using computer simulations. The E1219V mutation in SpCas9 fine-tunes the water accessibility in the PAM binding pocket and promotes new interactions in the SpCas9:noncanonical T-rich PAM, thus weakening the PAM stringency. The nucleotide-specific interaction of two arginine residues (i.e., R1333 and R1335 of SpCas9) ensured stringent 5'-NGG-3' PAM recognition. R1335A substitution (SpCas9R1335A) completely disrupts the direct interaction between SpCas9 and PAM sequences (canonical or noncanonical), accounting for the loss of editing activity. Interestingly, the double mutant (SpCas9R1335A,E1219V) boosts DNA binding affinity by favoring protein:PAM electrostatic contact in a desolvated pocket. The underlying thermodynamics explain the varied DNA cleavage activity of SpCas9 variants. A direct link between the energetics, structures, and activity is highlighted, which can aid in the rational design of improved SpCas9-based genome editing tools.

Nonbonded Force Field Parameters from MBIS Partitioning of the Molecular Electron Density Improve Binding Affinity Predictions of the T4-Lysozyme Double Mutant

The use of computer simulation for binding affinity prediction is growing in drug discovery. However, its wider use is constrained by the accuracy of the free energy calculations. The key sources of error are the force fields used to depict molecular interactions and insufficient sampling of the configurational space. To improve the quality of the force field, we developed a Python-based computational workflow. The workflow described here uses the minimal basis iterative stockholder (MBIS) method to determine atomic charges and Lennard-Jones parameters from the polarized molecular density. This is done by performing electronic structure calculations on various configurations of the ligand when it is both bound and unbound. In addition, we validated a simulation procedure that accounts for the protein and ligand degrees of freedom to precisely calculate binding free energies. This was achieved by comparing the self-adjusted mixture sampling and nonequilibrium thermodynamic integration methods using various protein and ligand conformations. The accuracy of predicting binding affinity is improved by using MBIS-derived force field parameters and a validated simulation procedure. This improvement surpasses the chemical precision for the eight aromatic ligands, reaching a root-mean-square error of 0.7 kcal/mol.

Exploring Molecular Energy Landscapes by Coupling the DFTB Potential with a Tree-Based Stochastic Algorithm: Investigation of the Conformational Diversity of Phthalates

Exploring the global energy landscape of relatively large molecules at the quantum level is a challenging problem. In this work, we report the coupling of a nonredundant conformational space exploration method, namely, the robotics-inspired iterative global exploration and local optimization (IGLOO) algorithm, with the quantum-chemical density functional tight binding (DFTB) potential. The application of this fast and efficient computational approach to three close-sized molecules of the phthalate family (DBP, BBP, and DEHP) showed that they present different conformational landscapes. These differences have been rationalized by making use of descriptors based on distances and dihedral angles. Coulomb interactions, steric hindrance, and dispersive interactions have been found to drive the geometric properties. A strong correlation has been evidenced between the two dihedral angles describing the side-chain orientation of the phthalate molecules. Our approach identifies low-energy minima without prior knowledge of the potential energy surface, paving the way for future investigations into transition paths and states.

Binding Sites of Bicarbonate in Phosphoenolpyruvate Carboxylase

Phosphoenolpyruvate carboxylase (PEPC) is used in plant metabolism for fruit maturation or seed development as well as in the C4 and crassulacean acid metabolism (CAM) mechanisms in photosynthesis, where it is used for the capture of hydrated CO2 (bicarbonate). To find the yet unknown binding site of bicarbonate in this enzyme, we have first identified putative binding sites with nonequilibrium molecular dynamics simulations and then ranked these sites with alchemical free energy calculations with corrections of computational artifacts. Fourteen pockets where bicarbonate could bind were identified, with three having realistic binding free energies with differences with the experimental value below 1 kcal/mol. One of these pockets is found far from the active site at 14 Å and predicted to be an allosteric binding site. In the two other binding sites, bicarbonate is in direct interaction with the magnesium ion; neither sequence alignment nor the study of mutant K606N allowed to discriminate between these two pockets, and both are good candidates as the binding site of bicarbonate in phosphoenolpyruvate carboxylase.

