A robust computational quest: Discovering potential hits to improve the treatment of pyrazinamide-resistant Mycobacterium tuberculosis.

The rise of pyrazinamide (PZA)-resistant strains of Mycobacterium tuberculosis (MTB) poses a major challenge to conventional tuberculosis (TB) treatments. PZA, a cornerstone of TB therapy, must be activated by the mycobacterial enzyme pyrazinamidase (PZase) to convert its active form, pyrazinoic acid, which targets the ribosomal protein S1. Resistance, often associated with mutations in the RpsA protein, complicates treatment and highlights a critical gap in the understanding of structural dynamics and mechanisms of resistance, particularly in the context of the G97D mutation. This study utilizes a novel integration of computational techniques, including multiscale biomolecular and molecular dynamics simulations, physicochemical and medicinal chemistry predictions, quantum computations and virtual screening from the ZINC and Chembridge databases, to elucidate the resistance mechanism and identify lead compounds that have the potential to improve treatment outcomes for PZA-resistant MTB, namely ZINC15913786, ZINC20735155, Chem10269711, Chem10279789 and Chem10295790. These computational methods offer a cost-effective, rapid alternative to traditional drug trials by bypassing the need for organic subjects while providing highly accurate insight into the binding sites and efficacy of new drug candidates. The need for rapid and appropriate drug development emphasizes the need for robust computational analysis to justify further validation through in vitro and in vivo experiments.

Honey and Nigella sativa against COVID-19 in Pakistan (HNS-COVID-PK): A multicenter placebo-controlled randomized clinical trial.

Until now, no specific and effective treatment exists for coronavirus disease 2019 (COVID-19). Since honey and Nigella sativa (HNS) have established antiviral, antibacterial, antiinflammatory, antioxidant, and immunomodulatory properties, we tested their efficacy for this disease in a multicenter, placebo-controlled, and randomized clinical trial at four medical care facilities in Pakistan. RT-PCR confirmed COVID-19 adults showing moderate or severe disease were enrolled in the trial. Patients were randomly assigned in a 1:1 ratio to receive either honey (1 g kg-1 day-1 ) and Nigella sativa seeds (80 mg kg-1 day-1 ) or a placebo for up to 13 days along with standard care. The outcomes included symptoms' alleviation, viral clearance, and 30-day mortality in the intention-to-treat population. Three hundred and thirteen patients, 210 with moderate and 103 with severe disease, underwent randomization from April 30 to July 29, 2020. Among the moderate cases, 107 were assigned to HNS, whereas 103 were assigned to the placebo group. Among the severe cases, 50 were given HNS, and 53 were given the placebo. HNS resulted in ~50% reduction in time taken to alleviate symptoms as compared to placebo (moderate cases: 4 vs. 7 days, Hazard Ratio [HR]: 6.11; 95% Confidence Interval [CI]: 4.23-8.84, p < 0.0001 and for severe cases: 6 vs. 13 days, HR: 4.04; 95% CI: 2.46-6.64; p < 0.0001). HNS also cleared the virus earlier than placebo in both moderate cases (6 vs. 10 days, HR: 5.53; 95% CI: 3.76-8.14, p < 0.0001) and severe cases (8.5 vs. 12 days, HR: 4.32; 95% CI: 2.62-7.13, p < 0.0001). HNS further led to a better clinical score on day 6 with normal activity resumption in 63.6% vs. 10.9% among moderate cases (OR: 0.07; 95% CI: 0.03-0.13, p < 0.0001) and hospital discharge in 50% versus 2.8% in severe cases (OR: 0.03; 95% CI: 0.01-0.09, p < 0.0001). In severe cases, the mortality rate was less than 1/4th in the HNS group than in placebo (4% vs. 18.87%, OR: 0.18; 95% CI: 0.02-0.92, p = 0.029). No HNS-related adverse effects were observed. HNS, compared with placebo, significantly improved symptoms, expedited viral load clearance, and reduced mortality in COVID-19 patients. This trial was registered on April 15, 2020 with ClinicalTrials.gov Identifier: NCT04347382.

Morphometric and Myelin Basic Protein Expression Changes in Arcuate Nucleus Kisspeptin Neurons Underlie Activation of Hypothalamic Pituitary Gonadal-axis in Monkeys (Macaca Mulatta) during the Breeding Season.

