Leveraging New Approach Methodologies: Ecotoxicological Modelling of Endocrine Disrupting Chemicals to Danio rerio through Machine Learning and Toxicity Studies.

Emerging endocrine-disrupting chemicals (EDCs) are a diverse group of toxic substances that disrupt the endocrine system. These substances can only be approved after obtaining concrete evidence of their toxicity. New approach methodologies (NAMs) offer information tailored to the intended application while reducing the use of animals. NAMs aim to develop quantitative structure-activity relationship (QSAR) and quantitive-Read-Across structure-activity relationship (q-RASAR) models to predict and categorize the acute toxicity of known and unknown EDCs. The q-RASAR model was constructed and verified using validation metrics (R2 = 0.886 and Q2 = 0.814). The substructure fingerprint was well-fitted for the classification model and the model was validated using 10-fold average accuracy (Q = 86.88%), specificity (Sp = 88.89%), Matthew's correlation curve (MCC = 0.621) and receiver operating characteristics (ROC = 0.828). The dataset of unknown substances revealed that phenolphthalein (Php) exhibited a significant level of toxicity. The docking and simulation study results indicated that the computationally derived important features successfully bound to the target zebrafish sex hormone binding globulin (zfSHBG). The experimental LC50 value of 0.790 mg L-1 was very close to the predicted value of 0.763 mg L-1, which provides high confidence to the developed model.

In silico assessments of the small molecular boron agents to pave the way for artificial intelligence-based boron neutron capture therapy.

Boron neutron capture therapy (BNCT) is a highly targeted, selective and effective technique to cure various types of cancers, with less harm to the healthy cells. In principle, BNCT treatment needs to distribute the 10boron (10B) atoms inside the tumor tissues, selectively and homogeneously, as well as to initiate a nuclear fission reaction by capturing sufficient neutrons which releases high linear energy particles to kill the tumor cells. In BNCT, it is crucial to have high quality boron agents with acceptable bio-selectivity, homogeneous distribution and deliver in required quantity, similar to chemotherapy and other radiotherapy for tumor treatment. Nevertheless, boron drugs currently used in clinical trials yet to meet the full requirements. On the other hand, BNCT processing has opened up the era of renaissance due to the advanced development of the high-quality neutron source and the global construction of new BNCT centers. Consequently, there is an urgent need to use boron agents that have increased biocapacity. Artificial intelligence (AI) tools such as molecular docking and molecular dynamic simulation technologies have been utilized to develop new medicines. In this work, the in silico assessments including bioinformatics assessments of BNCT related tumoral receptor proteins, computational assessments of optimized small molecules of boron agents, are employed to speed up the screening process for boron drugs. The outcomes will be applicable to pave the way for future BNCT that utilizes artificial intelligence. The in silico molecular docking and dynamic simulation results of the optimized small boron agents, such as 4-borono-l-phenylalanine (BPA) with optimized proteins like the L-type amino acid transporter 1 (LTA1, also known as SLC7A5) will be examined. The in silico assessments results will certainly be helpful to researchers in optimizing druggable boron agents for the BNCT application. The clinical status of the optimized proteins, which are highly relevant to cancers that may be treated with BNCT, has been assessed using bioinformatics technology and discussed accordingly. Furthermore, the evaluations of cytotoxicity (IC50), boron uptake and tissue distribution of the optimized ligands 1 and 7 have been presented.

Dissolution of denim waste in N-methyl morpholine-N-oxide monohydrate for fabrication of regenerated cellulosic film: Experimental and simulation study.

