Homology modeling, virtual screening, molecular docking, molecular dynamic (MD) simulation, and ADMET approaches for identification of natural anti-Parkinson agents targeting MAO-B protein

Discovery of novel DdlA inhibitors in multidrug-resistant Pseudomonas aeruginosa using virtual screening, molecular docking, and dynamics simulations

Pseudomonas aeruginosa is a gram-negative, opportunistic pathogen that represents a serious risk factor in healthcare services due to its natural resistance mechanisms and the increasing prevalence of multi-drug resistant strains. This study utilized in silico computational approaches to identify the novel inhibitors for D-alanine-D-alanine ligase A (DdlA), an essential enzyme for the bacterial peptidoglycan biosynthesis pathway necessary for cell wall integrity. A structure-based virtual screening of The Medicinal Fungi Secondary Metabolites and Therapeutics (MeFSAT) chemical library was conducted, followed by molecular docking to evaluate the binding affinity of small molecules to the DdlA active site. MSID000191, MSID000200, and MSID000102 were recognized as the leading candidates in the preliminary docking data due to their low binding energy values. These compounds exhibited binding energies markedly superior to the control drug (D-cycloserine), suggesting a substantial potential for inhibiting the DdlA enzyme. Detailed interaction analyses revealed significant salt bridges and hydrogen bonds with active site residues, which enhance the stability of the complex. Density Functional Theory (DFT) analysis and MMPBSA calculations also provided insights into electronic properties and binding free energy, respectively. These findings highlight the potential of these inhibitors as therapeutic candidates and showcase the effectiveness of computational methods in accelerating drug discovery against multidrug-resistant P. aeruginosa. Future research should incorporate more in-silico techniques and experimental validations to confirm these results.

Chemoinformatics Study of Benzodiazepine-1, 2, 3-triazole Derivatives Targeting Butyrylcholinesterase

This study aims to assess the potential bioactivity of newly designed benzodiazepine-1,2,3-triazole derivatives using in-silico methodologies, with a primary focus on elucidating their inhibitory interactions with the butyrylcholinesterase (BuChE) enzyme, which is implicated in Alzheimer's disease. We employed multiple linear regression (MLR) methods to conduct a quantitative structure-activity relationship (QSAR) analysis on a collection of 31 benzodiazepine-1,2,3-triazole derivatives, with the goal of investigating, assessing, and predicting their activities, as well as designing novel compounds. This approach yielded highly accurate results, with coefficients of determination (R²) of 0.77 and 0.81 for the training and test datasets, respectively. Additionally, the optimized compounds were subjected to an Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) analysis, demonstrating their potential as non-hepatotoxic agents with enhanced absorption and blood-brain barrier permeability. To further validate these findings, the most favorable docking conformations were analyzed using molecular dynamics (MD) simulations with GROMACS software, predicting the stability of the formed complexes. These simulations underscored the critical role of hydrogen bonds in stabilizing the compounds at the BuChE receptor binding site. The results hold great promise for the development of innovative benzodiazepine-1,2,3-triazole derivatives as effective BuChE inhibitors, potentially leading to therapeutic interventions for Alzheimer's disease.

Unveiling the anxiolytic and analgesic effects of citronellal in Swiss mice: in vivo and in silico insights into COX and GABAA receptor pathways

This study aims to evaluate the analgesic and anxiolytic effects of citronellal (CTL) in Swiss mice using two new sensitive and economic mouse models along with possible molecular mechanisms through in silico studies. For this, we employed marble displacement and spiked-field apparatus to check the anxiolytic and analgesic effects, respectively. In silico studies were done against cyclooxygenase (COX) enzymes and GABAA receptor subunits. Findings suggest that the test sample CTL significantly reduced the number of marbles displaces (NMD), square crosses (NSC), paw massages (NPM), and escaping attempts (NEA) in animals than the control group. CTL exhibited better effects in the perforated-field study compared to the reference drug diclofenac sodium. In both cases, CTL (200 mg/kg) significantly reduced the test parameters when combined with the standard drugs (diazepam or diclofenac sodium). In in silico studies, CTL showed binding affinities of - 5.5, - 5.2, - 4.8, and - 4.8 kcal/mol with the COX1, COX2, and GABAA receptors (α2 and α3 subunits), respectively. Taken together, CTL may exert anxiolytic- analgesic-like effects on mice, possibly through the GABAA receptor and COXs interaction pathways. These new tools might be used to check the anxiolytic and analgesic properties of a wide variety of test substances.