INTRODUCTION: Kisspeptin is involved in the hypothalamic pituitary gonadal-axis' seasonal regulation in rodents and sheep. Studies of kisspeptin signaling in regulating the transition between breeding and nonbreeding seasons have focused on kisspeptin expression, myelin basic protein (MBP) expression around kisspeptin-ir cells, and quantifying the synaptic connections between kisspeptin and gonadotropin-releasing hormone (GnRH) neurons in various animal models; however, the role of kisspeptin in regulating the seasonal breeding of primates has not been explored yet. OBJECTIVE: This study investigated changes in kisspeptin signaling during breeding and a non-breeding season in a non-human primate model, the rhesus monkey. METHODS: Three adult male monkeys (n = 3) from the breeding season and two monkeys (n = 2) from the non-breeding season were used in this study. After measuring the testicular volume and collecting a single blood sample, all animals were humanely euthanized under controlled conditions, and their hypothalami were collected and processed. Two 20 µm thick hypothalamic sections (mediobasal hypothalamus) from each animal were processed for kisspeptin-MBP and kisspeptin-GnRH immunohistochemistry (IHC). One section from each animal was used as a primary antibody omitted control to check the nonspecific binding in each IHC. RESULTS: Compared to the non-breeding season, plasma testosterone levels and testicular volumes were significantly higher in monkeys during the breeding season. Furthermore, compared to the non-breeding season, increased kisspeptin expression and a higher number of synaptic contacts between kisspeptin fibers and GnRH cell bodies were observed in the arcuate nucleus of the breeding season monkeys. In contrast, enlarged kisspeptin soma and higher MBP expression were observed in non-breeding monkeys. CONCLUSION: Our results indicated enhanced kisspeptin signaling in primate hypothalamus during the breeding season. These findings support the idea that kisspeptin acts as a mediator for the seasonal regulation of the reproductive axis in higher primates.

Structural Homology-Based Drug Repurposing Approach for Targeting NSP12 SARS-CoV-2.

The severe acute respiratory syndrome coronavirus 2, also known as SARS-CoV-2, is the causative agent of the COVID-19 global pandemic. SARS-CoV-2 has a highly conserved non-structural protein 12 (NSP-12) involved in RNA-dependent RNA polymerase (RdRp) activity. For the identification of potential inhibitors for NSP-12, computational approaches such as the identification of homologous proteins that have been previously targeted by FDA-approved antivirals can be employed. Herein, homologous proteins of NSP-12 were retrieved from Protein DataBank (PDB) and the evolutionary conserved sequence and structure similarity of the active site of the RdRp domain of NSP-12 was characterized. The identified homologous structures of NSP-12 belonged to four viral families: Coronaviridae, Flaviviridae, Picornaviridae, and Caliciviridae, and shared evolutionary conserved relationships. The multiple sequences and structural alignment of homologous structures showed highly conserved amino acid residues that were located at the active site of the RdRp domain of NSP-12. The conserved active site of the RdRp domain of NSP-12 was evaluated for binding affinity with the FDA-approved antivirals, i.e., Sofosbuvir and Dasabuvir in a molecular docking study. The molecular docking of Sofosbuvir and Dasabuvir with the active site that contains conserved motifs (motif A-G) of the RdRp domain of NSP-12 revealed significant binding affinity. Furthermore, MD simulation also inferred the potency of Sofosbuvir and Dasabuvir. In conclusion, targeting the active site of the RdRp domain of NSP-12 with Dasabuvir and Sofosbuvir might reduce viral replication and pathogenicity and could be further studied for the treatment of SARS-CoV-2.

Expression and co-localization of RFRP-3 and kisspeptin during breeding and non-breeding season in the hypothalamus of male rhesus monkey (Macaca mulatta).