Helical Content Correlations and Hydration Structures of the Folding Ensemble of the B Domain of Protein A

The B domain of protein A (BdpA), a small three-helix bundle, folds on a time scale of a few microseconds with heterogeneous native and unfolded states. It is widely used as a model for understanding protein folding mechanisms. In this work, we use structure-based models (SBMs) and atomistic simulations to comprehensively investigate how BdpA folding is associated with the formation of its secondary structure. The energy landscape visualization method (ELViM) was used to characterize the pathways that connect the folded and unfolded states of BdpA as well as the sets of structures displaying specific ellipticity patterns. We show that the native state conformational diversity is due mainly to the conformational variability of helix I. Helices I, II, and III occur in a weakly correlated manner, with Spearman's rank correlation coefficients of 0.1539 (I and II), 0.1259 (I and III), and 0.2561 (II and III). These results, therefore, suggest the highest cooperativity between helices II and III. Our results allow the clustering of partially folded structures of folding of the B domain of protein A on the basis of its secondary structure, paving the way to an understanding of environmental factors in the relative stability of the basins of the folding ensemble, which are illustrated by the structural dependency of the protein hydration structures, as computed with minimum-distance distribution functions.

Molecular Insights into the Variability in Infection and Immune Evasion Capabilities of SARS-CoV-2 Variants: A Sequence and Structural Investigation of the RBD Domain

As severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants continuously emerge, an increasing number of mutations are accumulating in the Spike protein receptor-binding domain (RBD) region. Through sequence analysis of various Variants of Concern (VOC), we identified that they predominantly fall within the ο lineage although recent variants introduce any novel mutations in the RBD. Molecular dynamics simulations were employed to compute the binding free energy of these variants with human Angiotensin-converting enzyme 2 (ACE2). Structurally, the binding interface of the ο RBD displays a strong positive charge, complementing the negatively charged binding interface of ACE2, resulting in a significant enhancement in the electrostatic potential energy for the ο variants. Although the increased potential energy is partially offset by the rise in polar solvation free energy, enhanced electrostatic potential contributes to the long-range recognition between the ο variant's RBD and ACE2. We also conducted simulations of glycosylated ACE2-RBD proteins. The newly emerged ο (JN.1) variant has added a glycosylation site at N-354@RBD, which significantly weakened its binding affinity with ACE2. Further, our interaction studies with three monoclonal antibodies across multiple SARS-CoV-2 strains revealed a diminished neutralization efficacy against the ο variants, primarily attributed to the electrostatic repulsion between the antibodies and RBD interface. Considering the characteristics of the ο variant and the trajectory of emerging strains, we propose that newly developed antibodies against SARS-CoV-2 RBD should have surfaces rich in negative potential and, postbinding, exhibit strong van der Waals interactions. These findings provide invaluable guidance for the formulation of future therapeutic strategies.

ELViM: Exploring Biomolecular Energy Landscapes through Multidimensional Visualization

Molecular dynamics (MD) simulations provide a powerful means of exploring the dynamic behavior of biomolecular systems at the atomic level. However, analyzing the vast data sets generated by MD simulations poses significant challenges. This article discusses the energy landscape visualization method (ELViM), a multidimensional reduction technique inspired by the energy landscape theory. ELViM transcends one-dimensional representations, offering a comprehensive analysis of the effective conformational phase space without the need for predefined reaction coordinates. We apply the ELViM to study the folding landscape of the antimicrobial peptide Polybia-MP1, showcasing its versatility in capturing complex biomolecular dynamics. Using dissimilarity matrices and a force-scheme approach, the ELViM provides intuitive visualizations, revealing structural correlations and local conformational signatures. The method is demonstrated to be adaptable, robust, and applicable to various biomolecular systems.

