Computational identification of potential acetylcholinesterase (AChE) and monoamine oxidase-B inhibitors from Vitis vinifera: a case study of Alzheimer's disease (AD).

Alzheimer's disease (AD) is the most prevalent neurodegenerative disease that affects people aged 60 years and above. Yet, the discovery of potent therapeutic agents against this disease has no utmost progress and a number of drug candidates could not make it out of the clinical trials at varied stages. At the same time, the currently available anti-cholinesterase (AChE) and monoamine oxidase-B (MAO-B) for the treatment of AD can only improve the clinical symptoms while the recently approved immunotherapy agent "remains questionable. Thus, the need for novel therapeutic agents with the potential to treat the aetiology of the disease. Herein, this study sought to examine the potential of a number of bioactive compounds derived from Vitis vinifera as a promising agent against AChE and MAO-B. Using a computational approach via molecular docking 23 bioactive agents were screened against AChE and MAO-B, and the compounds with a binding score below that of the standard ligand were further subjected to drug-likeness and pharmacokinetic screening. Eight and thirteen of the studied agents optimally saturated the active pocket of the AChE and MAO-B respectively, forming principal interactions with a number of amino acids at the active pocket of the targets and among these compounds only rutin failed the drug-likeness test by violating four parameters while all showed moderate pharmacokinetics features. A number of Vitis vinifera-derived bioactive compounds show excellent inhibitory potential against AChE and MAO-B, and moderate pharmacokinetic features when compared to the reference ligand (tacrine). These compounds are therefore proposed as novel AChE and MAO-B inhibitors for the treatment of AD and wet-lab analysis is necessary to affirm their potency.

Integrated computational approach identifies potential inhibitors of ASK1-(JNK/P38) interaction signaling: new insights into cancer therapeutics.

Cancers are characterized by the aberrant expression of certain genes that trigger a cascade of molecular events that culminate in dysregulated cell division. Consequently, the inhibition of the products of these expressedgenes has emerged as a rational approach in cancer therapy. The apoptosis signal-regulating kinase 1 (ASK1) protein, encoded by the mitogen-activated protein kinase kinase kinase 5 (MAP3K5) gene, plays pertinent roles in the mediation of cell death induced by stress and inflammation, andis often found at elevated levels in cancer. Consequently, it has emerged as a molecular target for the development of potential chemotherapeutics through identification of selective inhibitors. However, there is still dearth of ASK1 inhibitors in clinical use. Hence, molecular modelling approaches were employed in this study to discover potential ASK1 inhibitors from phytochemicals. Twenty-five phytocompounds from four medicinal plants were tested for their inhibitory prowess via molecular docking. Interestingly, all the compounds exhibited promising inhibitory potentials for ASK1. However, further subjection to filtering procedures via different pipelines including drug-likeness evaluation, pharmacokinetics screening, toxicity profiling, and better affinities compared to the approved inhibitor resulted in three hit compounds namely ellagic acid, luteolin, and kaempferol with suitable properties. Profiling of the interactions formed between the hit\compounds and the targets revealed several interactions that were not present in that of the approved inhibitor, while molecular dynamics (MD) simulation revealed the complexes formed as stable. Conclusively, this study identified three compounds with ASK1 inhibitory potentials that are worthy of further exploration in in vitro and in vivo studies.Communicated by Ramaswamy H. Sarma.

Design of small molecules for CDK-2 inhibition in colorectal cancer based on substructure search.

The global frequency of colorectal cancer motivates extensive drug discovery efforts. CDK2, a key member of the CDK family, has been linked to tumor progression, unregulated cell proliferation, and growth promotion. Water-soluble flavonoids with a fast metabolism called anthocyanins have been shown to have a variety of pharmacological properties, including anti-cancer properties. This study aims to find possible CDK2 inhibitors from Anthocyanin-like molecules. Anthocyanins sourced from PubChem were screened using a virtual screening approach that included a KNIME workflow, QSAR-model, Pharmacophore hypothesis, and a structure-based screening to identify compounds with a better binding affinity and predicted bioactivity compared to the standard, Sorafenib. The top compounds were subjected to a 100 ns MD simulation to confirm their stability at the active site. Compounds 1-5 were shown to have higher binding affinity and bioactivity in this study. These substances interacted with the critical amino acids (LEU 83, ASP 145 and LYS 89) at CDK2's active site. Compared to the reference with a pIC50 value of 6.003 nM, the top compounds listed have superior predicted bioactivity ranging from 6.539 to 6.36 nM. Also, ADMET predictions predicted that Compounds 1-5 were not carcinogenic and not a p-glycoprotein substrate. MD simulation also validated Compound 1's stability at the active site compared to the standard. This study uncovers potential CDK2 inhibitors with good binding affinities, shedding light on their interactions with the target protein. While promising, further in vivo and in vitro investigations are essential to validate the anticancer potential of these compounds.Communicated by Ramaswamy H. Sarma.