Propose: The mechanism that underpins how RFRP-3 and kisspeptin interacts are not fully understood in higher primates. This study therefore set out to assess RFRP-3 and kisspeptin expression and their morphological interactions in the breeding, and in the non-breeding period in monkey hypothalamus. Methods: Eight mature male macaques (Macaca mulatta) in the breeding season (February; n = 4) and non-breeding season (June; n = 4) were used. To reveal the expression and co-localization of RFRP-3 and kisspeptin, double-labeled immunohistochemistry was performed. Testicular volume, sperm count, and plasma testosterone level were also measured to validate the breeding and non-breeding paradigms. Results: Testicular volume, plasma testosterone level, and sperm count showed a significant reduction during non-breeding season. The number of kisspeptin-positive cells was significantly increased during the breeding season (p < 0.05), whereas more RFRP-3-positive cell bodies were seen in the non-breeding season (p < 0.01). Close contacts of RFRP-3 fibers with kisspeptin cells showed no significant difference (p > 0.05) across seasons. However, co-localization of RFRP-3-ir cell bodies onto kisspeptin IR cell bodies showed a statistical increase (p < 0.01) in non-breeding season. Conclusion: In higher primates, RFRP-3 decreases kisspeptin drives from the same cells to GnRH neurons in an autocrine manner causing suppression of the reproductive axis during the non-breeding period.

Age-dependent changes in the reproductive axis responsiveness to kisspeptin-10 administration in healthy men.

The present study was designed to assess the responsiveness of hypothalamic-pituitary-gonadal axis to kisspeptin administration with increasing age in men. Human kisspeptin-10 was administered in single iv bolus dose (1 µg/kg BW) to healthy adult, middle and advanced age men. Serial blood samples were collected for 30 min pre- and 120 min post-kisspeptin injection periods at 30-min interval. Analysis of plasma LH by ELISA showed a significant (p < 0.05) increase after kisspeptin-10 administration in all groups, whereas plasma testosterone concentration was significantly elevated (p < 0.05) after kisspeptin-10 injection only in the adult men group. Present results suggest that in men, central hypothalamic-pituitary axis remains active and shows responsiveness to kisspeptin stimulation across life. However, Leydig cell responsiveness to kisspeptin-induced LH decreases with age in men.

Immunocytochemical localization of kisspeptin and kisspeptin receptor in the primate testis.

BACKGROUND: Hypothalamic kisspeptin-kisspeptin receptor signalling in primates ensures the successful progression into puberty during development and maintenance of reproductive capacity during adulthood. Human testis has been shown to express high-to-moderate levels of kisspeptin and kisspeptin receptor gene expression. In this study, we aimed at characterizing the localization of kisspeptin and kisspeptin receptor in adult primate testis tissue. METHODS: Immunocytochemistry was performed on paraffin-embedded testicular sections from adult rhesus monkeys and from common marmoset monkeys. RESULTS: Kisspeptin receptor was detected in Sertoli cells in the periphery of the seminiferous tubules in adult testes of both species. In contrast, kisspeptin was not localized in the seminiferous epithelium and was detected only in the interstitial compartment of the adult rhesus monkey testis. CONCLUSION: Kisspeptin receptor and kisspeptin are localized in the testis of Old World and New World primates.

Unraveling the mechanisms of Sofosbuvir resistance in HCV NS3/4A protease: Structural and molecular simulation-based insights.

Current management of HCV infection is based on Direct-Acting Antiviral Drugs (DAAs). However, resistance-associated mutations, especially in the NS3 and NS5B regions are gradually decreasing the efficacy of DAAs. Among the most effective HCV NS3/4A protease drugs, Sofosbuvir also develops resistance due to mutations in the NS3 and NS5B regions. Four mutations at positions A156Y, L36P, Q41H, and Q80K are classified as high-level resistance mutations. The resistance mechanism of HCV NS3/4A protease toward Sofosbuvir caused by these mutations is still unclear, as there is less information available regarding the structural and functional effects of the mutations against Sofosbuvir. In this work, we combined molecular dynamics simulation, molecular mechanics/Generalized-Born surface area calculation, principal component analysis, and free energy landscape analysis to explore the resistance mechanism of HCV NS3/4A protease due to these mutations, as well as compare interaction changes in wild-type. Subsequently, we identified that the mutant form of HCV NS3/4A protease affects the activity of Sofosbuvir. In this study, the resistance mechanism of Sofosbuvir at the atomic level is proposed. The proposed drug-resistance mechanism will provide valuable guidance for the design of HCV drugs.

Chemoenzymatic synthesis of (1R,3R)-3-hydroxycyclopentanemethanol: An intermediate of carbocyclic-ddA.