Despite the significant amount of denim waste and its potential as a cellulose source, its use has been neglected. This study uses N-methyl morpholine-N-oxide, an eco-friendly solvent, to dissolve denim (including 100 % cotton) and create a denim film. Achieving a 10 % denim record solubility, a cellulosic film was also fabricated for comparison. Characterisation techniques were applied, and molecular dynamics simulations explored intramolecular interactions and the influence of indigo dye on dissolution process. FTIR spectra indicated no chemical reactions during dissolution and regeneration, though a shift in OH stretching suggested a change in crystallinity, confirmed by XRD results showing decreased crystallinity and a structural shift from cellulose I to cellulose II. 13C NMR analysis revealed disruptions in interchain hydrogen bonds after regeneration. TGA results showed lower decomposition temperatures for both films compared to the powders. Testing mechanical properties showed the denim film had higher elongation at break but lower tensile strength than the cellulose film. MD simulations indicated indigo dye did not significantly affect fundamental interactions but decreased denim solubility by reducing the diffusion coefficient. Rheological tests supported the simulation results, showing higher viscosity and molecular weight for the denim solution compared to cellulose.

Discerning computational, in vitro and in vivo investigations of self-assembling empagliflozin polymeric micelles in type-2 diabetes.

BACKGROUND: Empagliflozin (EMPA) is an SGLT2 inhibitor, a new class of anti-diabetic medication, indicated for treating type-2 diabetes. Its low permeability, poor solubility and bioavailability limits its use in management of diabetes. The study was aimed to formulate EMPA loaded polymeric micelles (PMs) to overcome these obstacles in oral absorption. METHODOLOGY: In silico studies-molecular docking, molecular dynamic simulation (MDS), and quantum chemical calculation were employed to study the interaction of EMPA with different polymers. EMPA loaded TPGS polymeric micelles (EMPA-TPGS-PMs) were formulated by direct dissolution method and characterized in terms of surface morphology, entrapment, particle size, in vitro drug release, and in vitro cytotoxicity (HEK293 cells). In vivo pharmacokinetic and pharmacodynamic studies were also performed. RESULTS: The results suggested a good interaction between TPGS and EMPA with lowest binding energy compared to other polymers. Further MDS results and DFT calculations validated the stable binding of the complex hence TPGS was selected for further wet lab experiments. The EMPA-TPGS complex displayed lower value of Total energy (T.E.) than its individual components, indicating the overall stability of the complex while, the energy band gap (∆E) value lied between the two individual molecules, signifying the better electron transfer between HOMO and LUMO of the complex. Based on the solubility, entrapment and cytotoxicity studies, 5% TPGS was selected for formulating drug loaded micelles. EMPA-TPGS5-PMs presented a size of 9.008 ± 1.25 nm, Polydispersity index (PDI) of 0.254 ± 0.100, a controlled release behaviour upto 24 h. SEM and AFM images of the nanoformulation suggested spherical particles whereas, DSC, and PXRD studies confirmed the loss of crystallinity of EMPA. A 3.12-folds higher AUC and a greater reduction in blood glucose levels was exhibited by EMPA-TPGS5-PMs in comparison to EMPA-SUSP in mice model. CONCLUSION: EMPA-TPGS-PMs has exhibited better bio absorption and therapeutic effectiveness in diabetes treatment. This improved performance would open the possibility of dose reduction, reduced dosing frequency & dose-related side effects, improving pharmaco-economics and thereby improved overall compliance to the patient. However, this translation from bench to bedside would necessitate studies in higher animals and human volunteers.

Ortho-Vanillin Ameliorates Spinetoram-Induced Oxidative Stress in the Silkworm Bombyx mori: Biochemical and In Silico Insights.

This study investigates the toxic effects of the insecticide spinetoram on the model organism Bombyx mori (Linnaeus) and explores the potential ameliorative properties of O-Vanillin. Sub-lethal concentrations of spinetoram were given to silkworm larvae via oral feed, resulting in reduced body weight, larval length, and impaired cocoon characteristics. A study of the enzymatic and non-enzymatic antioxidants revealed oxidative stress in the gut, fat body, and silk gland tissues, characterized by decreased antioxidants and increased lipid peroxidation. However, post-treatment with O-Vanillin effectively mitigated these toxic effects, preserving antioxidant capacities and preventing lipid peroxidation. Additionally, O-Vanillin prevented the loss of body weight and improved cocoon characteristics. At the histological level, spinetoram exposure caused mild histological damage in the gut, fat body, and silk gland. However, O-Vanillin post-treatment had ameliorative effects and mitigated the histological damages. To delve deeper into the mechanism of amelioration of O-Vanillin, in silico studies were used to study the interaction between an important xenobiotic metabolism protein of the Bombyx mori, i.e., Cytochrome p450, specifically CYP9A19, and O-Vanillin. We performed blind molecular docking followed by molecular dynamic simulation, and the results demonstrated stable binding interactions between O-Vanillin and CYP9A19, a cytochrome P450 protein in silkworm, belonging to the subfamily CYP9A, suggesting a potential role for O-vanillin in modulating xenobiotic metabolism.

