Potential inhibitors of VEGFR1, VEGFR2, and VEGFR3 developed through Deep Learning for the treatment of Cervical Cancer.

Cervical cancer stands as a prevalent gynaecologic malignancy affecting women globally, often linked to persistent human papillomavirus infection. Biomarkers associated with cervical cancer, including VEGF-A, VEGF-B, VEGF-C, VEGF-D, and VEGF-E, show upregulation and are linked to angiogenesis and lymphangiogenesis. This research aims to employ in-silico methods to target tyrosine kinase receptor proteins-VEGFR-1, VEGFR-2, and VEGFR-3, and identify novel inhibitors for Vascular Endothelial Growth Factors receptors (VEGFRs). A comprehensive literary study was conducted which identified 26 established inhibitors for VEGFR-1, VEGFR-2, and VEGFR-3 receptor proteins. Compounds with high-affinity scores, including PubChem ID-25102847, 369976, and 208908 were chosen from pre-existing compounds for creating Deep Learning-based models. RD-Kit, a Deep learning algorithm, was used to generate 43 million compounds for VEGFR-1, VEGFR-2, and VEGFR-3 targets. Molecular docking studies were conducted on the top 10 molecules for each target to validate the receptor-ligand binding affinity. The results of Molecular Docking indicated that PubChem IDs-71465,645 and 11152946 exhibited strong affinity, designating them as the most efficient molecules. To further investigate their potential, a Molecular Dynamics Simulation was performed to assess conformational stability, and a pharmacophore analysis was also conducted for indoctrinating interactions.

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.

Exploring the macromolecules for secretory pathway in cancer disease.

Secretory proteins play an important role in the tumor microenvironment and are widely distributed throughout tumor tissues. Tumor cells secrete a protein that mediates communication between tumor cells and stromal cells, thereby controlling tumor growth and affecting the success of cancer treatments in the clinic. The cancer secretome is produced by various secretory pathways and has a wide range of applications in oncoproteomics. Secretory proteins are involved in cancer development and tumor cell migration, and thus serve as biomarkers or effective therapeutic targets for a variety of cancers. Several proteomic strategies have recently been used for the analysis of cancer secretomes in order to gain a better understanding and elaborate interpretation. For instance, the development of exosome proteomics, degradomics, and tumor-host cell interaction provide clear information regarding the mechanism of cancer pathobiology. In this chapter, we emphasize the recent advances in secretory protein and the challenges in the field of secretome analysis and their clinical applications.

High-Throughput Screening and Quantum Mechanics for Identifying Potent Inhibitors Against Mac1 Domain of SARS-CoV-2 Nsp3.

SARS-CoV-2 encodes the Mac1 domain within the large nonstructural protein 3 (Nsp3), which has an ADP-ribosylhydrolase activity conserved in other coronaviruses. The enzymatic activity of Mac1 makes it an essential virulence factor for the pathogenicity of coronavirus (CoV). They have a regulatory role in counteracting host-mediated antiviral ADP-ribosylation, which is unique part of host response towards viral infections. Mac1 shows highly conserved residues in the binding pocket for the mono and poly ADP-ribose. Therefore, SARS-CoV-2 Mac1 enzyme is considered as an ideal drug target and inhibitors developed against them can possess a broad antiviral activity against CoV. ADP-ribose-1 phosphate bound closed form of Mac1 domain is considered for screening with large database of ZINC. XP docking and QPLD provides strong potential lead compounds, that perfectly fits inside the binding pocket. Quantum mechanical studies expose that, substrate and leads have similar electron donor ability in the head regions, that allocates tight binding inside the substrate-binding pocket. Molecular dynamics study confirms the substrate and new lead molecules presence of electron donor and acceptor makes the interactions tight inside the binding pocket. Overall binding phenomenon shows both substrate and lead molecules are well-adopt to bind with similar binding mode inside the closed form of Mac1.

Structure-based virtual screening and molecular dynamics simulation of SARS-CoV-2 Guanine-N7 methyltransferase (nsp14) for identifying antiviral inhibitors against COVID-19.