The synthesis of carbocyclic-ddA, a potent antiviral agent against hepatitis B, relies significantly on (1R,3R)-3-hydroxycyclopentanemethanol as a key intermediate. To effectively produce this intermediate, our study employed a chemoenzymatic approach. The selection of appropriate biocatalysts was based on substrate similarity, leading us to adopt the CrS enoate reductase derived from Thermus scotoductus SA-01. Additionally, we developed an enzymatic system for NADH regeneration, utilising formate dehydrogenase from Candida boidinii. This system facilitated the efficient catalysis of (S)-4-(hydroxymethyl)cyclopent-2-enone, resulting in the formation of (3R)-3-(hydroxymethyl) cyclopentanone. Furthermore, we successfully cloned, expressed, purified, and characterized the CrS enzyme in Escherichia coli. Optimal reaction conditions were determined, revealing that the highest activity occurred at 45 °C and pH 8.0. By employing 5 mM (S)-4-(hydroxymethyl)cyclopent-2-enone, 0.05 mM FMN, 0.2 mM NADH, 10 µM CrS, 40 µM formic acid dehydrogenase, and 40 mM sodium formate, complete conversion was achieved within 45 min at 35 °C and pH 7.0. Subsequently, (1R,3R)-3-hydroxycyclopentanemethanol was obtained through a simple three-step chemical conversion process. This study not only presents an effective method for synthesizing the crucial intermediate but also highlights the importance of biocatalysts and enzymatic systems in chemoenzymatic synthesis approaches.

Machine learning and molecular simulation-based protocols to identify novel potential inhibitors for reverse transcriptase against HIV infections.

Acquired immunodeficiency syndrome (AIDS) is a potentially fatal condition affecting the human immune system, which is attributed to the human immunodeficiency virus (HIV). The suppression of reverse transcriptase activity is a promising and feasible strategy for the therapeutic management of AIDS. In this study, we employed machine learning algorithms, such as support vector machines (SVM), k-nearest neighbor (k-NN), random forest (RF), and Gaussian naive base (GNB), which are fast and effective tools commonly used in drug design. For model training, we initially obtained a dataset of 5,159 compounds from BindingDB. The models were assessed using tenfold cross-validation to ensure their accuracy and reliability. Among these compounds, 1,645 compounds were labeled as active, having an IC50 below 0.49 µM, while 3,514 compounds were labeled "inactive against reverse transcriptase. Random forest achieved 86% accuracy on the train and test set among the different machine learning algorithms. Random forest model was then applied to an external ZINC dataset. Subsequently, only three hits-ZINC1359750464, ZINC1435357562, and ZINC1545719422-were selected based on the Lipinski Rule, docking score, and good interaction. The stability of these molecules was further evaluated by deploying molecular dynamics simulation and MM/GBSA, which were found to be -38.6013 ± 0.1103 kcal/mol for the Zidovudine/RT complex, -59.1761 ± 2.2926 kcal/mol for the ZINC1359750464/RT complex, -47.6292 ± 2.4206 kcal/mol for the ZINC1435357562/RT complex, and -50.7334 ± 2.5713 kcal/mol for the ZINC1545719422/RT complex.Communicated by Ramaswamy H. Sarma.

Evaluation of machine learning models for predicting TiO2 photocatalytic degradation of air contaminants.

The escalation of global urbanization and industrial expansion has resulted in an increase in the emission of harmful substances into the atmosphere. Evaluating the effectiveness of titanium dioxide (TiO2) in photocatalytic degradation through traditional methods is resource-intensive and complex due to the detailed photocatalyst structures and the wide range of contaminants. Therefore in this study, recent advancements in machine learning (ML) are used to offer data-driven approach using thirteen machine learning techniques namely XG Boost (XGB), decision tree (DT), lasso Regression (LR2), support vector regression (SVR), adaBoost (AB), voting Regressor (VR), CatBoost (CB), K-Nearest Neighbors (KNN), gradient boost (GB), random Forest (RF), artificial neural network (ANN), ridge regression (RR), linear regression (LR1) to address the problem of estimation of TiO2 photocatalytic degradation rate of air contaminants. The models are developed using literature data and different methodical tools are used to evaluate the developed ML models. XGB, DT and LR2 models have high R2 values of 0.93, 0.926 and 0.926 in training and 0.936, 0.924 and 0.924 in test phase. While ANN, RR and LR models have lowest R2 values of 0.70, 0.56 and 0.40 in training and 0.62, 0.63 and 0.31 in test phase respectively. XGB, DT and LR2 have low MAE and RMSE values of 0.450 min-1/cm2, 0.494 min-1/cm2 and 0.49 min-1/cm2 for RMSE and 0.263 min-1/cm2, 0.285 min-1/cm2 and 0.29 min-1/cm2 for MAE in test stage. XGB, DT, and LR2 have 93% percent errors within 20% error range in training phase. XGB has 92% and DT, and LR2 have 94% errors with 20% range in test phase. XGB, DT, LR2 models remained the highest performing models and XGB is the most robust and effective in predictions. Feature importances reveal the role of input parameters in prediction made by developed ML models. Dosage, humidity, UV light intensity remain important experimental factors. This study will impact positively in providing efficient models to estimate photocatalytic degradation rate of air contaminants using TiO2.

