Atomoxetine: toxicological aspects of a new treatment for Attention Deficit Hyperactivity Disorder in Brazil.

Atomoxetine is a drug widely used for the treatment of the attention deficit hyperactivity disorder (ADHD) with reduced risk of adverse motor reactions and chemical dependence. However, the pharmacokinetics characteristics as well as the toxicological risk of atomoxetine deserves further investigation to comprehensively analyze the therapeutic and safety aspects of this drug. This study aimed to predict the physicochemical profile and medicinal chemistry characteristics of atomoxetine, alongside its pharmacokinetic properties-namely absorption, distribution, metabolism, and excretion-as well as its toxicology (ADMET) potential through the utilization of web-based in silico tools. This research emphasizes predicted physicochemical, medicinal chemistry, and absorption parameters of atomoxetine that could influence the efficacy and safety of this drug for ADHD treatment. Additionally, atomoxetine also presents noteworthy predicted risks of hepatotoxicity, cardiotoxicity, neurotoxicity, nephrotoxicity, respiratory system toxicity, skin toxicity, and carcinogenicity. These findings underscore the necessity for further assessments of atomoxetine's safety profile, particularly considering different patient populations and durations of drug treatment. The data reported here from in silico predictions suggest that closer monitoring is warranted when atomoxetine is administered to patients with ADHD. Moreover, controlled studies detailing reliable protocols for personalized dosing, considering the multifactorial variability in metabolism efficiency and toxicological potential, would enable a more comprehensive assessment of atomoxetine's safety profile.

Advancing molecular modeling and reverse vaccinology in broad-spectrum yellow fever virus vaccine development.

Yellow fever outbreaks are prevalent, particularly in endemic regions. Given the lack of an established treatment for this disease, significant attention has been directed toward managing this arbovirus. In response, we developed a multiepitope vaccine designed to elicit an immune response, utilizing advanced immunoinformatic and molecular modeling techniques. To achieve this, we predicted B- and T-cell epitopes using the sequences from all structural (E, prM, and C) and nonstructural proteins of 196 YFV strains. Through comprehensive analysis, we identified 10 cytotoxic T-lymphocyte (CTL) and 5T-helper (Th) epitopes that exhibited overlap with B-lymphocyte epitopes. These epitopes were further evaluated for their affinity to a wide range of human leukocyte antigen system alleles and were rigorously tested for antigenicity, immunogenicity, allergenicity, toxicity, and conservation. These epitopes were linked to an adjuvant ( ß -defensin) and to each other using ligands, resulting in a vaccine sequence with appropriate physicochemical properties. The 3D structure of this sequence was created, improved, and quality checked; then it was anchored to the Toll-like receptor. Molecular Dynamics and Quantum Mechanics/Molecular Mechanics simulations were employed to enhance the accuracy of docking calculations, with the QM portion of the simulations carried out utilizing the density functional theory formalism. Moreover, the inoculation model was able to provide an optimal codon sequence that was inserted into the pET-28a( +) vector for in silico cloning and could even stimulate highly relevant humoral and cellular immunological responses. Overall, these results suggest that the designed multi-epitope vaccine can serve as prophylaxis against the yellow fever virus.

In silico evaluation of anti-colorectal cancer inhibitors by Resveratrol derivatives targeting Armadillo repeats domain of APC: molecular docking and molecular dynamics simulation.

Colorectal cancer is the second leading cause of cancer-related deaths. In 2018, there were an estimated 1.8 million cases, and this number is expected to increase to 2.2 million by 2030. Despite its prevalence, the current therapeutic option has a lot of side effects and limitations. Therefore, this study was designed to employ a computational approach for the identification of anti-cancer inhibitors against colorectal cancer using Resveratrol derivatives. Initially, the pass prediction spectrum of 50 derivatives was conducted and selected top seven compounds based on the maximum pass prediction score. After that, a comprehensive analysis, including Lipinski Rule, pharmacokinetics, ADMET profile study, molecular orbitals analysis, molecular docking, molecular dynamic simulations, and MM-PBSA binding free energy calculations. The reported binding affinity ranges of Resveratrol derivatives from molecular docking were -6.1 kcal/mol to -7.9 kcal/mol against the targeted receptor of human armadillo repeats domain of adenomatous polyposis coli (APC) (PDB ID: 3NMW). Specifically, our findings reported that two compounds [(03) Resveratrol 3-beta-mono-D-glucoside, and (29) Resveratrol 3-Glucoside] displayed the highest level of effectiveness compared to all other derivatives (-7.7 kcal/mol and -7.9 kcal/mol), and favorable drug-likeness, and exceptional safety profiles. Importantly, almost all the molecules were reported as free from toxic effects. Subsequently, molecular dynamic simulations conducted over 100ns confirmed the stability of the top two ligand-protein complexes. These findings suggest that Resveratrol derivatives may be effective drug candidate to manage the colorectal cancer. However, further experimental research, such as in vitro/in vivo studies, is essential to validate these computational findings and confirm their practical value.

