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.

Studying the Collective Functional Response of a Receptor in Alchemical Ligand Binding Free Energy Simulations with Accelerated Solvation Layer Dynamics.

Ligand binding free energy simulations (LB-FES) that involve sampling of protein functional conformations have been longstanding challenges in research on molecular recognition. Particularly, modeling of the conformational transition pathway and design of the heuristic biasing mechanism are severe bottlenecks for the existing enhanced configurational sampling (ECS) methods. Inspired by the key role of hydration in regulating conformational dynamics of macromolecules, this report proposes a novel ECS approach that facilitates binding-associated structural dynamics by accelerated hydration transitions in combination with the λ-exchange of free energy perturbation (FEP). Two challenging protein-ligand binding processes involving large configurational transitions of the receptor are studied, with hydration transitions at binding sites accelerated by Hamiltonian-simulated annealing of the hydration layer. Without the need for pathway analysis or ad hoc barrier flattening potential, LB-FES were performed with FEP/λ-exchange molecular dynamics simulation at a minor overhead for annealing of the hydration layer. The LB-FES studies showed that the accelerated rehydration significantly enhances the collective conformational transitions of the receptor, and convergence of binding affinity calculations is obtained at a sweet-spot simulation time scale. Alchemical LB-FES with the proposed ECS strategy is free from the effort of trial and error for the setup and realizes efficient on-the-fly sampling for the collective functional response of the receptor and bound water and therefore presents a practical approach to high-throughput screening in drug discovery.

NMR characterization of RNA binding property of the DEAD-box RNA helicase DDX3X and its implications for helicase activity.

The DEAD-box RNA helicase (DDX) plays a central role in many aspects of RNA metabolism by remodeling the defined structure of RNA molecules. While a number of structural studies have revealed the atomistic details of the interaction between DDX and RNA ligands, the molecular mechanism of how this molecule unwinds a structured RNA into an unstructured single-stranded RNA (ssRNA) has largely remained elusive. This is due to challenges in structurally characterizing the unwinding intermediate state and the lack of thermodynamic details underlying this process. In this study, we use solution nuclear magnetic resonance (NMR) spectroscopy to characterize the interaction of human DDX3X, a member of the DDX family, with various RNA ligands. Our results show that the inherent binding affinity of DDX3X for ssRNA is significantly higher than that for structured RNA elements. This preferential binding, accompanied by the formation of a domain-closed conformation in complex with ssRNA, effectively stabilizes the denatured ssRNA state and thus underlies the unwinding activity of DDX3X. Our results provide a thermodynamic and structural basis for the DDX function, whereby DDX can recognize and remodel a distinct set of structured RNAs to participate in a wide range of physiological processes.

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.

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.

Thermodynamically and Kinetically Enhanced Benzene Vapor Sensor Based on the Cu-TCPP-Cu MOF with Extremely Low Limit of Detection.

As a carcinogenic and highly neurotoxic hazardous gas, benzene vapor is particularly difficult to be distinguished in BTEX (benzene, toluene, ethylbenzene, xylene) atmosphere and be detected in low concentrations due to its chemical inertness. Herein, we develop a depth-related pore structure in Cu-TCPP-Cu to thermodynamically and kinetically enhance the adsorption of benzene vapor and realize the detection of ultralow-temperature benzene gas. We find that the in-plane π electronic nature and proper pore sizes in Cu-TCPP-Cu can selectively induce the adsorption and diffusion of BTEX. Interestingly, the theoretical calculations (including density functional theory (DFT) and grand canonical Monte Carlo (GCMC) simulations) exhibit that benzene molecules are preferred to adsorb and array as a consecutive arrangement mode in the Cu-TCPP-Cu pore, while the TEX (toluene, ethylbenzene, xylene) dominate the jumping arrangement model. The differences in distribution behaviors can allow adsorption and diffusion of more benzene molecules within limited room. Furthermore, the optimal pore-depth range (60-65 nm) of Cu-TCPP-Cu allows more exposure of active sites and hinders the gas-blocking process. The optimized sensor exhibits ultrahigh sensitivity to benzene vapor (155 Hz/µg@1 ppm), fast response time (less than 10 s), extremely low limit of detection (65 ppb), and excellent selectivity (83%). Our research thus provides a fundamental understanding to design and optimize two-dimensional metal-organic framework (MOF)-based gas sensors.

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.

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.

G-Quadruplexes as Sensors of Intracellular Na+/K+ Ratio: Potential Role in Regulation of Transcription and Translation.