Targeting human progesterone receptor (PR), through pharmacophore-based screening and molecular simulation revealed potent inhibitors against breast cancer.

Breast cancer, the prevailing malignant tumor among women, is linked to progesterone and its receptor (PR) in both tumorigenesis and treatment responsiveness. Despite thorough investigation, the precise molecular mechanisms of progesterone in breast cancer remain unclear. The human progesterone receptor (PR) serves as an essential therapeutic target for breast cancer treatment, warranting the rapid design of small molecule therapeutics that can effectively inhibit HPR. By employing cutting-edge computational techniques like molecular screening, simulation, and free energy calculation, the process of identifying potential lead molecules from natural products has been significantly expedited. In this study, we employed pharmacophore-based virtual screening and molecular simulations to identify natural product-based inhibitors of human progesterone receptor (PR) in breast cancer treatment. High-throughput molecular screening of traditional Chinese medicine (TCM) and zinc databases was performed, leading to the identification of potential lead compounds. The analysis of binding modes for the top five compounds from both database provides valuable structural insights into the inhibition of HPR for breast cancer treatment. The top five hits exhibited enhanced stability and compactness compared to the reference compound. In conclusion, our study provides valuable insights for identifying and refining lead compounds as HPR inhibitors.

Immunoinformatics-driven In silico vaccine design for Nipah virus (NPV): Integrating machine learning and computational epitope prediction.

The Nipah virus (NPV) is a highly lethal virus, known for its significant fatality rate. The virus initially originated in Malaysia in 1998 and later led to outbreaks in nearby countries such as Bangladesh, Singapore, and India. Currently, there are no specific vaccines available for this virus. The current work employed the reverse vaccinology method to conduct a comprehensive analysis of the entire proteome of the NPV virus. The aim was to identify and choose the most promising antigenic proteins that could serve as potential candidates for vaccine development. We have also designed B and T cell epitopes-based vaccine candidate using immunoinformatics approach. We have identified a total of 5 novel Cytotoxic T Lymphocytes (CTL), 5 Helper T Lymphocytes (HTL), and 6 linear B-cell potential antigenic epitopes which are novel and can be used for further vaccine development against Nipah virus. Then we performed the physicochemical properties, antigenic, immunogenic and allergenicity prediction of the designed vaccine candidate against NPV. Further, Computational analysis indicated that these epitopes possessed highly antigenic properties and were capable of interacting with immune receptors. The designed vaccine were then docked with the human immune receptors, namely TLR-2 and TLR-4 showed robust interaction with the immune receptor. Molecular dynamics simulations demonstrated robust binding and good dynamics. After numerous dosages at varied intervals, computational immune response modeling showed that the immunogenic construct might elicit a significant immune response. In conclusion, the immunogenic construct shows promise in providing protection against NPV, However, further experimental validation is required before moving to clinical trials.

Investigating the role of functional mutations in leucine binding to Sestrin2 in aging and age-associated degenerative pathologies using structural and molecular simulation approaches.