A promising approach for the removal of hexavalent and trivalent chromium from aqueous solution using low-cost biomaterial

Heavy metal pollution is an enduring environmental challenge that calls for sustainable and eco-friendly solutions. One promising approach is to harness discarded plant biomass as a highly efficient environmental friendly adsorbents. In this context, a noteworthy study has spotlighted the employment of Euryale ferox Salisbury seed coat (E.feroxSC) for the exclusion of trivalent and hexavalent chromium ions. This study aims to transform discarded plant residue into a novel, environmentally friendly, and cost-effective alternative adsorbent, offering a compelling alternative to more expensive adsorption methods. By repurposing natural materials, we can contribute to mitigating heavy-metal pollution while promoting sustainable and economically viable solutions in environmental remediation. The effect of different parameters, i.e., chromium ions' initial concentration (5-25 mg L-1), solution pH (2-7), adsorbent dosage (0.2-2.4 g L-1), contact time (20-240 min), and temperature (298-313 K), were investigated. E.feroxSC proved highly effective, achieving 96.5% removal of Cr(III) ions at pH 6 and 97.7% removal of Cr(VI) ions at pH 2, with a maximum biosorption capacity of 18.33 mg/g for Cr(III) and 13.64 mg/g for Cr(VI), making it a promising, eco-friendly adsorbent for tackling heavy-metal pollution. The adsorption process followed the pseudo-second-order kinetic model, aligning well with the Langmuir isotherm, exhibited favorable thermodynamics, and was characterized as feasible, spontaneous, and endothermic with physisorption mechanisms. The investigation revealed that E.feroxSC effectively adsorbed Cr(VI) which could be rejuvenated in a basic solution with minimal depletion in its adsorption capacity. Conversely, E.feroxSC's adsorption of Cr(III) demanded rejuvenation in an acidic milieu, exhibiting comparatively less efficient restoration.

Understanding the Reversible Binding of a Multichain Protein-Protein Complex through Free-Energy Calculations

We demonstrate that the binding affinity of a multichain protein-protein complex, insulin dimer, can be accurately predicted using a streamlined route of standard binding free-energy calculations. We find that chains A and C, which do not interact directly during binding, stabilize the insulin monomer structures and reduce the binding affinity of the two monomers, therefore enabling their reversible association. Notably, we confirm that although classical methods can estimate the binding affinity of the insulin dimer, conventional molecular dynamics, enhanced sampling algorithms, and classical geometrical routes of binding free-energy calculations may not fully capture certain aspects of the role played by the noninteracting chains in the binding dynamics. Therefore, this study not only elucidates the role of noninteracting chains in the reversible binding of the insulin dimer but also offers a methodological guide for investigating the reversible binding of multichain protein-protein complexes utilizing streamlined free-energy calculations.

Fibrillation of Human Serum Albumin Differentially Affected by Asp-, Arg-, and Tyr-Capped Gold Nanoparticles

Fibrillation of proteins is associated with a number of debilitating diseases, including various neurodegenerative disorders. Prevention of the protein fibrillation process is therefore of immense importance. We investigated the effect of amino acid-capped AuNPs on the prevention of the fibrillation process of human serum albumin (HSA), a model protein. Amino acid-capped AuNPs of varying sizes and agglomeration extents were synthesized under physiological conditions. The AuNPs were characterized by their characteristic surface plasmon resonance (SPR), and their interactions with HSA were investigated through emission spectroscopy in addition to circular dichroism (CD) spectral analyses. Fluorescence lifetime imaging (FLIM) as well as transmission electron microscopy (TEM) were used to observe the fibrillar network. Thermodynamic and kinetic analyses from CD and fluorescence emission spectra provided insights into the fibrillation pathway adopted by HSA in the presence of capped AuNPs. Kinetics of the fibrillation pathway followed by ThT fluorescence emission confirmed the sigmoidal nature of the process. The highest cooperativity was observed in the case of Asp-AuNPs with HSA. This was in accordance with the ΔG value obtained from the CD spectral analyses, where Arg-AuNPs with HSA showed the highest positive ΔG value and Asp-AuNPs with HSA showed the most negative ΔG value. The study provides information about the potential use of conjugate AuNPs to monitor the fibrillation process in proteins.