Computational identification of potential inhibitors targeting cdk1 in colorectal cancer.

Introduction: Despite improved treatment options, colorectal cancer (CRC) remains a huge public health concern with a significant impact on affected individuals. Cell cycle dysregulation and overexpression of certain regulators and checkpoint activators are important recurring events in the progression of cancer. Cyclin-dependent kinase 1 (CDK1), a key regulator of the cell cycle component central to the uncontrolled proliferation of malignant cells, has been reportedly implicated in CRC. This study aimed to identify CDK1 inhibitors with potential for clinical drug research in CRC. Methods: Ten thousand (10,000) naturally occurring compounds were evaluated for their inhibitory efficacies against CDK1 through molecular docking studies. The stability of the lead compounds in complex with CDK1 was evaluated using molecular dynamics simulation for one thousand (1,000) nanoseconds. The top-scoring candidates' ADME characteristics and drug-likeness were profiled using SwissADME. Results: Four hit compounds, namely, spiraeoside, robinetin, 6-hydroxyluteolin, and quercetagetin were identified from molecular docking analysis to possess the least binding scores. Molecular dynamics simulation revealed that robinetin and 6-hydroxyluteolin complexes were stable within the binding pocket of the CDK1 protein. Discussion: The findings from this study provide insight into novel candidates with specific inhibitory CDK1 activities that can be further investigated through animal testing, clinical trials, and drug development research for CRC treatment.

Discovery of putative inhibitors against main drivers of SARS-CoV-2 infection: Insight from quantum mechanical evaluation and molecular modeling.

SARS-CoV-2 triggered a worldwide medical crisis, affecting the world's social, emotional, physical, and economic equilibrium. However, treatment choices and targets for finding a solution to COVID-19's threat are becoming limited. A viable approach to combating the threat of COVID-19 is by unraveling newer pharmacological and therapeutic targets pertinent in the viral survival and adaptive mechanisms within the host biological milieu which in turn provides the opportunity to discover promising inhibitors against COVID-19. Therefore, using high-throughput virtual screening, manually curated compounds library from some medicinal plants were screened against four main drivers of SARS-CoV-2 (spike glycoprotein, PLpro, 3CLpro, and RdRp). In addition, molecular docking, Prime MM/GBSA (molecular mechanics/generalized Born surface area) analysis, molecular dynamics (MD) simulation, and drug-likeness screening were performed to identify potential phytodrugs candidates for COVID-19 treatment. In support of these approaches, we used a series of computational modeling approaches to develop therapeutic agents against COVID-19. Out of the screened compounds against the selected SARS-CoV-2 therapeutic targets, only compounds with no violations of Lipinski's rule of five and high binding affinity were considered as potential anti-COVID-19 drugs. However, lonchocarpol A, diplacol, and broussonol E (lead compounds) were recorded as the best compounds that satisfied this requirement, and they demonstrated their highest binding affinity against 3CLpro. Therefore, the 3CLpro target and the three lead compounds were selected for further analysis. Through protein-ligand mapping and interaction profiling, the three lead compounds formed essential interactions such as hydrogen bonds and hydrophobic interactions with amino acid residues at the binding pocket of 3CLpro. The key amino acid residues at the 3CLpro active site participating in the hydrophobic and polar inter/intra molecular interaction were TYR54, PRO52, CYS44, MET49, MET165, CYS145, HIS41, THR26, THR25, GLN189, and THR190. The compounds demonstrated stable protein-ligand complexes in the active site of the target (3CLpro) over a 100 ns simulation period with stable protein-ligand trajectories. Drug-likeness screening shows that the compounds are druggable molecules, and the toxicity descriptors established that the compounds demonstrated a good biosafety profile. Furthermore, the compounds were chemically reactive with promising molecular electron potential properties. Collectively, we propose that the discovered lead compounds may open the way for establishing phytodrugs to manage COVID-19 pandemics and new chemical libraries to prevent COVID-19 entry into the host based on the findings of this computational investigation.