Integrated virtual screening and molecular dynamics simulation approaches revealed potential natural inhibitors for DNMT1 as therapeutic solution for triple negative breast cancer

Identification of promising inhibitors against breast cancer disease by targeting NUDIX hydrolase 5 (NUDT5) biomolecule

It is well documented that NUDT5 enzyme inhibition in breast cancer cell lines arrest cancer cells growth, invasiveness and migration. The NUDT5 enzyme enhances breast cancer aggressiveness and act as key regulator of oncogenic pathways. Similarly, the NUDT5 enzyme plays a primer role in ATP-dependent cellular processes and proliferation in breast cancer. Thus, the NUDT5 enzyme plays a profound contribution in promoting breast cancers carcinogenesis and could be an ideal target for anti-cancer drug discovery. In this work, LAS_51382001, LAS_51177972 and LAS_51380924 with binding energy of -12.64 kcal/mol, -11.59 kcal/mol and -10.01 kcal/mol, respectively were filtered as lead molecules. The control molecule binding energy was -10.87 kcal/mol. The system dynamics were found uniform in molecular dynamics simulation studies and observed with no major structural changes. Among the leads, the LAS_51177972 showed the most stable binding energy values. The MM-GBSA binding energy of the compound was -37.07 kcal/mol and MM-PBSA binding energy of -43.56 kcal/mol. Similarly, the compound revealed very stable WaterSwap absolute binding energy values; Bennett's, TI and FEP energy of -36.2 kcal/mol, -36.13 kcal/mol and -36.58 kcal/mol, respectively. Similarly, the leads reported very favorable physicochemical properties, water solubility, pharmacokinetics, druglikeness and medicinal chemistry properties. In a nutshell, the compounds are potent in term of the current computational study however, need to be subjected to experimental studies.Communicated by Ramaswamy H. Sarma.

Exploring the anti-cancer properties of Carissa carandas as a multi-targeted approach against breast cancer

The escalating incidence of breast cancer globally presents a formidable challenge within oncology. Our research pursued an examination of the anti-cancer potential of Carissa carandas, a shrub traditionally used for medicinal purposes and known for its composition of vital nutrients and phytochemicals. We employed a network pharmacology strategy combined with molecular docking and molecular dynamics simulations to elucidate the intricate relationships between the phytochemical constituents of C. carandas, critical breast cancer proteins, and associated signaling pathways. The study highlighted a complex network of protein interactions, identifying AKT1, HIF1A, PTGS2, and GSK3B as key nodes within this network. These proteins are engaged by numerous investigated compounds from C. carandas and are fundamental in modulating crucial signaling pathways such as those involving Estrogen, HIF-1, Prolactin, VEGF, and Th17 cell differentiation-each of which plays a recognized role in breast cancer progression, affecting tumor growth, proliferation, and metastatic potential. Our analysis suggests that the phytochemicals in C. carandas may exert anti-cancer activity by synergistically modulating these pathways, highlighting the benefit of multi-targeted therapeutic approaches over single-targeted ones. In summary, through the application of advanced network pharmacology, molecular docking, MD simulations, and MM/PBSA analysis, our study offers a detailed exploration of the potential mechanisms by which C. carandas may exert anti-cancer effects. This sets a foundation for further in-depth experimental and clinical trials to validate these mechanisms and support the advancement of novel plant-derived therapeutic options towards breast cancer, with the possibility of significantly advancing the therapeutic options for this prevalent disease.

Drug repurposing to tackle parainfluenza 3 based on multi-similarities and network proximity analysis