Identification of Helicobacter pylori-carcinogenic TNF-alpha-inducing protein inhibitors via daidzein derivatives through computational approaches.

Gastric cancer is considered a class 1 carcinogen that is closely linked to infection with Helicobacter pylori (H. pylori), which affects over 1 million people each year. However, the major challenge to fight against H. pylori and its associated gastric cancer due to drug resistance. This research gap had led our research team to investigate a potential drug candidate targeting the Helicobacter pylori-carcinogenic TNF-alpha-inducing protein. In this study, a total of 45 daidzein derivatives were investigated and the best 10 molecules were comprehensively investigated using in silico approaches for drug development, namely pass prediction, quantum calculations, molecular docking, molecular dynamics simulations, Lipinski rule evaluation, and prediction of pharmacokinetics. The molecular docking study was performed to evaluate the binding affinity between the target protein and the ligands. In addition, the stability of ligand-protein complexes was investigated by molecular dynamics simulations. Various parameters were analysed, including root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), radius of gyration (Rg), hydrogen bond analysis, principal component analysis (PCA) and dynamic cross-correlation matrix (DCCM). The results has confirmed that the ligand-protein complex CID: 129661094 (07) and 129664277 (08) formed stable interactions with the target protein. It was also found that CID: 129661094 (07) has greater hydrogen bond occupancy and stability, while the ligand-protein complex CID 129664277 (08) has greater conformational flexibility. Principal component analysis revealed that the ligand-protein complex CID: 129661094 (07) is more compact and stable. Hydrogen bond analysis revealed favourable interactions with the reported amino acid residues. Overall, this study suggests that daidzein derivatives in particular show promise as potential inhibitors of H. pylori.

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.

Predictive modeling for breast cancer classification in the context of Bangladeshi patients by use of machine learning approach with explainable AI.

Breast cancer has rapidly increased in prevalence in recent years, making it one of the leading causes of mortality worldwide. Among all cancers, it is by far the most common. Diagnosing this illness manually requires significant time and expertise. Since detecting breast cancer is a time-consuming process, preventing its further spread can be aided by creating machine-based forecasts. Machine learning and Explainable AI are crucial in classification as they not only provide accurate predictions but also offer insights into how the model arrives at its decisions, aiding in the understanding and trustworthiness of the classification results. In this study, we evaluate and compare the classification accuracy, precision, recall, and F1 scores of five different machine learning methods using a primary dataset (500 patients from Dhaka Medical College Hospital). Five different supervised machine learning techniques, including decision tree, random forest, logistic regression, naive bayes, and XGBoost, have been used to achieve optimal results on our dataset. Additionally, this study applied SHAP analysis to the XGBoost model to interpret the model's predictions and understand the impact of each feature on the model's output. We compared the accuracy with which several algorithms classified the data, as well as contrasted with other literature in this field. After final evaluation, this study found that XGBoost achieved the best model accuracy, which is 97%.

Alkaloids as drug leads in Alzheimer's treatment: Mechanistic and therapeutic insights.