The recent pandemic caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) calls the whole world into a medical emergency. For tackling Coronavirus Disease 2019 (COVID-19), researchers from around the world are swiftly working on designing and identifying inhibitors against all possible viral key protein targets. One of the attractive drug targets is guanine-N7 methyltransferase which plays the main role in capping the 5'-ends of viral genomic RNA and sub genomic RNAs, to escape the host's innate immunity. We performed homology modeling and molecular dynamic (MD) simulation, in order to understand the molecular architecture of Guanosine-P3-Adenosine-5',5'-Triphosphate (G3A) binding with C-terminal N7-MTase domain of nsp14 from SARS-CoV-2. The residue Asn388 is highly conserved in present both in N7-MTase from SARS-CoV and SARS-CoV-2 and displays a unique function in G3A binding. For an in-depth understanding of these substrate specificities, we tried to screen and identify inhibitors from the Traditional Chinese Medicine (TCM) database. The combination of several computational approaches, including screening, MM/GBSA, MD simulations, and PCA calculations, provides the screened compounds that readily interact with the G3A binding site of homology modeled N7-MTase domain. Compounds from this screening will have strong potency towards inhibiting the substrate-binding and efficiently hinder the viral 5'-end RNA capping mechanism. We strongly believe the final compounds can become COVID-19 therapeutics, with huge international support.[Formula: see text]The focus of this study is to screen for antiviral inhibitors blocking guanine-N7 methyltransferase (N7-MTase), one of the key drug targets involved in the first methylation step of the SARS-CoV-2 RNA capping mechanism. Compounds binding the substrate-binding site can interfere with enzyme catalysis and impede 5'-end cap formation, which is crucial to mimic host RNA and evade host cellular immune responses. Therefore, our study proposes the top hit compounds from the Traditional Chinese Medicine (TCM) database using a combination of several computational approaches.Communicated by Ramaswamy H. Sarma.

Structural dynamic studies on identification of EGCG analogues for the inhibition of Human Papillomavirus E7.

High risk human papillomaviruses are highly associated with the cervical carcinoma and the other genital tumors. Development of cervical cancer passes through the multistep process initiated from benign cyst to increasingly severe premalignant dysplastic lesions in an epithelium. Replication of this virus occurs in the fatal differentiating epithelium and involves in the activation of cellular DNA replication proteins. The oncoprotein E7 of human papillomavirus expressed in the lower epithelial layers constrains the cells into S-phase constructing an environment favorable for genome replication and cell proliferation. To date, no suitable drug molecules exist to treat HPV infection whereas anticipation of novel anti-HPV chemotherapies with distinctive mode of actions and identification of potential drugs are crucial to a greater extent. Hence, our present study focused on identification of compounds analogue to EGCG, a green tea molecule which is considered to be safe to use for mammalian systems towards treatment of cancer. A three dimensional similarity search on the small molecule library from natural product database using EGCG identified 11 potential small molecules based on their structural similarity. The docking strategies were implemented with acquired small molecules and identification of the key interactions between protein and compounds were carried out through binding free energy calculations. The conformational changes between the apoprotein and complexes were analyzed through simulation performed thrice demonstrating the dynamical and structural effects of the protein induced by the compounds signifying the domination. The analysis of the conformational stability provoked us to describe the features of the best identified small molecules through electronic structure calculations. Overall, our study provides the basis for structural insights of the identified potential identified small molecules and EGCG. Hence, the identified analogue of EGCG can be potent inhibitors against the HPV 16 E7 oncoprotein.

An In silico Approach to Identify High Affinity Small Molecule Targeting m-TOR Inhibitors for the Clinical Treatment of Breast Cancer

Breast cancer is the most frequent malignancy among women. It is a heterogeneous disease with different subtypes defined by its hormone receptor. A hormone receptor is mainly concerned with the progression of the PI3K/AKT/ mTOR pathway which is often dysregulated in breast cancer. This is a major signaling pathway that controls the activities such as cell growth, cell division, and cell proliferation. The present study aims to suppress mTOR protein by its various inhibitors and to select one with the highest binding affinity to the receptor protein. Out of 40 inhibitors of mTOR against breast cancer, SF1126 was identified to have the best docking score of -8.705, using Schrodinger Suite which was further subjected for high throughput screening to obtain best similar compound using Lipinski's filters. The compound obtained after virtual screening, ID: ZINC85569445 is seen to have the highest affinity with the target protein mTOR. The same result based on the binding free energy analysis using MM-GBSA showed that the compound ZINC85569445 to have the the highest binding free energy. The next study of interaction between the ligand and receptor protein with the pharmacophore mapping showed the best conjugates, and the ZINC85569445 can be further studied for future benefits of treatment of breast cancer.