Given that there is currently no clinically approved drug or vaccine for parainfluenza 3 (PIV3), we applied a drug repurposing method based on disease similarity and chemical similarity to screen 2,585 clinically approved chemical drugs using PIV3 potential drugs BCX-2798 and zanamivir as our controls. Twelve candidate drugs were obtained after being screened with good disease similarity and chemical similarity (S > 0.50, T > 0.56). When docking them with the PIV3 target protein, hemagglutinin-neuraminidase (HN), only oseltamivir was docked with a better score than BCX-2798, which indicates that oseltamivir has an inhibitory effect on PIV3. After the distance (Z d c ) between the drug target of 14 drugs and the PIV3 disease target was measured by the network proximity method based on the PIV3 disease module, it was found that theZ d c values of amikacin, oseltamivir, ribavirin, and streptomycin were less than those of the control. Thus, oseltamivir is the best potential drug because it met all the above screening requirements. Additionally, to explore whether oseltamivir binds to HN stably, molecular dynamics simulation of the binding of oseltamivir to HN was carried out, and the results showed that the RMSD value of the complex tended to be stable within 100 ns, and the binding free energy of the complex was low (-10.60 kcal/mol). It was proved that oseltamivir screened by our drug repurposing method had the potential feasibility of treating PIV3.

Chemoinformatics and machine learning techniques to identify novel inhibitors of the lemur tyrosine kinase-3 receptor involved in breast cancer

Breast cancer is still the largest cause of cancer death in women, and around 70% of primary breast cancer patients are estrogen receptor (ER)-positive, which is the most frequent kind of breast cancer. The lemur tyrosine kinase-3 (LMTK3) receptor has been linked to estrogen responsiveness in breast cancer. However, the function of LMTK3 in reaction to cytotoxic chemotherapy has yet to be studied. Breast cancer therapy research remains tricky due to a paucity of structural investigations on LMTK3. We performed structural investigations on LMTK3 using molecular docking and molecular dynamics (MD) simulations of the LMTK3 receptor in complex with the top three inhibitor molecules along with a control inhibitor. Analysis revealed the top three compounds show the best binding affinities during docking simulations. Interactive analysis of hydrogen bonds inferred hotspot residues Tyr163, Asn138, Asp133, Tyr56, Glu52, Ser132, Asp313, and Asp151. Some other residues in the 5-Å region determined strong alkyl bonds and conventional hydrogen bond linkages. Furthermore, protein dynamics analysis revealed significant modifications among the top complexes and the control system. There was a transition from a loop to a-helix conformation in the protein-top1 complex, and in contrast, in complexes top2 and top3, the formation of a stabilizing sheet in the C chain was observed, which limited significant mobility and increased complex stability. Significant structural alterations were observed in the protein-top complexes, including a shorter helix region and the creation of some loop regions in comparison to the control system. Interestingly, binding free energies, including MMGB/PBSA WaterSwap analysis estimation, reveals that the top1 complex system was more stable than other systems, especially in comparison to the control inhibitor complex system. These results suggest a the plausible mode of action for the novel inhibitors. Therefore, the current investigation contributes to understanding the mechanism of action, serving as a basis for future experimental studies.

Network pharmacology and molecular dynamic simulation integrated strategy for the screening of active components and mechanisms of phytochemicals from Datura innoxia on Alzheimer and cognitive decline

Alzheimer's disease (AD) ranks as the most prevalent neurodegenerative disorder with dementia and it accounts for more than 70% of all cases. Despite extensive reporting on the experimental investigation of Datura innoxia (DI) and its phytochemical components in the treatment of AD, the urgent need for elucidation of the principle of multi-mechanism and multi-level treatment of AD remains. In this research, molecular docking and network pharmacology were used to evaluate active compounds and molecular targets of DI for the treatment of AD. The phytochemical compounds of DI were obtained from the Indian Medicinal Plants, Phytochemistry, and Therapeutics (IMPPAT) as well as the Traditional Chinese Medicine System Pharmacology (TCMSP) databases. The screening includes the 28 most abundant components of DI and the Swiss Target Prediction database was used to predict targets of these compounds. The GeneCards database was used to collect AD-related genes. Both DI and AD targets were imported into a Venn diagram, and the 28 overlapped genes were identified as potential DI anti-AD targets. The results showed that Dinoxin B, Meteloidine, Scopoline, and Tropic acid had no effect on AD-related genes. Furthermore, the GO enrichment analysis indicates that DI influences molecular functions and biological processes such as learning or memory and modulation of chemical synaptic transmission as well as the membrane raft and membrane microdomain. The KEGG pathway analysis revealed that the key pathways implicated in DI's anti-AD actions include serotonergic synapse, IL-17 signaling pathway, and AGE-RAGE signaling pathway in diabetic complications. Based on the STRING and Cytoscape network-analysis platforms, the top ten anti-AD core targets include APP, CASP3, IL6, BACE1, IL1B, ACE, PSEN1, GAPDH, GSK3B and ACHE. The molecular docking and molecular dynamic simulation of the top two molecules against the top three target proteins confirmed the strong binding affinity and stability at the docked site. Overall, our findings pave the path for further research into the development and optimization of potential anti-AD agents from DI.