Data on the structure of G-quadruplexes, noncanonical nucleic acid forms, supporting an idea of their potential participation in regulation of gene expression in response to the change in intracellular Na+i/K+i ratio are considered in the review. Structural variety of G-quadruplexes, role of monovalent cations in formation of this structure, and thermodynamic stability of G-quadruplexes are described. Data on the methods of their identification in the cells and biological functions of these structures are presented. Analysis of information about specific interactions of G-quadruplexes with some proteins was conducted, and their potential participation in the development of some pathological conditions, in particular, cancer and neurodegenerative diseases, is considered. Special attention is given to the plausible role of G-quadruplexes as sensors of intracellular Na+i/K+i ratio, because alteration of this parameter affects folding of G-quadruplexes changing their stability and, thereby, organization of the regulatory elements of nucleic acids. The data presented in the conclusion section demonstrate significant change in the expression of some early response genes under certain physiological conditions of cells and tissues depending on the intracellular Na+i/K+i ratio.

[Effects of maltodextrin on water adsorption and thermodynamic properties of Codonopsis Radix spray-dried powder].

This study aims to reveal the effects of maltodextrin(MD) on the water adsorption and thermodynamic properties of Codonopsis Radix(DS) spray-dried powder by determining the moisture and energy changes of the powder in the process of moisture absorption. The static weighing method was used to obtain the isothermal water adsorption data of the spray-dried powder in 6 saturated salt solutions(KAc, MgCl_2·6H_2O, K_2CO_3, NaBr, NaCl, and KCl) at 3 temperatures(25, 35, and 45 ℃). Six models were used for fitting of the water adsorption process, and the most suitable model was selected based on the model performance. Furthermore, the corresponding net equivalent adsorption heat and differential entropy were calculated, and the adsorption entropy change was integrated. The linear relationship between net equivalent adsorption heat and differential entropy was drawn based on the entropy-enthalpy complementarity theory. The results showed that the water adsorption properties of DS and DS-MD spray-dried powder followed the type Ⅲ isotherm and was well fitted by the GAB model. The monolayer water content M_0 decreased with the increase in temperature. At the same temperature, the M_0 of DS spray-dried powder decreased after the addition of MD. The net equivalent adsorption heat and differential entropy of DS and DS-MD spray-dried powder decreased with the increase in water content, which presented a linear relationship. The addition of MD decreased the water activity corresponding to the lowest integral adsorption entropy of the powder, and the system became more stable. The results indicated that the spray-dried powder became more stable after the addition of MD.

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.

Removal of antiretroviral drugs from wastewater using activated macadamia nutshells: Adsorption kinetics, adsorption isotherms, and thermodynamic studies.

Antiretroviral drugs (ARVDs) have been extensively employed in health care to improve the quality of life and lifecycle longevity. However, overuse and improper disposal of ARVDs have been recognized as an emerging concern whereby wastewater treatment major recipients. Therefore, in this work, the activated macadamia nutshells (MCNs) were explored as low-cost adsorbents for the removal of ARVDs in wastewater samples. Fourier transform infrared spectroscopy (FTIR), Scanning Electron microscopy (SEM), Brunauer-Emmet-Teller (BET), and Powder X-ray diffraction (PXRD). The highest removal efficiency (R.E) was above 86% for the selected analytes nevirapine, abacavir, and efavirenz. The maximum adsorption capacity of the functionalized MCN adsorbent was 10.79, 27.44, and 38.17 mg/g for nevirapine, abacavir, and efavirenz for HCl-modified adsorbent. In contrast, NaOH modified had adsorption capacities of 13.67, 14.25, and 20.79 mg/g. The FTIR showed distinct functional groups OH and CO, which facilitate the removal of selected ARVDs. From studying kinetics parameters, the pseudo-second-order (R2 = 0.990-0.996) was more dominant than the pseudo-first-order (R2 = 0.872-0.994). The experimental data was most fitted in the Freundlich model with (R2 close to 1). The thermodynamic parameters indicated that the adsorption process was spontaneous and exothermic. The study indicated that MCNs are an eco-friendly, low-cost, and effective adsorbent for the removal of nevirapine, abacavir, and efavirenz. PRACTITIONER POINTS: Modification macadamia nutshell with HCl and NaOH improved physio-chemical properties that yielded high removal efficiency compared with raw macadamia nutshells. Modification of macadamia by HCl showed high removal efficiency, which could be attributed to high interaction such as H-bonding that improves adsorption. The macadamia nutshell as an adsorbent showed so much robustness with regeneration studies yielding to about 69.64% of selected compounds.