In silico study to identify novel NEK7 inhibitors from natural sources by a combination strategy

Cancer poses a significant global health challenge and significantly contributes to mortality. NEK7, related to the NIMA protein kinase family, plays a crucial role in spindle assembly and cell division. The dysregulation of NEK7 is closely linked to the onset and progression of various cancers, especially colon and breast cancer, making it a promising target for cancer therapy. Nevertheless, the shortage of high-quality NEK7 inhibitors highlights the need for new therapeutic strategies. In this study, we utilized a multidisciplinary approach, including virtual screening, molecular docking, pharmacokinetics, molecular dynamics simulations (MDs), and MM/PBSA calculations, to evaluate natural compounds as NEK7 inhibitors comprehensively. Through various docking strategies, we identified three natural compounds: (-)-balanol, digallic acid, and scutellarin. Molecular docking revealed significant interactions at residues such as GLU112 and ALA114, with docking scores of -15.054, -13.059, and -11.547 kcal/mol, respectively, highlighting their potential as NEK7 inhibitors. MDs confirmed the stability of these compounds at the NEK7-binding site. Hydrogen bond analysis during simulations revealed consistent interactions, supporting their strong binding capacity. MM/PBSA analysis identified other crucial amino acids contributing to binding affinity, including ILE20, VAL28, ILE75, LEU93, ALA94, LYS143, PHE148, LEU160, and THR161, crucial for stabilizing the complex. This research demonstrated that these compounds exceeded dabrafenib in binding energy, according to MM/PBSA calculations, underscoring their effectiveness as NEK7 inhibitors. ADME/T predictions showed lower oral toxicity for these compounds, suggesting their potential for further development. This study highlights the promise of these natural compounds as bases for creating more potent derivatives with significant biological activities, paving the way for future experimental validation.

Animal models in neuroscience with alternative approaches: Evolutionary, biomedical, and ethical perspectives

Animal models have been a crucial tool in neuroscience research for decades, providing insights into the biomedical and evolutionary mechanisms of the nervous system, disease, and behavior. However, their use has raised concerns on several ethical, clinical, and scientific considerations. The welfare of animals and the 3R principles (replacement, reduction, refinement) are the focus of the ethical concerns, targeting the importance of reducing the stress and suffering of these models. Several laws and guidelines are applied and developed to protect animal rights during experimenting. Concurrently, in the clinic and biomedical fields, discussions on the relevance of animal model findings on human organisms have increased. Latest data suggest that in a considerable amount of time the animal model results are not translatable in humans, costing time and money. Alternative methods, such as in vitro (cell culture, microscopy, organoids, and micro physiological systems) techniques and in silico (computational) modeling, have emerged as potential replacements for animal models, providing more accurate data in a minimized cost. By adopting alternative methods and promoting ethical considerations in research practices, we can achieve the 3R goals while upholding our responsibility to both humans and other animals. Our goal is to present a thorough review of animal models used in neuroscience from the biomedical, evolutionary, and ethical perspectives. The novelty of this research lies in integrating diverse points of views to provide an understanding of the advantages and disadvantages of animal models in neuroscience and in discussing potential alternative methods.

A Survey on Computational Methods in Drug Discovery for Neurodegenerative Diseases

Currently, the age structure of the world population is changing due to declining birth rates and increasing life expectancy. As a result, physicians worldwide have to treat an increasing number of age-related diseases, of which neurological disorders represent a significant part. In this context, there is an urgent need to discover new therapeutic approaches to counteract the effects of neurodegeneration on human health, and computational science can be of pivotal importance for more effective neurodrug discovery. The knowledge of the molecular structure of the receptors and other biomolecules involved in neurological pathogenesis facilitates the design of new molecules as potential drugs to be used in the fight against diseases of high social relevance such as dementia, Alzheimer's disease (AD) and Parkinson's disease (PD), to cite only a few. However, the absence of comprehensive guidelines regarding the strengths and weaknesses of alternative approaches creates a fragmented and disconnected field, resulting in missed opportunities to enhance performance and achieve successful applications. This review aims to summarize some of the most innovative strategies based on computational methods used for neurodrug development. In particular, recent applications and the state-of-the-art of molecular docking and artificial intelligence for ligand- and target-based approaches in novel drug design were reviewed, highlighting the crucial role of in silico methods in the context of neurodrug discovery for neurodegenerative diseases.

Structure-based virtual screening of novel natural products as chalcone derivatives against SARS-CoV-2 Mpro

Coronavirus disease 2019 (COVID-19), which is caused by SARS-CoV-2, has spread quickly around the world, causing a global pandemic. It has infected more than 500 million people as of April 28, 2022. Much research has been reported to stop the virus from spreading, but there are currently no approved medicines to treat COVID-19. In this work, a dataset of 142 natural products collected from various medicinal plants was used to perform structure-based virtual screening (SBVS) through the combined application of molecular docking and molecular dynamics (MD) simulation methods. First, the dataset of compounds was optimized using the density functional theory (DFT) approach. The optimized compounds were then submitted to the first screening, which was done by the pKCM web server to look for drug-likeness and the PyRx to look for binding affinity. Among the 142 natural substances, 10 compounds were selected for docking validation. Compounds that interact with CYS145 and LEU141, the essential catalytic residues, as well as compounds with binding affinities less than -8.0 kcal/mol, are considered promising anti-SARS-CoV-2 drug candidates. The top-ranked compounds were then evaluated by MD simulations and MM-GBSA method. These results could help researchers come up with new natural compounds that could be used to treat SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Computer-aided drug design applied to a series of pyridinyl imidazole derivatives targeting p38α MAP kinase: 2D-QSAR, docking, MD simulation, and ADMET investigations

The p38 mitogen-activated protein kinase (MAPK) is a crucial target for chronic inflammation.