Exploring therapeutic targets and drug candidates for obesity: a combined network pharmacology, bioinformatics approach

The remarkably high prevalence of obesity in Saudi Arabia reflects a global epidemic demanding urgent attention due to its associated health risks. The integration of traditional medicine, a vital cultural aspect, involves the use of medicinal plants to address various diseases, including obesity. This research merges network pharmacology (NP) and bioinformatics to innovate obesity treatment by identifying effective phytochemicals from native plants in the Taif valley. Focusing on six indigenous plants-Senna alexandrina, Capsicum annuum, Zingiber officinale, Curcuma longa, Trigonella foenum-graecum, and Foeniculum vulgare-we conducted preliminary screenings for potential bioactive compounds. We systematically compiled compound data from public databases and reviewed literature, revealing active compounds like apigenin, kaempferol, moupinamide, cyclocurcumin, chrysoeriol, isorhamnetin, rheinanthrone, cyclocurcumin, and riboflavin.Constructing a compound-target genes-obesity network unveiled their significant impact on metabolic regulation and fat accumulation, interacting notably with key proteins AKT1 and PTGS2. Molecular docking and 100 ns Molecular Dynamic (MD) simulations demonstrated robust binding affinity and stability at the docking site. Employing adipocytes as a cellular model, we gauged their viability and response to obesity-related stressors post-treatment with these native plant compounds.In conclusion, Saudi Arabia's indigenous plants hold promise as natural solutions for obesity treatment. This research opens new avenues in the battle against this pervasive health crisis by incorporating the potential of native botanicals.Communicated by Ramaswamy H. Sarma.

Exploring the antibacterial, antidiabetic, and anticancer potential of Mentha arvensis extract through in-silico and in-vitro analysis

BACKGROUND

Mentha arvensis has been utilized in diverse traditional medicines as an antidiabetic, anticarcinogenic, antiallergic, antifungal, and antibacterial agent. In this work, we have explored the phytochemical analyses and pharmacological potential of Mentha arvensis using both in silico and in vitro approaches for drug discovery.

METHODS

To determine the extract with the highest potential for powerful bioactivity, ethanol was used as the solvent. The phytochemical components of the extracts were quantified using liquid chromatography-mass spectrometry analysis. The potential bioactivities of extracts and lead phytocompounds, including their antibacterial, cytotoxic, and anti-diabetic effects, were evaluated.

RESULTS

The compounds oleanolic acid, rosmarinic acid, luteolin, isoorientin, and ursolic acid have been identified through liquid chromatography mass spectrometry analysis. Based on antimicrobial research, it has been found that the Mentha arvensis extract shows potential activity against K. pneumoniae which was 13.39 ± 0.16. Mentha arvensis has demonstrated a greater degree of efficacy in inhibiting α-glucosidase, with an inhibition rate of 58.36 ± 0.12, and in inhibiting α-amylase, with an inhibition rate of 42.18 ± 0.83. The growth of HepG2 cells was observed to be significantly suppressed upon treatment with extracts obtained from Mentha arvensis. Finally, In-silico methods demonstrated that the Luteolin and Rosmarinic acid exhibit acceptable drug-like characteristics. Furthermore, Molecular docking studies further demonstrated that both compounds have strong potential to inhibit the active sites of therapeutically relevant enzymes involved in Diabetes, Bacterial infections, and Cancer.

CONCLUSIONS

The results of this study suggest that the Mentha arvensis extract possesses potent pharmacological potentials, particularly in terms of antibacterial, anti-diabetic, and cytotoxic effects. Particularly, Luteolin and Rosmarinic acid were identified as the top contenders for potential bioactivity with acceptable drug-like properties.