Alzheimer's disease (AD) has few effective treatment options and continues to be a major global health concern. AD is a neurodegenerative disease that typically affects elderly people. Alkaloids have potential sources for novel drug discovery due to their diverse chemical structures and pharmacological activities. Alkaloids, natural products with heterocyclic nitrogen-containing structures, are considered potential treatments for AD. This review explores the neuroprotective properties of alkaloids in AD, focusing on their ability to regulate pathways such as amyloid-beta aggregation, oxidative stress, synaptic dysfunction, tau hyperphosphorylation, and neuroinflammation. The FDA has approved alkaloids such as acetylcholinesterase inhibitors like galantamine and rivastigmine. This article explores AD's origins, current market medications, and clinical applications of alkaloids in AD therapy. This review explores the development of alkaloid-based drugs for AD, focusing on pharmacokinetics, blood-brain barrier penetration, and potential adverse effects. Future research should focus on the clinical evaluation of promising alkaloids, developing recently discovered alkaloids, and the ongoing search for novel alkaloids for medical treatment. A pharmaceutical option containing an alkaloid may potentially slow down the progression of AD while enhancing its symptoms. This review highlights the potential of alkaloids as valuable drug leads in treating AD, providing a comprehensive understanding of their mechanisms of action and therapeutic implications.

Unlocking the Immunomodulatory Potential of Rosmarinic Acid Isolated from Punica granatum L. Using Bioactivity-Guided Approach: In Silico, In Vitro, and In Vivo Approaches.

BACKGROUND: Punica granatum L. is well-known for its multifaceted therapeutic potential, including anti-inflammatory and immunomodulatory activities. AIM: This study aimed to characterize an immunomodulatory compound isolated from Punica granatum L. using a bioactivity-guided approach. METHODS: Chromatographic techniques were adopted for isolation and purification of secondary metabolites. In silico, in vitro, and in vivo methods were performed to characterize the therapeutic potential of the isolated compound. RESULTS: Using preparative thin-layer chromatography, rosmarinic acid was isolated from F4 (column chromatography product obtained from a butanolic fraction of the extract). The impact of rosmarinic acid was assessed in rats using the neutrophil adhesion test, DTH response, and phagocytic index. In immunized rats, rosmarinic acid demonstrated significant immunomodulatory potential. Computational experiments, like molecular docking and molecular dynamics, were also conducted against two targeted receptors, Cereblon (PDB ID: 8AOQ) and human CD22 (PDB ID: 5VKM). Computational studies suggested that an increase in phagocytic index by rosmarinic acid could be attributed to inhibiting Cereblon and CD22. Pharmacokinetics and toxicity prediction also suggested the drug-likeness of rosmarinic acid. CONCLUSION: Rosmarinic acid is a potential candidate, but extensive research needs to be done to translate this molecule from bench to bedside.

Synthesis of Silver Nanoparticles Using Camellia sinensis Leaf Extract: Promising Particles for the Treatment of Cancer and Diabetes.

Both diabetes and cancer pose significant threats to public health. To overcome these challenges, nanobiotechnology offers innovative solutions for the treatment of these diseases. However, the synthesis of nanoparticles can be complex, costly and environmentally toxic. Therefore, in this study, we successfully synthesized Camellia sinensis silver nanoparticles (CS-AgNPs) biologically from methanolic leaf extract of C. sinensis and as confirmed by the visual appearance which exhibited strong absorption at 456 nm in UV-visible spectroscopy. The fourier transform infrared spectroscopy (FTIR) analysis revealed that phytochemicals of C. sinensis were coated with AgNPs. Scanning electron microscopy (SEM) analysis showed the spherical shape of CS-AgNPs, with a size of 15.954 nm, while X-ray diffraction spectrometry (XRD) analysis detected a size of 20.32 nm. Thermogravimetric analysis (TGA) indicated the thermal stability of CS-AgNPs. The synthesized CS-AgNPs significantly inhibited the ehrlich ascites carcinoma (EAC) cell growth with 53.42±1.101 %. The EAC cell line induced mice exhibited increased level of the serum aspartate aminotransferase (AST), alanine transaminase (ALT), and alkaline phosphatase (ALP), however this elevated serum parameter significantly reduced and controlled by the treatment with CS-AgNPs. Moreover, in a streptozotocin-induced diabetic mice model, CS-AgNPs greatly reduced blood glucose, total cholesterol, triglyceride, low-density lipoprotein (LDL) and creatinine levels. These findings highlight that the synthesized CS-AgNPs have significant anticancer and antidiabetic activities that could be used as promising particles for the treatment of these major diseases. However, pre-clinical and clinical trial should be addressed before use this particles as therapeutics agents.