In silico design of misfolding resistant proteins: the role of structural similarity of a competing conformational ensemble in the optimization of frustration

Most state-of-the-art in silico design methods fail due to misfolding of designed sequences to a conformation other than the target. Thus, a method to design misfolding resistant proteins will provide a better understanding of the misfolding phenomenon and will also increase the success rate of in silico design methods. In this work, we optimize the conformational ensemble to be selected for negative design purposes based on the similarity of the conformational ensemble to the target. Five ensembles with different degrees of similarity to the target are created and destabilized and the target is stabilized while designing sequences using mean field theory and Monte Carlo simulation methods. The results suggest that the degree of similarity of the non-native conformations to the target plays a prominent role in designing misfolding resistant protein sequences. The design procedures that destabilize the conformational ensemble with moderate similarity to the target have proven to be more promising. Incorporation of either highly similar or highly dissimilar conformations to the target conformation into the non-native ensemble to be destabilized may lead to sequences with a higher misfolding propensity. This will significantly reduce the conformational space to be considered in any protein design procedure. Interestingly, the results suggest that a sequence with higher frustration in the target structure does not necessarily lead to a misfold prone sequence. A successful design method may purposefully choose a frustrated sequence in the target conformation if that sequence is even more frustrated in the competing non-native conformations.

Structure, Self-Assembly, and Phase Behavior of Neuroactive N-Acyl GABAs: Doxorubicin Encapsulation in NPGABA/DPPC Liposomes and Release Studies

N-Acylated amino acids and neurotransmitters in mammals exert significant biological effects on the nervous system, immune responses, and vasculature. N-Acyl derivatives of γ-aminobutyric acid (N-acyl GABA), which belong to both classes mentioned above, are prominent among them. In this work, a homologous series of N-acyl GABAs bearing saturated N-acyl chains (C8-C18) have been synthesized and characterized with respect to self-assembly, thermotropic phase behavior, and supramolecular organization. Differential scanning calorimetric studies revealed that the transition enthalpies and entropies of N-acyl GABAs are linearly dependent on the acyl chain length. The crystal structure of N-tridecanoyl GABA showed that the molecules are packed in bilayers with the acyl chains aligned parallel to the bilayer normal and that the carboxyl groups from opposite layers associate to form dimeric structures involving strong O-H···O hydrogen bonds. In addition, N-H···O and C-H···O hydrogen bonds between amide moieties of adjacent molecules within each layer stabilize the molecular packing. Powder X-ray diffraction studies showed odd-even alternation in the d spacings, suggesting that the odd chain and even chain compounds pack differently. Equimolar mixtures of N-palmitoyl GABA and dipalmitoylphosphatidylcholine (DPPC) were found to form stable unilamellar vesicles with diameters of ∼300-340 nm, which could encapsulate doxorubicin, an anticancer drug, with higher efficiency and better release characteristics than DPPC liposomes at physiologically relevant pH. These liposomes exhibit faster release of doxorubicin at acidic pH (<7.0), indicating their potential utility as drug carriers in cancer chemotherapy.

Insight into the molecular interaction between the anticancer drug, enzalutamide and human alpha-2-macroglobulin: Biochemical and biophysical approach

The drug pharmacokinetics is affected upon binding with proteins, thus making drug-protein interactions crucial. This study investigated the interaction between enzalutamide and human major antiproteinase alpha-2-macroglobulin (α2M) by using multi spectroscopic and calorimetric techniques. The spectroscopic techniques such as circular dichroism (CD), intrinsic fluorescence, and UV-visible absorption were used to determine the mechanism of enzalutamide-α2M interaction. Studies on the quenching of fluorescence at three different temperatures showed that the enzalutamide-α2M complex is formed through static quenching mechanism. The change in microenvironment around tyrosine residues in protein was detected through synchronised fluorescence. The secondary structure of α2M was slightly altered by enzalutamide according to far UV-CD spectral analysis. Changes in position of amide I band in FTIR spectra further confirm the secondary structural alteration in α2M. According to thermodynamic characteristics such as fluorescence quenching and isothermal titration calorimetry (ITC), hydrogen bonds and hydrophobic interactions were involved in the interaction machanism. The ITC reiterated the exothermic and spontaneous nature of the interaction. The lower proteinase inhibitory activity of the α2M-enzalutamide conjugate as reflects the disruption of the native α2M structure upon interaction with enzalutamide.