6-Gingerol modulates miRNAs and PODXL gene expression via methyltransferase enzymes in NB4 cells: an in silico and in vitro study.

This investigation delves into the influence of predicted microRNAs on DNA methyltransferases (DNMTs) and the PODXL gene within the NB4 cell line, aiming to elucidate their roles in the pathogenesis of acute myeloid leukemia (AML). A comprehensive methodological framework was adopted to explore the therapeutic implications of 6-gingerol on DNMTs. This encompassed a suite of bioinformatics tools for protein structure prediction, docking, molecular dynamics, and ADMET profiling, alongside empirical assessments of miRNA and PODXL expression levels. Such a multifaceted strategy facilitated an in-depth understanding of 6-gingerol's potential efficacy in DNMT modulation. The findings indicate a nuanced interplay where 6-gingerol administration modulated miRNA expression levels, decreasing in DNMT1 and DNMT3A expression in NB4 cells. This alteration indirectly influenced PODXL expression, contributing to the manifestation of oncogenic phenotypes. The overexpression of DNMT1 and DNMT3A in NB4 cells may contribute to AML, which appears modulable via microRNAs such as miR-193a and miR-200c. Post-treatment with 6-gingerol, DNMT1 and DNMT3A expression alterations were observed, culminating in the upregulation of miR-193a and miR-200c. This cascade effect led to the dysregulation of tumor suppressor genes in cancer cells, including downregulation of PODXL, and the emergence of cancerous traits. These insights underscore the therapeutic promise of 6-gingerol in targeting DNMTs and microRNAs within the AML context.

Natural product-derived ALK inhibitors for treating ALK-driven lung cancers: an in silico study.

Anaplastic lymphoma kinase (ALK)-driven lung cancer represents a critical therapeutic target, demanding innovative approaches for the identification of effective inhibitors. Anaplastic lymphoma kinase (ALK), a key protein involved in the pathogenesis of ALK-driven lung cancers, has been the focus of extensive drug discovery efforts. This study employed a comprehensive computational drug discovery approach, integrating virtual screening with the Lipinski filter, re-docking, molecular dynamics (MD) simulations, and free energy calculations to identify potential inhibitors from a natural compound library. Utilizing the MTiOpenScreen web server, we screened for compounds that exhibit favorable interactions with ALK, resulting in 1227 compounds with virtual screening scores ranging from - 10.2 to - 3.7 kcal/mol. Subsequent re-docking of three selected compounds (ZINC000059779788, ZINC000043552589, and ZINC000003594862) and one reference compound against ALK yielded docking scores - 10.4, - 10.2, - 10.2, and - 10.1 kcal/mol, respectively. These compounds demonstrated promising interactions with ALK, suggesting potential inhibitory effects. Advanced analyses, including MD simulation and binding free energy calculations, further supported the potential efficacy of these compounds. MD simulations, particularly the root mean square deviation (RMSD) and root mean square fluctuation (RMSF) analyses, revealed that compounds ZINC000059779788 and ZINC000003594862 achieved better stability compared to compound ZINC000043552589. These stable conformations suggest effective binding over time. Free energy calculations using the MM/GBSA method showed that ZINC000059779788 had the most favorable binding energy, indicating a strong and stable interaction with the ALK protein. The promising computational findings from this study emphasize the necessity for additional experimental testing to verify the therapeutic efficacy of these natural compounds for treating lung cancers.

Heat shock protein (Hsp27)-ceramide synthase (Cers1) protein-protein interactions provide a new avenue for unexplored anti-cancer mechanism and therapy.