Co-infections and immune-evading viral hybrids: A perspective.

Background and Aims: Co-infections occur when two or more different types of pathogens infect the same host at the same time. Initially, it may develop via a primary infection and then later segue into a superinfection. Although some research suggests that coinfections do not affect the effect of disease outcomes, alternate evidence says otherwise. While the disease outcomes are frequently influenced by the interactions between many viruses, how these viruses interact during coinfections is poorly understood. This article aims to shed light on the interaction between viruses at a cellular and subcellular level, and the clinical implications for the same. Methods: The articles were sought by conducting a thorough literature search on Google Scholar, ScienceDirect, PubMed, PubMed Central, Dimensions, and EBSCO Host, using keywords such as coinfections, virus, viral hybrids, and superinfection. The articles pertinent to the concept were then included. Results: There is a growing body of evidence that suggests the formation of hybrid viral particles (HVPs) which conjugate at the cellular and subcellular level. While the formation of HVPs is bizarre, it may potentially have a profound effect on the clinical manifestations. Conclusion: While there has been evidence of the formation of HVPs between a couple of viruses, researchers fear the existence of several other combinations, including zoonotic viruses. While this could be detrimental to the human race both at an individual-as well as a community-level, an in-depth understanding of the same may help in better management of the clinical manifestations of the disease.

Quantum biochemical analysis of the binding interactions between a potential inhibitory drug and the Ebola viral glycoprotein.

Ebola virus disease (EVD) causes outbreaks and epidemics in West Africa that persist until today. The envelope glycoprotein of Ebola virus (GP) consists of two subunits, GP1 and GP2, and plays a key role in anchoring or fusing the virus to the host cell in its active form on the virion surface. Toremifene (TOR) is a ligand that mainly acts as an estrogen receptor antagonist; however, a recent study showed a strong and efficient interaction with GP. In this context, we aimed to evaluate the energetic affinity features involved in the interaction between GP and toremifene by computer simulation techniques using the Molecular Fractionation Method with Conjugate Caps (MFCC) scheme and quantum-mechanical (QM) calculations, as well as missense mutations to assess protein stability. We identified ASP522, GLU100, TYR517, THR519, LEU186, LEU515 as the most attractive residues in the EBOV glycoprotein structure that form the binding pocket. We divided toremifene into three regions and evaluated that region i was more important than region iii and region ii for the formation of the TOR-GP1/GP2 complex, which might control the molecular remodeling process of TOR. The mutations that caused more destabilization were ARG134, LEU515, TYR517 and ARG559, while those that caused stabilization were GLU523 and ASP522. TYR517 is a critical residue for the binding of TOR, and is highly conserved among EBOV species. Our results may help to elucidate the mechanism of drug action on the GP protein of the Ebola virus and subsequently develop new pharmacological approaches against EVD.Communicated by Ramaswamy H. Sarma.

A clinical study and future prospects for bioactive compounds and semi-synthetic molecules in the therapies for Huntington's disease.

A neurodegenerative disorder (ND) refers to Huntington's disease (HD) which affects memory loss, weight loss, and movement dysfunctions such as chorea and dystonia. In the striatum and brain, HD most typically impacts medium-spiny neurons. Molecular genetics, excitotoxicity, oxidative stress (OS), mitochondrial, and metabolic dysfunction are a few of the theories advanced to explicit the pathophysiology of neuronal damage and cell death. Numerous in-depth studies of the literature have supported the therapeutic advantages of natural products in HD experimental models and other treatment approaches. This article briefly discusses the neuroprotective impacts of natural compounds against HD models. The ability of the discovered natural compounds to suppress HD was tested using either in vitro or in vivo models. Many bioactive compounds considerably lessened the memory loss and motor coordination brought on by 3-nitropropionic acid (3-NP). Reduced lipid peroxidation, increased endogenous enzymatic antioxidants, reduced acetylcholinesterase activity, and enhanced mitochondrial energy generation have profoundly decreased the biochemical change. It is significant since histology showed that therapy with particular natural compounds lessened damage to the striatum caused by 3-NP. Moreover, natural products displayed varying degrees of neuroprotection in preclinical HD studies because of their antioxidant and anti-inflammatory properties, maintenance of mitochondrial function, activation of autophagy, and inhibition of apoptosis. This study highlighted about the importance of bioactive compounds and their semi-synthetic molecules in the treatment and prevention of HD.