Solubility determination and thermodynamic model analysis of L-α-glyceryl phosphorylcholine in different organic solvents of 278.15 K to 323.15 K

L-α-glyceryl phosphorylcholine, also referred to as choline ethanol phosphate and phosphocholine glycerophosphate, is a naturally occurring metabolite of water-soluble phospholipids in animals. This molecular property is important for informing the crystallization and purification of drugs. The solubility of L-α-glyceryl phosphorylcholine was determined in ten pure solvents and three mixed solvents under atmospheric pressure. The experimental results indicate that L-α-glyceryl phosphorylcholine is most soluble in methanol and least soluble in acetone. Additionally, the solubility of L-α-glyceryl phosphorylcholine was found to increase with temperature within the experimental range. Furthermore, the solubility of L-α-glyceryl phosphorylcholine in binary solvents is dependent on the proportion of positive solvent and temperature. The solubility of L-α-glyceryl phosphorylcholine increases with the proportion of positive solvent. XRD and DSC results indicate that the crystal form of L-α-glyceryl phosphorylcholine remains unchanged before and after dissolution in the reagent, and its melting point temperature is 413.15 K. Various models, including the modified Apelblat model, λh model, Jouyban-Acree model, SUN model, and CNIBS/R-K model, were used to fit the solubility data of L-α-glyceryl phosphorylcholine in different solvents. The study found that the modified Apelblat model and CNIBS/R-K model were the most appropriate for fitting the data. The KAT-LSER model was used to analyze the molecular interactions between solvents and solutes, revealing that the solvent step method with non-specific polarity/polarization interaction had the greatest impact on solubility.

Eco-friendly synthesis of clay-chitosan composite for efficient removal of alizarin red S dye from wastewater: A comprehensive experimental and theoretical investigation

Alizarin Red S (ARS) is commonly utilized for dyeing in textile industry. The dye represents a refractory pollutant in the aquatic environment unless properly treated. To tackle this pollutant, the applicability of chitosan-clay composite (3C) for the ARS removal from textile wastewater was studied. Characterization studies were conducted on the synthesized adsorbent using Fourier transformation infrared (FT-IR), X-ray diffraction (XRD), and energy dispersive X-ray (EDX) techniques. Optimized parameters such as adsorbent's dosage, pH, reaction time, and initial concentrations were tested in a batch system. Additionally, density functional theory (DFT) was calculated to understand the adsorption mechanism and the role of benzene rings and oxygen atoms in the ARS as electron donors. At the same initial concentration of 30 mg/L and optimized conditions of 50 mg of dose, pH 2, and 10 min of reaction time, about 86% of ARS removal was achieved using the composite. The pseudo-second-order kinetic was applicable to model a reasonable fitness of the adsorption reaction, while the Temkin model was representative to simulate the reaction with a maximum adsorption capacity of 44.39 mg/g. This result was higher than magnetic chitosan (40.12 mg/g), or pure chitosan (42.48 mg/g). With ΔH = 27.22 kJ/mol and ΔG<0, the data implied the endothermic and spontaneous nature of the adsorption process. Overall, this implies that the clay-chitosan composite is promising to remove target dye from contaminated wastewater.

Thermodynamic and molecular dynamic insights into how fusion influences peptide-tag recognition of an antibody

To understand the effect of protein fusion on the recognition of a peptide-tag by an antibody, we fused a CCR5-derived peptide-tag (pep1) to GFP and investigated its recognition by an anti-pep1 antibody, 4B08. First, to characterize the thermodynamic properties associated with the pep1-4B08 binding, isothermal titration calorimetry experiments were conducted. It was found that pep1 fused to the C-terminus of GFP (GFP-CT) enhanced the enthalpic gain by 2.1 kcal mol-1 and the entropic loss only by 0.9 kcal mol-1, resulting in an 8-fold increase in the binding affinity compared to the unfused pep1. On the other hand, pep1 fused to the N-terminus of GFP (GFP-NT) enhanced the enthalpic gain by 3.0 kcal mol-1 and the entropic loss by 3.2 kcal mol-1, leading to no significant enhancement of the binding affinity. To gain deeper insights, molecular dynamics simulations of GFP-NT, GFP-CT, and pep1 were performed. The results showed that the location of the fusion point sensitively affects the interaction energy, the solvent accessible surface area, and the fluctuation of pep1 in the unbound state, which explains the difference in the experimental thermodynamic properties.