Hsp27 is a member of the small heat-shock proteins (sHSPs) - the known cellular line of defence against abnormal protein folding behaviors. Nevertheless, its upregulation is linked to a variety of pathological disorders, including several types of cancers. The ceramide synthases (CerS) mediate the synthesis of ceramide, a critical structural and signaling lipid. Functionally, downstream ceramide metabolites are implicated in the apoptosis process and their abnormal functionality has been linked to anticancer resistance. Studies showed that CerS1 are possibly inhibited by Hsp27 leading to biochemical anticancer effects in vitro. Nevertheless, the nature of such protein-protein interaction (PPI) has not been considerably investigated in molecular terms, hence, we present the first description of the dynamics CerS1-Hsp27 interaction landscapes using molecular dynamics simulations. Time-scale molecular dynamics simulation analysis indicated a system-wide conformational events of decreased stability, increased flexibility, reduced compactness, and decreased folding of CerS1. Analysis of binding energy showed a favorable interaction entailing 56 residues at the interface and a total stabilizing energy of -158 KJ/mol. The CerS1 catalytic domain experienced an opposite trend compared to the protein backbone. Yet, these residues adopted a highly compact conformation as per DCCM and DSSP analysis. Furthermore, conserved residues (SER 212, ASP 213, ALA 240, GLY 243, ASP 319) comprising the substrate shuttling machinery showed notable rigidity implying a restrained ceramide precursor access and assembly; hence, a possible inhibitory mechanism. Findings from this report would streamline a better molecular understanding of CerS1-Hsp27 interactions and decipher its potential avenue toward unexplored anti-cancer mechanisms and therapy.

Hydrologic variability governs GHG emissions in rice-based cropping systems of Eastern India.

Reducing methane (CH4) emissions is increasingly recognized as an urgent greenhouse gas mitigation priority for avoiding ecosystem 'tipping points' that will accelerate global warming. Agricultural systems, namely ruminant livestock and rice cultivation are dominant sources of CH4 emissions. Efforts to reduce methane from rice typically focus on water management strategies that implicitly assume that irrigated rice systems are consistently flooded and that farmers exert a high level of control over the field water balance. In India most rice is cultivated during the monsoon season and hydrologic variability is common, particularly in the Eastern Gangetic Plains (EGP) where high but variable rainfall, shallow groundwater, and subtle differences in topography interact to create complex mosaics of field water conditions. Here, we characterize the hydrologic variability of monsoon season rice fields (n = 207) in the Indian EGP ('Eastern India') across two contrasting climate years (2021, 2022) and use the Denitrification Decomposition (DNDC) model to estimate GHG emissions for the observed hydrologic conditions. Five distinct clusters of field hydrology patterns were evident in each year, but cluster characteristics were not stable across years. In 2021, average GHG emissions (8.14 mt CO2-eq ha-1) were twice as high as in 2022 (3.81 mt CO2-eq ha-1). Importantly, intra-annual variability between fields was also high, underlining the need to characterize representative emission distributions across the landscape and across seasons to appropriately target GHG mitigation strategies and generate accurate baseline values. Simulation results were also analyzed to identify main drivers of emissions, with readily identified factors such as flooding period and hydrologic interactions with crop residues and nitrogen management practices emerging as important. These insights provide a foundation for understanding landscape variability in GHG emissions from rice in Eastern India and suggest priorities for mitigation that honor the hydrologic complexity of the region.

Cholinergic Inhibition and Antioxidant Potential of Gongronema latifolium Benth Leaf in Neurodegeneration: Experimental and In Silico Study.