Targeted therapies of curcumin focus on its therapeutic benefits in cancers and human health: Molecular signaling pathway-based approaches and future perspectives.

The curry powder spices turmeric (Curcuma longa L.), which contains curcumin (diferuloylmethane), an orange-yellow chemical. Polyphenols are the most commonly used sources of curcumin. It combats oxidative stress and inflammation in diseases, such as hyperlipidemia, metabolic syndrome, arthritis, and depression. Most of these benefits are due to their anti-inflammatory and antioxidant properties. Curcumin consumption leads to decreased bioavailability, resulting in limited absorption, quick metabolism, and quick excretion, which hinders health improvement. Numerous factors can increase its bioavailability. Piperine enhances bioavailability when combined with curcumin in a complex. When combined with other enhancing agents, curcumin has a wide spectrum of health benefits. This review evaluates the therapeutic potential of curcumin with a specific emphasis on its approach based on molecular signaling pathways. This study investigated its influence on the progression of cancer, inflammation, and many health-related mechanisms, such as cell proliferation, apoptosis, and metastasis. Curcumin has a significant potential for the prevention and treatment of various diseases. Curcumin modulates several biochemical pathways and targets involved in cancer growth. Despite its limited tissue accumulation and bioavailability when administered orally, curcumin has proven useful. This review provides an in-depth analysis of curcumin's therapeutic applications, its molecular signaling pathway-based approach, and its potential for precision medicine in cancer and human health.

Mechanistic insight of Staphylococcus aureus associated skin cancer in humans by Santalum album derived phytochemicals: an extensive computational and experimental approaches.

An excessive amount of multidrug-resistant Staphylococcus aureus is commonly associated with actinic keratosis (AK) and squamous cell carcinoma (SCC) by secreted virulence products that induced the chronic inflammation leading to skin cancer which is regulated by staphylococcal accessory regulator (SarA). It is worth noting that there is currently no existing published study that reports on the inhibitory activity of phytochemicals derived from Santalum album on the SarA protein through in silico approach. Therefore, our study has been designed to find the potential inhibitors of S. aureus SarA protein from S. album-derived phytochemicals. The molecular docking study was performed targeting the SarA protein of S. aureus, and CID:5280441, CID:162350, and CID: 5281675 compounds showed the highest binding energy with -9.4 kcal/mol, -9.0 kcal/mol, and -8.6 kcal/mol respectively. Further, molecular dynamics simulation revealed that the docked complexes were relatively stable during the 100 ns simulation period whereas the MMPBSA binding free energy proposed that the ligands were sustained with their binding site. All three complexes were found to be similar in distribution with the apoprotein through PCA analysis indicating conformational stability throughout the MD simulation. Moreover, all three compounds' ADMET profiles revealed positive results, and the AMES test did not show any toxicity whereas the pharmacophore study also indicates a closer match between the pharmacophore model and the compounds. After comprehensive in silico studies we evolved three best compounds, namely, Vitexin, Isovitexin, and Orientin, which were conducted in vitro assay for further confirmation of their inhibitory activity and results exhibited all of these compounds showed strong inhibitory activity against S. aureus. The overall result suggests that these compounds could be used as a natural lead to inhibit the pathogenesis of S. aureus and antibiotic therapy for S. aureus-associated skin cancer in humans as well.

In silico designing of multiepitope-based-peptide (MBP) vaccine against MAPK protein express for Alzheimer's disease in Zebrafish.