Break oily water emulsion during petroleum enhancing production processes using green approach for the synthesis of SnCuO@FeO nanocomposite from microorganisms

The aim of this work was to synthesize a green nanoparticle SnCuO@FeO nanocomposite core-shell to break oily water emulsions during petroleum-enhancing production processes as an alternative to chemical and physical processes. In this study, eight bacterial isolates (MHB1-MHB8) have been isolated from tree leaves, giant reeds, and soil samples. The investigation involved testing bacterial isolates for their ability to make FeO nanoparticles and choosing the best producers. The selected isolate (MHB5) was identified by amplification and sequencing of the 16S rRNA gene as Bacillus paramycoides strain OQ878685. MHB5 produced the FeO nanoparticles with the smallest particle size (78.7 nm) using DLS. XRD, FTIR, and TEM were used to characterize the biosynthesized nanoparticles. The jar experiment used SnCuO@FeO with different ratios of Sn to CuO (1:1, 2:1, and 3:1) to study the effect of oil concentration, retention time, and temperature. The most effective performance was observed with a 1:1 ratio of Sn to CuO, achieving an 85% separation efficiency at a concentration of 5 mg/L, for a duration of 5 min, and at a temperature of 373 K. Analysis using kinetic models indicates that the adsorption process can be accurately described by both the pseudo-first-order and pseudo-second-order models. This suggests that the adsorption mechanism likely involves a combination of film diffusion and intraparticle diffusion. Regarding the adsorption isotherm, the Langmuir model provides a strong fit for the data, while the D-R model indicates that physical interactions primarily govern the adsorption mechanism. Thermodynamic analysis reveals a ∆H value of 18.62 kJ/mol, indicating an exothermic adsorption process. This suggests that the adsorption is a favorable process, as energy is released during the process. Finally, the synthesized green SnCuO@FeO nanocomposite has potential for use in advanced applications in the oil and gas industry to help the industry meet regulatory compliance, lower operation costs, reduce environmental impact, and enhance production efficiency.

Calculations of Absolute Free Energies, Enthalpies, and Entropies for Drug Binding

Computer simulation methods can aid in the rational design of drugs aimed at a specific target, typically a protein. The affinity of a drug for its target is given by the free energy of binding. Binding can be further characterized by the enthalpy and entropy changes in the process. Methods exist to determine exact free energies, enthalpies, and entropies that are dependent only on the quality of the potential model and adequate sampling of conformational degrees of freedom. Entropy and enthalpy are roughly an order of magnitude more difficult to calculate than the free energy. This project combines a replica exchange method for enhanced sampling, designed to be efficient for protein-sized systems, with free energy calculations. This approach, replica exchange with dynamical scaling (REDS), uses two conventional simulations at different temperatures so that the entropy can be found from the temperature dependence of the free energy. A third replica is placed between them, with a modified Hamiltonian that allows it to span the temperature range of the conventional replicas. REDS provides temperature-dependent data and aids in sampling. It is applied to the bromodomain-containing protein 4 (BRD4) system. We find that for the force fields used, the free energies are accurate but the entropies and enthalpies are not, with the entropic contribution being too positive. Reproducing the entropy and enthalpy of binding appears to be a more stringent test of the force fields than reproducing the free energy.

Toward Post-Hartree-Fock Accuracy for Protein-Ligand Affinities Using the Molecules-in-Molecules Fragmentation-Based Method

The complexity and size of large molecular systems, such as protein-ligand complexes, pose computational challenges for accurate post-Hartree-Fock calculations. This study delivers a thorough benchmarking of the Molecules-in-Molecules (MIM) method, presenting a clear and accessible strategy for layer/theory selections in post-Hartree-Fock computations on substantial molecular systems, notably protein-ligand complexes. An approach is articulated, enabling augmented computational efficiency by strategically canceling out common subsystem energy terms between complexes and proteins within the supermolecular equation. Employing DLPNO-based post-Hartree-Fock methods in conjunction with the three-layer MIM method (MIM3), this study demonstrates the achievement of protein-ligand binding energies with remarkable accuracy (errors <1 kcal mol-1), while significantly reducing computational costs. Furthermore, noteworthy correlations between theoretically computed interaction energies and their experimental equivalents were observed, with R2 values of approximately 0.90 and 0.78 for CDK2 and BZT-ITK sets, respectively, thus validating the efficacy of the MIM method in calculating binding energies. By highlighting the crucial role of diffuse or small Pople-style basis sets in the middle layer for reducing energy errors, this work provides valuable insights and practical methodologies for interaction energy computations in large molecular complexes and opens avenues for their application across a diverse range of molecular systems.