Leucine is the native known ligand of Sestrin2 (Sesn2) and its interaction with Sesn2 is particularly important, as it influences the activity of mTOR in aging and its associated pathologies. It is important to find out how leucine interacts with Sesn2 and how mutations in the binding pocket of leucine affect the binding of leucine. Therefore, this study was committed to investigating the impact of non-synonymous mutations by incorporating a broad spectrum of simulation techniques, from molecular dynamics to free energy calculations. Our study was designed to model the atomic-scale interactions between leucine and mutant forms of Sesn2. Our results demonstrated that the interaction paradigm for the mutants has been altered thus showing a significant decline in the hydrogen bonding network. Moreover, these mutations compromised the dynamic stability by altering the conformational flexibility, sampling time, and leucine-induced structural constraints that consequently caused variation in the binding and structural stability. Molecular dynamics-based flexibility analysis revealed that the regions 217-339 and 371-380 demonstrated a higher fluctuation. Noteworthy, these regions correspond to a linker (217-339) and a loop (371-380) that cover the leucine binding cavity that is critical for the 'latch' mechanism in the N-terminal, which is essential for leucine binding. Further validation of reduced binding and modified internal motions caused by the mutants was obtained through binding free energy calculations, principal components analysis (PCA), and free energy landscape (FEL) analysis. By unraveling the molecular intricacies of Sesn2-leucine interactions and their mutations, we hope to pave the way for innovative strategies to combat the inevitable tide of aging and its associated diseases.Communicated by Ramaswamy H. Sarma.

In silico design of peptide inhibitors for Dengue virus to treat Dengue virus-associated infections.

Dengue virus is a single positive-strand RNA virus that is composed of three structural proteins including capsid, envelope, and precursor membrane while seven non-structural proteins (NS1, NS2A, NS2B, NS3A, NS3B, NS4, and NS5). Dengue is a viral infection caused by the dengue virus (DENV). DENV infections are asymptomatic or produce only mild illness. However, DENV can occasionally cause more severe cases and even death. There is no specific treatment for dengue virus infections. Therapeutic peptides have several important advantages over proteins or antibodies: they are small in size, easy to synthesize, and have the ability to penetrate the cell membranes. They also have high activity, specificity, affinity, and less toxicity. Based on the known peptide inhibitor, the current study designs peptide inhibitors for dengue virus envelope protein using an alanine and residue scanning technique. By replacing I21 with Q21, L14 with H14, and V28 with K28, the binding affinity of the peptide inhibitors was increased. The newly designed peptide inhibitors with single residue mutation improved the binding affinity of the peptide inhibitors. The inhibitory capability of the new promising peptide inhibitors was further confirmed by the utilization of MD simulation and free binding energy calculations. The molecular dynamics simulation demonstrated that the newly engineered peptide inhibitors exhibited greater stability compared to the wild-type peptide inhibitors. According to the binding free energies MM(GB)SA of these developed peptides, the first peptide inhibitor was the most effective against the dengue virus envelope protein. All peptide derivatives had higher binding affinities for the envelope protein and have the potential to treat dengue virus-associated infections. In this study, new peptide inhibitors were developed for the dengue virus envelope protein based on the already reported peptide inhibitor.

Vitamin B6 Via p-JNK/Nrf-2/NF-κB Signaling Ameliorates Cadmium Chloride-Induced Oxidative Stress Mediated Memory Deficits in Mice Hippocampus.

BACKGROUND: Cadmium chloride (Cd) is a pervasive environmental heavy metal pollutant linked to mitochondrial dysfunction, memory loss, and genetic disorders, particularly in the context of neurodegenerative diseases like Alzheimer's disease (AD). METHODS: This study investigated the neurotherapeutic potential of vitamin B6 (Vit. B6) in mitigating Cd-induced oxidative stress and neuroinflammation-mediated synaptic and memory dysfunction. Adult albino mice were divided into four groups: Control (saline-treated), Cd-treated, Cd+Vit. B6- treated, and Vit. B6 alone-treated. Cd and Vit. B6 were administered intraperitoneally, and behavioral tests (Morris Water Maze, Y-Maze) were conducted. Subsequently, western blotting, antioxidant assays, blood glucose, and hyperlipidemia assessments were performed. RESULTS: Cd-treated mice exhibited impaired cognitive function, while Cd+Vit. B6-treated mice showed significant improvement. Cd-induced neurotoxic effects, including oxidative stress and neuroinflammation, were observed, along with disruptions in synaptic proteins (SYP and PSD95) and activation of p-JNK. Vit. B6 administration mitigated these effects, restoring synaptic and memory deficits. Molecular docking and MD simulation studies confirmed Vit. B6's inhibitory effect on IL-1ß, NRF2, and p-JNK proteins. CONCLUSION: These results highlight Vit. B6 as a safe therapeutic supplement to mitigate neurodegenerative disorders, emphasizing the importance of assessing nutritional interventions for combating environmental neurotoxicity in the interest of public health.

