Several recent studies have pointed out that arc GTPase activating protein 1 (RACGAP1) is a putative oncogene in many human tumors. However, to date, no pan-cancer analysis has been performed to study the different aspects of this gene expression and behavior in tumor tissues. Here, we applied several bioinformatics tools to perform a comprehensive analysis for RACGAP1. First, we assessed the expression of RACGAP1 in several types of human tumors and tried to correlate that with the stage of the tumors analyzed. We then performed a survival analysis to study the correlation between RACGAP1 upregulation in tumors and the clinical outcome. Additionally, we investigated the mutation forms, the correlation with several immune cell infiltration, the phosphorylation status of the interested protein in normal and tumor tissues, and the potential molecular mechanisms of RACGAP1 in cancerous tissue. The results demonstrated that RACGAP1, a highly expressed gene across several types of tumors, correlated with a poor prognosis in several types of human cancers. Moreover, it was found that RACGAP1 affects the tumor immune microenvironment by influencing the infiltration level of several immune cells. Collectively, the current study provides a comprehensive overview of the oncogenic roles of RACGAP1, where our results nominate it as a potential prognostic biomarker and a target for antitumor therapy development.

Cancer is a major public health concern worldwide in terms of mortality. The exact reason behind the development of cancer is not understood clearly, but it is evidenced that alcohol consumption, radiation, and exposure to chemicals are main players in this pathogenesis. The current mode of treatments such as surgery, chemotherapy, and radiotherapy are effective, but, still, cancer is a major problem leading to death and other side effects. However, safer and effective treatment modules are needed to overcome the adverse effects of current treatment modules. In this regard, natural compounds have been recognized to ameliorate diseases by exerting anti-inflammatory, anti-oxidative, and anti-tumor potential through several mechanisms. Mangiferin, a xanthone C-glucoside, is found in several plant species including Mangifera indica (mango), and its role in disease prevention has been confirmed through its antioxidant and anti-inflammatory properties. Furthermore, its anti-cancer-potential mechanism has been designated through modulation of cell signaling pathways such as inflammation, angiogenesis, PI3K/AKT, apoptosis, and cell cycle. This article extensively reviews the anticancer potential of mangiferin in different cancers through the modulation of cell signaling pathways. Moreover, the synergistic effects of this compound with some commonly used anti-cancer drugs against different cancer cells are discussed. More clinical trials should be performed to reconnoiter the anti-cancer potential of this compound in human cancer treatment. Further, understanding of mechanisms of action and the safety level of this compound can help to manage diseases, including cancer.

Leveraging machine learning has been shown to improve the accuracy of structure-based virtual screening. Furthermore, a tremendous amount of empirical data is publicly available, which further enhances the performance of the machine learning approach. In this proof-of-concept study, the 3CL^pro enzyme of SARS-CoV-2 was used. Structure-based virtual screening relies heavily on scoring functions. It is widely accepted that target-specific scoring functions may perform more effectively than universal scoring functions in real-world drug research and development processes. It would be beneficial to drug discovery to develop a method that can effectively build target-specific scoring functions. In the current study, the bindingDB database was used to retrieve experimental data. Smina was utilized to generate protein-ligand complexes for the extraction of InteractionFingerPrint (IFP) and SimpleInteractionFingerPrint SIFP fingerprints via the open drug discovery tool (oddt). The present study found that randomforestClassifier and randomforestRegressor performed well when used with the above fingerprints along the Molecular ACCess System (MACCS), Extended Connectivity Fingerprint (ECFP4), and ECFP6. It was found that the area under the precision-recall curve was 0.80, which is considered a satisfactory level of accuracy. In addition, our enrichment factor analysis indicated that our trained scoring function ranked molecules correctly compared to smina’s generic scoring function. Further molecular dynamics simulations indicated that the top-ranked molecules identified by our developed scoring function were highly stable in the active site, supporting the validity of our developed process. This research may provide a template for developing target-specific scoring functions against specific enzyme targets.

Background: Alzheimer’s disease (AD) is a form of dementia that strikes elderly people more frequently than it does younger people. The cognitive skills and memory of Alzheimer’s sufferers continue to deteriorate over time. Recent studies have shown that patients with AD have greater amounts of inflammatory markers in their bodies, which suggests that inflammation occurs early on in the progression of the disease. There is a possibility that Aß oligomers and fibrils can be recognised by TLRs, in addition to the microglial receptors CD14, CD36, and CD47. When Aß binds to either CD36 or TLR4, it sets off a chain reaction of inflammatory chemokines and cytokines that ultimately results in neurodegeneration. Diabetes and Alzheimer’s disease have both been recently related to TLR4. The activation of TLR4 has been connected to a variety of clinical difficulties that are associated with diabetes, in addition to the internal environment of the body and the microenvironment of the brain. TLR4 inhibitors have been shown in clinical investigations to not only lessen the likelihood of getting sick but also to increase the average longevity. Result: In this work we used molecular docking and molecular dynamics modelling to investigate the effectiveness of FDA-approved antidiabetic plant derived drugs in combating the TLR4 receptor. Molecular docking experiments were used to make a prediction regarding the most important interactions involving 2-Bromoergocryptine Mesylate. With a binding affinity of -8.26 kcal/mol, it stood out from the other candidates as the one with the greatest potential. To verify the interaction pattern that takes place between 2-Bromoergocryptine Mesylate and the TLR4 receptor, a molecular dynamic simulation was run at a time scale of 150 nanoseconds. Because of this, 2-Bromoergocryptine Mesylate was able to make substantial contact with the active site, which led to increased structural stability during the process of the complex’s dynamic development. Conclusion: As a result of this, the results of our research may be relevant for future research into the efficacy of 2-bromoergocryptine mesylate as a potential lead treatment for TLR4 receptors in intracranial aneurysm rupture in AD.

Drug repurposing is the finding new activity of the existing drug. Recently, Albendazole’s well-known antihelmintic has got the attention of an anticancer drug. Plausible evidence of the interaction of Albendazole with one of the types of tyrosine kinase protein receptor, vascular endothelial growth factor receptor-2 (VEGFR-2) is still not well understood. Inhibition of the VEGFR-2 receptor can prevent tumor growth. The current study investigated the interaction of Albendazole with VEGFR-2.It was found that the said interaction exhibited potent binding energy ΔG = -7.12 kcal/mol, inhibitory concentration (Ki) = 6.04 μM, and as positive control comparison with standard drug (42Q1170A) showed ΔG = -12.35 kcal/mol and Ki = 881 μM. The key residue Asp1046 was formed involved hydrogen bonding with Albendazole. The molecular dynamics simulation study revealed the stable trajectory of the VEGFR-2 receptor with Albendazole bound complex having significant high free energy of binding as calculated from Molecular Mechanics Generalized Born and Surface Area study ΔG = -42.07±2.4 kcal/mol. The binding energy is significantly high for greater stability of the complex. Principal component analysis of molecular docking trajectories exhibited ordered motion at higher modes, implying a high degree of VEGFR-2 and Albendazole complex stability as seen with the standard drug 42Q. Therefore, the current work suggests the role of Albendazole as a potent angiogenesis inhibitor as ascertained by its potential interaction with VEGFR-2. The findings of research will aid in the future development of Albendazole in anticancer therapy.