Understanding the role of the mitogen-activated protein kinases (MAPKs) signalling pathway is essential in advancing treatments for neurodegenerative disorders like Alzheimer's. In this study, we investigate in-silico techniques involving computer-based methods to extract the MAPK1 sequence. Our applied methods enable us to analyze the protein's structure, evaluate its properties, establish its evolutionary relationships, and assess its prevalence in populations. We also predict epitopes, assess their ability to trigger immune responses, and check for allergenicity using advanced computational tools to understand their immunological properties comprehensively. We apply virtual screening, docking, and structure modelling to identify promising drug candidates, analyze their interactions, and enhance drug design processes. We identified a total of 30 cell-targeting molecules against the MAPK1 protein, where we selected top 10 CTL epitopes (PAGGGPNPG, GGGPNPGSG, SAPAGGGPN, AVSAPAGGG, AGGGPNPGS, ATAAVSAPA, TAAVSAPAG, ENIIGINDI, INDIIRTPT, and NDIIRTPTI) for further evaluation to determine their potential efficacy, safety, and suitability for vaccine design based on strong binding potential. The potential to cover a large portion of the world's population with these vaccines is substantial-88.5 % for one type and 99.99 % for another. In exploring the molecular docking analyses, we examined a library of compounds from the ZINC database. Among them, we identified twelve compounds with the lowest binding energy. Critical residues in the MAPK1 protein, such as VAL48, LYS63, CYS175, ASP176, LYS160, ALA61, LEU165, TYR45, SER162, ARG33, PRO365, PHE363, ILE40, ASN163, and GLU42, are pivotal for interactions with these compounds. Our result suggests that these compounds could influence the protein's behaviour. Moreover, our docking analyses revealed that the predicted peptides have a strong affinity for the MAPK1 protein. These peptides form stable complexes, indicating their potential as potent inhibitors. This study contributes to the identification of new drug compounds and the screening of their desired properties. These compounds could potentially help reduce the excessive activity of MAPK1, which is linked to Alzheimer's disease.

Novel computational and drug design strategies for the inhibition of human T-cell leukemia virus 1-associated lymphoma by Astilbin derivatives.

Human T-cell leukemia virus 1 (HTLV-1) associated lymphoma is a devastating malignancy triggered by HTLV-1 infections. We employeda comprehensive drug design and computational strategy in this work to explore the inhibitory activitiesof Astilbin derivatives against HTLV-1-associated lymphoma. We evaluated the stability, binding affinities, and various computational analysis of Astilbin derivatives against target proteins, such as HTLV-1 main protease and HTLV-1 capsid protein. The root mean square deviation (RMSD), root mean square fluctuation, radius of gyration, hydrogen bond analysis, principal component analysis (PCA) and dynamic cross-correlation matrix (DCCM) were applied to characterize these protein-ligand interactions further. Ligand-03 and ligand-04 exhibited notable binding affinity to HTLV-1 capsid protein, while ligand-05 displayed high binding affinity to HTLV-1 protease. MD simulation analysis revealed that ligand-03, bound to HTLV-1 capsid protein, demonstrated enhanced stability with lower RMSD values and fewer conformational changes, suggesting a promising binding orientation. Ligand-04, despite stable binding, exhibited increased structural deviations, making it less suitable. Ligand-05 demonstrated stable binding to HTLV-1 protease throughout the simulation period at 100 nanoseconds. Hydrogen bond analysis indicated that ligand-05 formed persistent hydrogen bonds with significantresidues, contributing to its stability. PCA highlighted ligand-03's more remarkable conformational changes, while DCCM showed ligand-05's distinct dynamics, indicating its different behavior in the complex. Furthermore, binding free energy calculations supported the favorable interactions of ligand-03 and ligand-04 with HTLV-1 capsid protein, while ligand-05 showed weaker interactions with HTLV-1 protease. Molecular electrostatic potential and frontier molecular orbital analyses provided insights into these compounds' charge distribution and stability. In conclusion, this research found Astilbin derivatives as potential inhibitors of HTLV-1-associated lymphoma. Future attempts at drug development will benefit from the steady interaction landscape provided by Ligand-03, Ligand-04 and Ligand-05, which showed the most attractive binding profile with the target protein. These results open up new opportunities for innovative drug development, and more experimental testing should be done between Astilbin derivatives and HTLV-1-associated lymphoma.Communicated by Ramaswamy H. Sarma.

Lupenone, a wonder chemical obtained from Euphorbia segetalis to boost affinity for the transcriptional factor escalating drought-tolerance in Solanum Lycopersicum: A cutting-edge computational biology approach.