Exploring Citrus sinensis Phytochemicals as Potential Inhibitors for Breast Cancer Genes BRCA1 and BRCA2 Using Pharmacophore Modeling, Molecular Docking, MD Simulations, and DFT Analysis.

BACKGROUND: Structure-activity relationship (SAR) is considered to be an effective in silico approach when discovering potential antagonists for breast cancer due to gene mutation. Major challenges are faced by conventional SAR in predicting novel antagonists due to the discovery of diverse antagonistic compounds. Methodologyand Results: In predicting breast cancer antagonists, a multistep screening of phytochemicals isolated from the seeds of the Citrus sinensis plant was applied using feasible complementary methodologies. A three-dimensional quantitative structure-activity relationship (3D-QSAR) model was developed through the Flare project, in which conformational analysis, pharmacophore generation, and compound alignment were done. Ten hit compounds were obtained through the development of the 3D-QSAR model. For exploring the mechanism of action of active compounds against cocrystal inhibitors, molecular docking analysis was done through Molegro software (MVD) to identify lead compounds. Three new proteins, namely, 1T15, 3EU7, and 1T29, displayed the best Moldock scores. The quality of the docking study was assessed by a molecular dynamics simulation. Based on binding affinities to the receptor in the docking studies, three lead compounds (stigmasterol P8, epoxybergamottin P28, and nobiletin P29) were obtained, and they passed through absorption, distribution, metabolism, and excretion (ADME) studies via the SwissADME online service, which proved that P28 and P29 were the most active allosteric inhibitors with the lowest toxicity level against breast cancer. Then, density functional theory (DFT) studies were performed to measure the active compound's reactivity, hardness, and softness with the help of Gaussian 09 software. CONCLUSIONS: This multistep screening of phytochemicals revealed high-reliability antagonists of breast cancer by 3D-QSAR using flare, docking analysis, and DFT studies. The present study helps in providing a proper guideline for the development of novel inhibitors of BRCA1 and BRCA2.

In silico exploration of the potential inhibitory activities of in-house and ZINC database lead compounds against alpha-glucosidase using structure-based virtual screening and molecular dynamics simulation approach.

Inhibitors of α-glucosidase have been used to treat type-2 diabetes (T2DM) by preventing the breakdown of carbohydrates into glucose and prevent enhancing glucose conversion. Structure-based virtual screening (SBVS) was used to generate novel chemical scaffold-ligand α-glucosidase inhibitors. The databases were screened against the receptor α-glucosidase using SBVS and molecular dynamics simulation (MDS) techniques in this study. Based on molecular docking studies, three and two compounds of α-glucosidase inhibitors were chosen from a commercial database (ZINC) and an In-house database for this study respectively. The mode of binding interactions of the selected compounds later predicted their α-glucosidase inhibitory potential. Finally, one out of three lead compound from ZINC and one out of two lead compound from In-house database were shortlisted based on interactions. Furthermore, MDS and post-MDS strategies were used to refine and validate the shortlisted leads along with the reference acarbose/α-glucosidase. The Hits' ability to inhibit α-glucosidase was predicted by SBVS, indicating that these compounds have good inhibitory activities. The lead inhibitor's structure may serve as templates for the design of novel inhibitors, and in vitro testing to confirm their anti-diabetic potential is necessary. These insights can help rationally design new effective anti-diabetic drugs.Communicated by Ramaswamy H. Sarma.

Molecular modeling and simulation approaches to characterize potential molecular targets for burdock inulin to instigate protection against autoimmune diseases.