The use of Gongronema latifolium for the management of various forms of neurological disorders has generated a lot of interest in the need to further investigate its neurotherapeutic constituents. This work, therefore, focused on assessing the inhibitory potential of selected bioactive components derived from G. latifolium against key neurotherapeutic targets and oxidant species associated with neurodegeneration using in vitro analysis and biomolecular modelling. G. latifolium methanol extract (GLME), solvent partition, chromatographic fractions (A-F) of GLME and pregnane compounds (Iloneoside and marsectohexol) derived from fraction-B with the highest activity were investigated for in vitro acetylcholinesterase (AChE), butyrylcholinesterase (BChE), and monoamine oxidase (MAO) inhibition in addition to their in vitro antioxidant activities. The interactions of iloneoside, marsectohexol, and reference drugs with human acetylcholinesterase, butyrylcholinesterase, and ß-secretase (BACE-1) were further assessed using molecular docking, binding free energy calculations, cluster analysis, and molecular dynamics simulations. The GLME and fractions inhibited the activities of both acetylcholinesterase and butyrylcholinesterase in a dose-dependent manner. Iloneoside and marsectohexol exhibited in vitro concentration-dependent inhibitory activities against acetylcholinesterase (IC50 = 19.28, 184.9 µM, respectively) and butyrylcholinesterase (IC50 = 30.75, 43.4 µM, respectively). These compounds also possess ferric ion-reducing, hydroxyl, and superoxide radical-scavenging activities. Iloneoside had the highest docking scores of -9.8, -9.9 -9.4 Kcal for AChE, BChE, and BACE1, respectively. The stability of the interaction of the bioactive compounds with the catalytic residues of the protein targets was preserved in a 100 ns molecular dynamics simulation. Iloneoside, a rare pregnane glycoside, was identified as a neurotherapeutic constituent of G. latifolium leaf. Further studies are suggested to investigate the neurotherapeutic potential in animal models.

Suppressing Mycobacterium tuberculosis virulence and drug resistance by targeting Eis protein through computational drug discovery.

Tuberculosis (TB) remains a critical health threat, particularly with the emergence of multidrug-resistant strains. This demands attention from scientific communities and healthcare professionals worldwide to develop effective treatments. The enhanced intracellular survival (Eis) protein is an acetyltransferase enzyme of Mycobacterium tuberculosis that functions by adding acetyl groups to aminoglycoside antibiotics, which interferes with their ability to bind to the bacterial ribosome, thereby preventing them from inhibiting protein synthesis and killing the bacterium. Therefore, targeting this protein accelerates the chance of restoring the aminoglycoside drug activity, thereby reducing the emergence of drug-resistant TB. For this, we have screened 406,747 natural compounds from the Coconut database against Eis protein. Based on MM/GBSA rescoring binding energy, the top 5 most prominent natural compounds, viz. CNP0187003 (- 96.14 kcal/mol), CNP0176690 (- 93.79 kcal/mol), CNP0136537 (- 92.31 kcal/mol), CNP0398701 (- 91.96 kcal/mol), and CNP0043390 (- 91.60 kcal/mol) were selected. These compounds exhibited the presence of a substantial number of hydrogen bonds and other significant interactions confirming their strong binding affinity with the Eis protein during the docking process. Subsequently, the MD simulation of these compounds exhibited that the Eis-CNP0043390 complex was the most stable, followed by Eis-CNP0187003 and Eis-CNP0176690 complex, further verified by binding free energy calculation, principal component analysis (PCA), and Free energy landscape analysis. These compounds demonstrated the most favourable results in all parameters utilised for this investigation and may have the potential to inhibit the Eis protein. There these findings will leverage computational techniques to identify and develop a natural compound inhibitor as an alternative for drug-resistant TB.

Experiment and Molecular Dynamic Simulation on Interactions between 3,4-Bis(3-nitrofurazan-4-yl) Furoxan (DNTF) and Some Low-Melting-Point Explosives.