Explainable Supervised Machine Learning Model To Predict Solvation Gibbs Energy

Many challenges persist in developing accurate computational models for predicting solvation free energy (ΔGsol). Despite recent developments in Machine Learning (ML) methodologies that outperformed traditional quantum mechanical models, several issues remain concerning explanatory insights for broad chemical predictions with an acceptable speed-accuracy trade-off. To overcome this, we present a novel supervised ML model to predict the ΔGsol for an array of solvent-solute pairs. Using two different ensemble regressor algorithms, we made fast and accurate property predictions using open-source chemical features, encoding complex electronic, structural, and surface area descriptors for every solvent and solute. By integrating molecular properties and chemical interaction features, we have analyzed individual descriptor importance and optimized our model though explanatory information form feature groups. On aqueous and organic solvent databases, ML models revealed the predictive relevance of solutes with increasing polar surface area and decreasing polarizability, yielding better results than state-of-the-art benchmark Neural Network methods (without complex quantum mechanical or molecular dynamic simulations). Both algorithms successfully outperformed previous ΔGsol predictions methods, with a maximum absolute error of 0.22 ± 0.02 kcal mol-1, further validated in an external benchmark database and with solvent hold-out tests. With these explanatory and statistical insights, they allow a thoughtful application of this method for predicting other thermodynamic properties, stressing the relevance of ML modeling for further complex computational chemistry problems.

Seed-Induced Living Two-Dimensional (2D) Supramolecular Polymerization in Water: Implications on Protein Adsorption and Enzyme Inhibition

In biological systems, programmable supramolecular frameworks characterized by coordinated directional non-covalent interactions are widespread. However, only a small number of reports involve pure water-based dynamic supramolecular assembly of artificial π-amphiphiles, primarily due to the formidable challenge of counteracting the strong hydrophobic dominance of the π-surface in water, leading to undesired kinetic traps. This study reveals the pathway complexity in hydrogen-bonding-mediated supramolecular polymerization of an amide-functionalized naphthalene monoimide (NMI) building block with a hydrophilic oligo-oxyethylene (OE) wedge. O-NMI-2 initially produced entropically driven, collapsed spherical particles in water (Agg-1); however, over a span of 72 h, these metastable Agg-1 gradually transformed into two-dimensional (2D) nanosheets (Agg-2), favoured by both entropy and enthalpy contributions. The intricate self-assembly pathways in O-NMI-2 enable us to explore seed-induced living supramolecular polymerization (LSP) in water for controlled synthesis of monolayered 2D assemblies. Furthermore, we demonstrated the nonspecific surface adsorption of a model enzyme, serine protease α-Chymotrypsin (α-ChT), and consequently the enzyme activity, which could be regulated by controlling the morphological transformation of O-NMI-2 from Agg-1 to Agg-2. We delve into the thermodynamic aspects of such shape-dependent protein-surface interactions and unravel the impact of seed-induced LSP on temporally controlling the catalytic activity of α-ChT.

Multiple spectroscopic and computational studies on binding interaction of 2-phenylamino-4-phenoxyquinoline derivatives with bovine serum albumin

Binding characteristics of potent non-nucleoside HIV-1 reverse transcriptase inhibitors, 4-(2',6'-dimethyl-4'-formylphenoxy)-2-(5″-cyanopyridin-2″ylamino) quinoline (1) and 4-(2',6'-dimethyl-4'-cyanophenoxy)-2-(5″-cyanopyridin-2″ylamino) quinoline (2), to bovine serum albumin (BSA) under simulative physiological conditions were investigated by multiple spectroscopic and computational methods. The experimental results demonstrated that (1) and (2) bound to BSA at site III (subdomain IB), and quenched BSA fluorescence through a static quenching process. The binding interaction of (1) or (2) to BSA forms stable complexes with the binding constants (Kb) at the level of 104 L/mol and the number of binding site was determined to be 1 for both systems, indicating that new synthesized compounds occupied one site in BSA with moderate binding affinities. Based on the analysis of the thermodynamic parameters, it can be indicated that the main binding forces for interaction between BSA and both compounds were hydrogen bonding and van der Waals force. Synchronous fluorescence results revealed that the interaction of two compounds with BSA led to modifications in the microenvironment surrounding tryptophan residue of BSA. Circular dichroism spectra demonstrated alterations in the secondary structure of BSA induced by (1) and (2). Moreover, the experimental data of molecular docking and molecular dynamics (MD) simulations supported the results obtained from multiple spectroscopic techniques, confirming the binding interactions between both compounds and BSA.