In the current study, we utilized molecular modeling and simulation approaches to define putative potential molecular targets for Burdock Inulin, including inflammatory proteins such as iNOS, COX-2, TNF-alpha, IL-6, and IL-1ß. Molecular docking results revealed potential interactions and good binding affinity for these targets; however, IL-1ß, COX-2, and iNOS were identified as the best targets for Inulin. Molecular simulation-based stability assessment demonstrated that inulin could primarily target iNOS and may also supplementarily target COX-2 and IL-1ß during DSS-induced colitis to reduce the role of these inflammatory mechanisms. Furthermore, residual flexibility, hydrogen bonding, and structural packing were reported with uniform trajectories, showing no significant perturbation throughout the simulation. The protein motions within the simulation trajectories were clustered using principal component analysis (PCA). The IL-1ß-Inulin complex, approximately 70% of the total motion was attributed to the first three eigenvectors, while the remaining motion was contributed by the remaining eigenvectors. In contrast, for the COX2-Inulin complex, 75% of the total motion was attributed to the eigenvectors. Furthermore, in the iNOS-Inulin complex, the first three eigenvectors contributed to 60% of the total motion. Furthermore, the iNOS-Inulin complex contributed 60% to the total motion through the first three eigenvectors. To explore thermodynamically favorable changes upon mutation, motion mode analysis was carried out. The Free Energy Landscape (FEL) results demonstrated that the IL-1ß-Inulin achieved a single conformation with the lowest energy, while COX2-Inulin and iNOS-Inulin exhibited two lowest-energy conformations each. IL-1ß-Inulin and COX2-Inulin displayed total binding free energies of - 27.76 kcal/mol and - 37.78 kcal/mol, respectively, while iNOS-Inulin demonstrated the best binding free energy results at - 45.89 kcal/mol. This indicates a stronger pharmacological potential of iNOS than the other two complexes. Thus, further experiments are needed to use inulin to target iNOS and reduce DSS-induced colitis and other autoimmune diseases.

Exploring the aroma profile and biomedical applications of Scutellaria nuristanica Rech. F.: A new insight as a natural remedy.

BACKGROUND: The Scutellaria genus has promising therapeutic capabilities as an aromatherapy. Based on that and local practices of S. nuristanica Rech. F. The essential oil was studied for the first time for its diverse biomedical applications. PURPOSE: This study aimed to evaluate and validate their therapeutic capabilities by screening the essential oil ingredients and examining their antimicrobial, antioxidant, carbonic anhydrase, and antidiabetic using further In silico assessment and In vivo anti-inflammatory and analgesic capabilities to devise novel sources as natural remedies alternative to the synthetic drugs. METHODS: Essential oil was obtained through hydrodistillation, and the constituents were profiled using GC-MS. The antimicrobial assessment was conducted using an agar well diffusion assay. Free radical scavenging capabilities were determined by employing DPPH and ABTS assay. The carbonic anhydrase-II was examined using colorimetric assay, while the antidiabetic significance was performed using α-Glucosidase assay. The anti-inflammatory significance was examined through carrageenan-induced paw edema, and the analgesic features of the essential oil were determined using an acetic acid-induced writhing assay. RESULTS: Fifty constituents were detected in S. nuristanica essential oil (SNEO), contributing 95.93 % of the total EO, with the predominant constituents being 24-norursa-3,12-diene (10.12 %), 3-oxomanoyl oxide (9.94 %), methyl 7-abieten-18-oate (8.85 %). SNEO presented significance resistance against the Gram-positive bacterial strains (GPBSs), Bacillus atrophaeus and Bacillus subtilis, as compared to the Salmonella typhi and Klebsiella pneumoniae, Gram-negative bacterial strains (GNBSs) as well as two fungal strains Aspergillus parasiticus and Aspergillus niger associated with their respective standards. Considerable free radical scavenging capacity was observed in DPPH compared to the ABTS assay when correlated with ascorbic acid. In addition, when equated with their standards, SNEO offered considerable in vitro carbonic anhydrase II and antidiabetic capabilities. Additionally, the antidiabetic behavior of the 9 dominant compounds of SNEO was tested via In silico techniques, such as molecular docking, which assisted in the assessment of the significance of binding contacts of protein with each chemical compound and pharmacokinetic evaluations to examine the drug-like characteristics. Molecular dynamic simulations at 100 ns and binding free energy evaluations such as PBSA and GBSA models explain the molecular mechanics and stability of molecular complexes. It was also observed that SNEO depicted substantial anti-inflammatory and analgesic capabilities. CONCLUSION: Hence, it was concluded that the SNEO comprises bioactive ingredients with biomedical significance, such as anti-microbial, antioxidant, CA-II, antidiabetic, anti-inflammatory, and analgesic agents. The computational validation also depicted that SNEO could be a potent source for the discovery of anti-diabetic drugs.