3,4-bis(3-nitrofurazan-4-yl) furoxan (DNTF) is an explosive with excellent performance, and the use of DNTF as a high-energy component is of great significance for improving the comprehensive performance of weapons. To explore the effect of DNTF on low-melting-point molten carrier explosives, the compatibility between DNTF and other low-melting-point explosives was analyzed by differential scanning calorimetry, and mechanical sensitivity was tested. The compatibility and cohesive energy density between DNTF and other low-melting-point explosives were calculated by Materials Studio. The results showed that DNTF has good compatibility with most low-melting-point explosives, and the peak temperature change of the mixed system formed by melt-casting is not obvious. Among them, DNTF has the best compatibility with MTNP, TNT, and DNAN; moderate compatibility with DFTNAN and DNP; and the worst compatibility with DNMT. The sensitivity test results indicate that the combination of DNTF and TNT has the most significant reduction in mechanical sensitivity. DFTNAN and MTNP have better stability than DNTF and can generate strong interaction forces with DNTF. Other low-melting-point explosives mixed with DNTF have lower intermolecular forces than DNTF. The DNTF/MTNP system requires the most energy to phase change when heated compared to other mixed systems and is the least sensitive to heat. The DNTF/DNMT system has the lowest cohesive energy density and is the most sensitive to heat.

Design and Optimization of Molecularly Imprinted Polymer Targeting Epinephrine Molecule: A Theoretical Approach.

Molecularly imprinted polymers (MIPs) are a growing highlight in polymer chemistry. They are chemically and thermally stable, may be used in a variety of environments, and fulfill a wide range of applications. Computer-aided studies of MIPs often involve the use of computational techniques to design, analyze, and optimize the production of MIPs. Limited information is available on the computational study of interactions between the epinephrine (EPI) MIP and its target molecule. A rational design for EPI-MIP preparation was performed in this study. First, density functional theory (DFT) and molecular dynamic (MD) simulation were used for the screening of functional monomers suitable for the design of MIPs of EPI in the presence of a crosslinker and a solvent environment. Among the tested functional monomers, acrylic acid (AA) was the most appropriate monomer for EPI-MIP formulation. The trends observed for five out of six DFT functionals assessed confirmed AA as the suitable monomer. The theoretical optimal molar ratio was 1:4 EPI:AA in the presence of ethylene glycol dimethacrylate (EGDMA) and acetonitrile. The effect of temperature was analyzed at this ratio of EPI:AA on mean square displacement, X-ray diffraction, density distribution, specific volume, radius of gyration, and equilibrium energies. The stability observed for all these parameters is much better, ranging from 338 to 353 K. This temperature may determine the processing and operating temperature range of EPI-MIP development using AA as a functional monomer. For cost-effectiveness and to reduce time used to prepare MIPs in the laboratory, these results could serve as a useful template for designing and developing EPI-MIPs.

Structural analogs of 2-(4-fluorophenyl)-6-methyl-3-(pyridin-4-yl)pyrazolo[1,5-a]pyridine for targeting Candida albicans non-essential stress kinase Yck2 through protein-ligand binding and dynamics analysis.

The rise in drug-resistant fungal infections poses a significant public health concern, necessitating the development of new antifungal therapies. We aimed to address this challenge by targeting a yeast casein kinase of Candida albicans for antifungal drug development. The compound library contained 589 chemical structures similar to the previously identified kinase inhibitor GW461484A. Through virtual screening, four compounds with the PubChem IDs 102583821, 12982634, 102487860, and 86260205 were selected based on their binding energies. Hydrophobic bonds and van der Waals interactions stabilised the docked complexes. Comprehensive interaction studies and a 200-nanosecond molecular dynamics simulation suggested that these molecules can maintain stable interactions with the target, as evidenced by satisfactory RMSD and RMSF values. The Rg-RMSD-based Free Energy Landscape of these complexes indicated thermodynamic stability due to the presence of conformers with global minima. These promising findings highlight the potential for developing novel antifungal therapies targeting Yck2 in C. albicans. Further experimental validation is required to assess the efficacy of these compounds as antifungal agents. This research provides a significant step towards combating antifungal resistance and opens up a new avenue for drug discovery.

Precise metabolic modeling in post-omics era: accomplishments and perspectives.