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.

Reversible strain-promoted DNA polymerization.

Molecular strain can be introduced to influence the outcome of chemical reactions. Once a thermodynamic product is formed, however, reversing the course of a strain-promoted reaction is challenging. Here, a reversible, strain-promoted polymerization in cyclic DNA is reported. The use of nonhybridizing, single-stranded spacers as short as a single nucleotide in length can promote DNA cyclization. Molecular strain is generated by duplexing the spacers, leading to ring opening and subsequent polymerization. Then, removal of the strain-generating duplexers triggers depolymerization and cyclic dimer recovery via enthalpy-driven cyclization and entropy-mediated ring contraction. This reversibility is retained even when a protein is conjugated to the DNA strands, and the architecture of the protein assemblies can be modulated between bivalent and polyvalent states. This work underscores the utility of using DNA not only as a programmable ligand for assembly but also as a route to access restorable bonds, thus providing a molecular basis for DNA-based materials with shape-memory, self-healing, and stimuli-responsive properties.

Revealing the Interaction Mechanism between Mycobacterium tuberculosis GyrB and Novobiocin, SPR719 through Binding Thermodynamics and Dissociation Kinetics Analysis.

With the rapid emergence of drug-resistant strains of Mycobacterium tuberculosis (Mtb), various levels of resistance against existing anti-tuberculosis (TB) drugs have developed. Consequently, the identification of new anti-TB targets and drugs is critically urgent. DNA gyrase subunit B (GyrB) has been identified as a potential anti-TB target, with novobiocin and SPR719 proposed as inhibitors targeting GyrB. Therefore, elucidating the molecular interactions between GyrB and its inhibitors is crucial for the discovery and design of efficient GyrB inhibitors for combating multidrug-resistant TB. In this study, we revealed the detailed binding mechanisms and dissociation processes of the representative inhibitors, novobiocin and SPR719, with GyrB using classical molecular dynamics (MD) simulations, tau-random acceleration molecular dynamics (τ-RAMD) simulations, and steered molecular dynamics (SMD) simulations. Our simulation results demonstrate that both electrostatic and van der Waals interactions contribute favorably to the inhibitors' binding to GyrB, with Asn52, Asp79, Arg82, Lys108, Tyr114, and Arg141 being key residues for the inhibitors' attachment to GyrB. The τ-RAMD simulations indicate that the inhibitors primarily dissociate from the ATP channel. The SMD simulation results reveal that both inhibitors follow a similar dissociation mechanism, requiring the overcoming of hydrophobic interactions and hydrogen bonding interactions formed with the ATP active site. The binding and dissociation mechanisms of GyrB with inhibitors novobiocin and SPR719 obtained in our work will provide new insights for the development of promising GyrB inhibitors.

The influence of model building schemes and molecular dynamics sampling on QM-cluster models: the chorismate mutase case study.

Most QM-cluster models of enzymes are constructed based on X-ray crystal structures, which limits comparison to in vivo structure and mechanism. The active site of chorismate mutase from Bacillus subtilis and the enzymatic transformation of chorismate to prephenate is used as a case study to guide construction of QM-cluster models built first from the X-ray crystal structure, then from molecular dynamics (MD) simulation snapshots. The Residue Interaction Network ResidUe Selector (RINRUS) software toolkit, developed by our group to simplify and automate the construction of QM-cluster models, is expanded to handle MD to QM-cluster model workflows. Several options, some employing novel topological clustering from residue interaction network (RIN) information, are evaluated for generating conformational clustering from MD simulation. RINRUS then generates a statistical thermodynamic framework for QM-cluster modeling of the chorismate mutase mechanism via refining 250 MD frames with density functional theory (DFT). The 250 QM-cluster models sampled provide a mean ΔG‡ of 10.3 ± 2.6 kcal mol-1 compared to the experimental value of 15.4 kcal mol-1 at 25 °C. While the difference between theory and experiment is consequential, the level of theory used is modest and therefore "chemical" accuracy is unexpected. More important are the comparisons made between QM-cluster models designed from the X-ray crystal structure versus those from MD frames. The large variations in kinetic and thermodynamic properties arise from geometric changes in the ensemble of QM-cluster models, rather from the composition of the QM-cluster models or from the active site-solvent interface. The findings open the way for further quantitative and reproducible calibration in the field of computational enzymology using the model construction framework afforded with the RINRUS software toolkit.

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.

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.

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.

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.