Drought is the single greatest abiotic factor influencing crop yield worldwide. Plants remain in one area for extended periods, making them vulnerable to natural and man-made influences. Understanding plant drought responses will help us develop strategies for breeding drought-resistant crops. Large proteome analysis revealed that leaf and root tissue proteins respond to drought differently depending on the plant's genotype. Commonly known as tomatoes, Solanum Lycopersicum is a globally important vegetable crop. However, drought stress is one of the most significant obstacles to tomato production, making the development of cultivars adapted to dry conditions an essential goal of agricultural biotechnology. Breeders have put quite a lot of time and effort into the tomato to increase its productivity, adaptability, and resistance to biotic and abiotic challenges. However, conventional tomato breeding has only improved drought resistance due to the complexity of drought traits. The resilience of tomatoes under drought stress has been the subject of extensive study. Using contemporary sequencing approaches like genomics, transcriptomics, proteomics, and metabolomics has dramatically aided in discovering drought-responsive genes. One of the most prominent families of plant transcription factors, WRKY genes, plays a crucial role in plant growth and development in response to natural and abiotic stimuli. To develop plants that can withstand both biotic and abiotic stress, understanding the relationships between WRKY-proteins (transcription factors) and other proteins and ligands in plant cells is essential. This is despite the fact that tomatoes have a long history of domestication. This research aims to utilize Lupenone, a hormone produced in plant roots in response to stress, to increase drought resistance in plants. Lupenone exhibits a strong affinity for the WRKY protein at -9.64 kcal/mol. Molecular docking and modeling studies show that these polyphenols have a significant role in making Solanum Lycopersicum drought-resistant and improving the quality of its fruit. As a result of climate change, droughts are occurring more frequently and persisting for more extended periods, making it necessary to breed crops resistant to drought. While considerable variability for tolerance exists in wild cousins, little is known about the processes and essential genes influencing drought tolerance in cultivated tomato species.

Identification of synthetically tractable MERS-CoV main protease inhibitors using structure-based virtual screening and molecular dynamics potential of mean force (PMF) calculations.

The Middle East Respiratory Syndrome Coronavirus (MERS-CoV) is a potentially lethal infection that presents a substantial threat to health, especially in Middle East nations. Given that no FDA-approved specific therapy for MERS infection exists, designing and discovering a potent antiviral therapy for MERS-CoV is crucial. One pivotal strategy for inhibiting MERS replication is to focus on the viral main protease (Mpro). In this study, we identify potential novel Mpro inhibitors employing structure-based virtual screening of our recently reported Ugi reaction-derived library (URDL) consisting of cherry-picked molecules from the literature. The key features of the URDL library include synthetic tractability (1-2 pot synthesis) of the molecules scaffold and unexplored chemical space. The hits were ranked based on the docking score, MM-GBSA free energy of binding, and the interaction pattern with the active site residues. A molecular dynamics (MD) simulation study was performed for the first two top-ranked compounds to analyze the stability and free binding energy based on the molecular mechanics Poisson-Boltzmann surface area. The potential mean force calculated from the steered molecular dynamics (SMD) simulations of the hits indicates improved H-bond potential, enhanced conformational stability, and binding affinity toward the target, compared to the cocrystallized ligand. The discovered hits represent novel synthetically tractable scaffolds as potential MERS-CoV Mpro inhibitors.Communicated by Ramaswamy H. Sarma.

Advances of Reverse Vaccinology for mRNA Vaccine Design against SARS-CoV-2: A Review of Methods and Tools.

mRNA vaccines are a new class of vaccine that can induce potent and specific immune responses against various pathogens. However, the design of mRNA vaccines requires the identification and optimization of suitable antigens, which can be challenging and time consuming. Reverse vaccinology is a computational approach that can accelerate the discovery and development of mRNA vaccines by using genomic and proteomic data of the target pathogen. In this article, we review the advances of reverse vaccinology for mRNA vaccine design against SARS-CoV-2, the causative agent of COVID-19. We describe the steps of reverse vaccinology and compare the in silico tools used by different studies to design mRNA vaccines against SARS-CoV-2. We also discuss the challenges and limitations of reverse vaccinology and suggest future directions for its improvement. We conclude that reverse vaccinology is a promising and powerful approach to designing mRNA vaccines against SARS-CoV-2 and other emerging pathogens.