Microbes have been extensively utilized for their sustainable and scalable properties in synthesizing desired bio-products. However, insufficient knowledge about intracellular metabolism has impeded further microbial applications. The genome-scale metabolic models (GEMs) play a pivotal role in facilitating a global understanding of cellular metabolic mechanisms. These models enable rational modification by exploring metabolic pathways and predicting potential targets in microorganisms, enabling precise cell regulation without experimental costs. Nonetheless, simplified GEM only considers genome information and network stoichiometry while neglecting other important bio-information, such as enzyme functions, thermodynamic properties, and kinetic parameters. Consequently, uncertainties persist particularly when predicting microbial behaviors in complex and fluctuant systems. The advent of the omics era with its massive quantification of genes, proteins, and metabolites under various conditions has led to the flourishing of multi-constrained models and updated algorithms with improved predicting power and broadened dimension. Meanwhile, machine learning (ML) has demonstrated exceptional analytical and predictive capacities when applied to training sets of biological big data. Incorporating the discriminant strength of ML with GEM facilitates mechanistic modeling efficiency and improves predictive accuracy. This paper provides an overview of research innovations in the GEM, including multi-constrained modeling, analytical approaches, and the latest applications of ML, which may contribute comprehensive knowledge toward genetic refinement, strain development, and yield enhancement for a broad range of biomolecules.

From docking to dynamics: Unveiling the potential non-peptide and non-covalent inhibitors of Mpro from natural products.

MOTIVATION: This study aims to investigate non-covalent and non-peptide inhibitors of Mpro, a crucial protein target, by employing a comprehensive approach that integrates molecular docking, molecular dynamics simulations, and top-hits activity predictions. The focus is on elucidating the non-covalent and non-peptide binding modes of potential inhibitors with Mpro. METHODS: We employed a semi-flexible molecular docking methodology, binding score and ADME screening, which are based on structure, to screen compounds from CMNPD and HERB in silico. These methodologies allowed us to find potential candidates depending on their binding values and interactions with the binding site of main protease. To further evaluate the stability of these interactions, we conducted molecular dynamics simulations and calculated binding energies. Ultimately, a top-hits activity prediction method was employed to prioritize compounds based on their predicted inhibitory potential. RESULTS: Through a combination of binding energy calculations and activity predictions, we identified six potential inhibitor molecules exhibiting promising activity against Mpro. These compounds demonstrated favorable binding interactions and stability profiles, making them attractive candidates for further experimental validation and drug development efforts targeting Mpro.

Hydrogen Bond Strengthens Acceptor Group: The Curious Case of the C-H···O=C Bond.

An H-bond involves the sharing of a hydrogen atom between an electronegative atom to which it is covalently bound (the donor) and another electronegative atom serving as an acceptor. Such bonds represent a critically important geometrical force in biological macromolecules and, as such, have been characterized extensively. H-bond formation invariably leads to a weakening within the acceptor moiety due to the pulling exerted by the donor hydrogen. This phenomenon can be compared to a spring connecting two masses; pulling one mass stretches the spring, similarly affecting the bond between the two masses. Herein, we describe the opposite phenomenon when investigating the energetics of the C-H···O=C bond. This bond underpins the most prevalent protein transmembrane dimerization motif (GxxxG) in which a glycine Cα-H on one helix forms a hydrogen bond with a carbonyl in a nearby helix. We use isotope-edited FT-IR spectroscopy and corroborating computational approaches to demonstrate a surprising strengthening of the acceptor C=O bond upon binding with the glycine Cα-H. We show that electronic factors associated with the Cα-H bond strengthen the C=O oscillator by increasing the s-character of the σ-bond, lowering the hyperconjugative disruption of the π-bond. In addition, a reduction of the acceptor C=O bond's polarity is observed upon the formation of the C-H···O=C bond. Our findings challenge the conventional understanding of H-bond dynamics and provide new insights into the structural stability of inter-helical protein interactions.

Nano-Liposomal Beetroot Phyto-Pigment in Photodynamic Therapy as a Prospective Green Approach for Cancer Management: In Vitro Evaluation and Molecular Dynamic Simulation.