Uranium capture from aqueous solution using palm-waste based activated carbon: sorption kinetics and equilibrium

Carbonaceous materials produced from agricultural waste (palm kernel shell) by pyrolysis can be a proper type of low-cost adsorbent for wide uses in radioactive effluent treatment. In this context, the as-produced bio-char (labeled as PBC) and its sub-driven sulfuric acid and zinc oxide activated carbons (labeled as PBC-SA, and PBC-Zn respectively) were employed as adsorbents for uranium sorption from aqueous solution. Various analytical techniques, including SEM (Scanning Electron Microscopy), EXD (X-ray Diffraction), BET (Brunauer-Emmett-Teller), FTIR (Fourier Transform Infrared Spectroscopy), and Zeta potential, provide insights into the material characteristics. Kinetic and isotherm investigations illuminated that the sorption process using the three sorbents is nicely fitted with Pseudo-second-order-kinetic and Langmuir isotherm models. The picked data display that the equilibrium time was 60 min, and the maximum sorption capacity was 9.89, 16.8, and 21.9 mg/g for PBC, PBC-SA, and PBC-Zn respectively, which reflects the highest affinity for zinc oxide, activated bio-char, among the three adsorbents, for uranium taking out from radioactive wastewater. Sorption thermodynamics declare that the sorption of U(VI) is an exothermic, spontaneous, and feasible process. About 92% of the uranium-loaded PBC-Zn sorbent was eluted using 1.0 M CH3COONa sodium ethanoate solution, and the sorbent demonstrated proper stability for 5 consecutive sorption/desorption cycles.

Adenine Methylation Enhances the Conformational Flexibility of an RNA Hairpin Tetraloop

The N6-methyladenosine modification is one of the most abundant post-transcriptional modifications in ribonucleic acid (RNA) molecules. Using molecular dynamics simulations and alchemical free-energy calculations, we studied the structural and energetic implications of incorporating this modification in an adenine mononucleotide and an RNA hairpin structure. At the mononucleotide level, we found that the syn configuration is more favorable than the anti configuration by 2.05 ± 0.15 kcal/mol. The unfavorable effect of methylation was due to the steric overlap between the methyl group and a nitrogen atom in the purine ring. We then probed the effect of methylation in an RNA hairpin structure containing an AUCG tetraloop, which is recognized by a "reader" protein (YTHDC1) to promote transcriptional silencing of long noncoding RNAs. While methylation had no significant conformational effect on the hairpin stem, the methylated tetraloop showed enhanced conformational flexibility compared to the unmethylated tetraloop. The increased flexibility was associated with the outward flipping of two bases (A6 and U7) which formed stacking interactions with each other and with the C8 and G9 bases in the tetraloop, leading to a conformation similar to that in the RNA/reader protein complex. Therefore, methylation-induced conformational flexibility likely facilitates RNA recognition by the reader protein.

Comparing the Chemistry of Malvidin-3-O-glucoside and Malvidin-3,5-O-diglucoside Networks: A Holistic Approach to the Acidic and Basic Paradigms with Implications in Biological Studies

The kinetics, thermodynamics, and degradation of malvidin mono- and diglucosides were studied following a holistic approach by extending to the basic medium. In acidic conditions, the reversible kinetics of the flavylium cation toward the equilibrium is controlled by the hydration and cis-trans isomerization steps, while in the basic medium, the OH- nucleophilic addition to the anionic quinoidal bases is the slowest step. There is a pH range (transition pHs), between the acidic and basic paradigms, that includes physiological pH (7.4), where degradation reactions occur faster, preventing the system from reaching the equilibrium. The transition pH of the diglucoside is narrower, and in contrast with the monoglucoside, there is no evidence for the formation of colored oligomers among the degradation products. Noteworthy, OH- addition in position 4 to form B42-, a kinetic product that decreases the overall equilibration rate, was observed only for the diglucoside.