Unveiling the ESR1 Conformational Stability and Screening Potent Inhibitors for Breast Cancer Treatment

BACKGROUND: The current study recognizes the significance of estrogen receptor alpha (ERα) as a member of the nuclear receptor protein family, which holds a central role in the pathophysiology of breast cancer. ERα serves as a valuable prognostic marker, with its established relevance in predicting disease outcomes and treatment responses. METHOD: In this study, computational methods are utilized to search for suitable drug-like compounds that demonstrate analogous ligand binding kinetics to ERα. RESULTS: Docking-based simulation screened out the top 5 compounds - ZINC13377936, NCI35753, ZINC35465238, ZINC14726791, and NCI663569 against the targeted protein. Further, their dynamics studies reveal that the compounds ZINC13377936 and NCI35753 exhibit the highest binding stability and affinity. CONCLUSION: Anticipating the competitive inhibition of ERα protein expression in breast cancer, we envision that both ZINC13377936 and NCI35753 compounds hold substantial promise as potential therapeutic agents. These candidates warrant thorough consideration for rigorous In vitro and In vivo evaluations within the context of clinical trials. The findings from this current investigation carry significant implications for the advancement of future diagnostic and therapeutic approaches for breast cancer.

Structure-Based Virtual Screening, Molecular Docking, Molecular Dynamics Simulation of EGFR for the Clinical Treatment of Glioblastoma.

Glioblastoma (GBM) is a WHO Grade IV tumor with poor visibility, a high risk of comorbidity, and exhibit limited treatment options. Resurfacing from second-rate glioma was originally classified as either mandatory or optional. Recent interest in personalized medicine has motivated research toward biomarker stratification-based individualized illness therapy. GBM biomarkers have been investigated for their potential utility in prognostic stratification, driving the development of targeted therapy and customizing therapeutic treatment. Due to the availability of a specific EGFRvIII mutational variation with a clear function in glioma-genesis, recent research suggests that EGFR has the potential to be a prognostic factor in GBM, while others have shown no clinical link between EGFR and survival. The pre-existing pharmaceutical lapatinib (PubChem ID: 208,908) with a higher affinity score is used for virtual screening. As a result, the current study revealed a newly screened chemical (PubChem CID: 59,671,768) with a higher affinity than the previously known molecule. When the two compounds are compared, the former has the lowest re-rank score. The time-resolved features of a virtually screened chemical and an established compound were investigated using molecular dynamics simulation. Both compounds are equivalent, according to the ADMET study. This report implies that the virtual screened chemical could be a promising Glioblastoma therapy.

Structure-Based Virtual Screening, Molecular Docking, and Molecular Dynamics Simulation of VEGF inhibitors for the clinical treatment of Ovarian Cancer.

Vascular endothelial growth factor (VEGF) and its receptor play an important role both in physiologic and pathologic angiogenesis, which is identified in ovarian cancer progression and metastasis development. The aim of the present investigation is to identify a potential vascular endothelial growth factor inhibitor which is playing a crucial role in stimulating the immunosuppressive microenvironment in tumor cells of the ovary and to examine the effectiveness of the identified inhibitor for the treatment of ovarian cancer using various in silico approaches. Twelve established VEGF inhibitors were collected from various literatures. The compound AEE788 displays great affinity towards the target protein as a result of docking study. AEE788 was further used for structure-based virtual screening in order to obtain a more structurally similar compound with high affinity. Among the 80 virtual screened compounds, CID 88265020 explicates much better affinity than the established compound AEE788. Based on molecular dynamics simulation, pharmacophore and comparative toxicity analysis of both the best established compound and the best virtual screened compound displayed a trivial variation in associated properties. The virtual screened compound CID 88265020 has a high affinity with the lowest re-rank score and holds a huge potential to inhibit the VGFR and can be implemented for prospective future investigations in ovarian cancer.

Computer-Aided Structure Prediction of Bluetongue Virus Coat Protein VP2 Assisted by Optimized Potential for Liquid Simulations (OPLS).

BACKGROUND: The capsid coated protein of Bluetongue virus (BTV) VP2 is responsible for BTV transmission by the Culicoides vector to vertebrate hosts. Besides, VP2 is responsible for BTV entry into permissive cells and hence plays a major role in disease progression. However, its mechanism of action is still unknown. OBJECTIVE: The present investigation aimed to predict the 3D structure of Viral Protein 2 of the bluetongue virus assisted by Optimized Potential for Liquid Simulations (OPLS), structure validation, and an active site prediction. METHODS: The 3D structure of the VP2 protein was built using a Python-based Computational algorithm. The templates were identified using Smith waterman's Local alignment. The VP2 protein structure validated using PROCHECK. Molecular Dynamics Simulation (MDS) studies were performed using an academic software Desmond, Schrodinger dynamics, for determining the stability of a model protein. The Ligand-Binding site was predicted by structure comparison using homology search and proteinprotein network analysis to reveal their stability and inhibition mechanism, followed by the active site identification. RESULTS: The secondary structure of the VP2 reveals that the protein contains 220 alpha helix atoms, 40 310 helix, 151 beta sheets, 134 coils and 424 turns, whereas the 3D structure of Viral Protein 2 of BTV has been found to have 15774 total atoms in the structure. However, 961 amino acids were found in the final model. The dynamical cross-correlation matrix (DCCM) analysis tool identifies putative protein domains and also confirms the stability of the predicted model and their dynamical behavior difference with the correlative fluctuations in motion. CONCLUSION: The biological interpretation of the Viral Protein 2 was carried out. DCCM maps were calculated, using a different coordinate reference frame, through which, protein domain boundaries and protein domain residue constituents were identified. The obtained model shows good reliability. Moreover, we anticipated that this research should play a promising role in the identification of novel candidates with the target protein to inhibit their functional significance.