Using plant extracts as photosensitizers in photodynamic therapy (PDT) represents a significant green approach toward sustainability. This study investigates beetroot juice (BRJ), betanin, and their liposomal formulations (Lip-BRJ, Lip-Bet) as photosensitizers in cancer PDT. BRJ was prepared, and its betanin content was quantified via HPLC. The p-nitrosodimethylaniline (RNO)/imidazole technique monitored the singlet oxygen formation. BRJ and betanin decreased the RNO absorbance at 440 nm by 12% and 9% after 45 min of irradiation, respectively. Furthermore, betanin interaction with Bcl-2 proteins was examined using binding free energy analysis and molecular dynamic simulation. The results revealed favorable interactions with ΔG values of -40.94 kcal/mol. Then, BRJ, betanin, Lip-BRJ, and Lip-Bet were tested as photosensitizers on normal (HEK 293) and human lung cancer (A549) cell lines. Irradiation significantly enhanced the cytotoxicity of Lip-Bet on HEK 293 cells (20% cell viability at 2000 µg/mL) and A549 cells (13% cell viability at 1000 µg/mL). For Lip-BRJ, irradiation significantly enhanced the cytotoxicity on HEK 293 cells at lower concentrations and on A549 cells at all tested concentrations. These results proved the positive effect of light and liposomal encapsulation on the anticancer activity of betanin and BRJ, suggesting the efficiency of liposomal beetroot pigments as green photosensitizers.

Characterization, structural, and evolutionary analysis of an extremophilic GH5 endoglucanase from Bacillus sp. G131: Insights from ancestral sequence reconstruction.

Nature has developed extremozymes that catalyze complex reaction processes in extreme environmental conditions. Accordingly, a combined approach consisting of extremozyme screening, ancestral sequence resurrection (ASR), and molecular dynamic simulation was utilized to construct a developed endoglucanase. The primary experimental and in-silico data led to the prediction of a hypothetical sequence of endoglucanase (EG5-G131) using Bacillus sp. G131 confirmed by amplification and sequencing. EG5-G131 exhibited noticeable stability in a broad-pH range, several detergents, organic solvents, and temperatures up to 80 °C. The molecular weight, Vmax, and Km of the purified endoglucanase were estimated to be 36 kDa, 4.32 µmol/min, and 23.62 mg/ml, respectively. The calculated thermodynamic parameters for EG5-G131 confirmed its intrinsic thermostability. Computational analysis revealed Glu142 and Glu230 as active-site residues of the enzyme. Furthermore, the enzyme remained bound to cellotetraose at 298 K, 333 K, 343 K, and 353 K for 300 ns, consistent with our experimental data. ASR of EG5-G131 led to the introduction of ancestral ANC204 and ANC205, which show similar thermodynamic characteristics with the last Firmicute common ancestor. Finally, truncating loops from the N-terminal of two sequences created two variants with desirable thermal stability, suggesting the evolutionary deciphering of the functional domain of the GH5 family in Bacillus sp. G131.

Disrupting protease and deubiquitinase activities of SARS-CoV-2 papain-like protease by natural and synthetic products discovered through multiple computational and biochemical approaches.

The single-stranded RNA genome of SARS-CoV-2 encodes several structural and non-structural proteins, among which the papain-like protease (PLpro) is crucial for viral replication and immune evasion and has emerged as a promising therapeutic target. The current study aims to discover new inhibitors of PLpro that can simultaneously disrupt its protease and deubiquitinase activities. Using multiple computational approaches, six compounds (CP1-CP6) were selected from our in-house compounds database, with higher docking scores (-7.97 kcal/mol to -8.14 kcal/mol) and fitted well in the active pocket of PLpro. Furthermore, utilizing microscale molecular dynamics simulations (MD), the dynamic behavior of selected compounds was studied. Those molecules strongly binds at the PLpro active site and forms stable complexes. The dynamic motions suggest that the binding of CP1-CP6 brought the protein to a closed conformational state, thereby altering its normal function. In an in vitro evaluation, CP2 showed the most significant inhibitory potential for PLpro (protease activity = 2.71 ± 0.33 µM and deubiquitinase activity = 3.11 ± 0.75 µM), followed by CP1, CP5, CP4 and CP6. Additionally, CP1-CP6 showed no cytotoxicity at a concentration of 30 µM in the human BJ cell line.