DLC1 as Druggable Target for Specific Subsets of Gastric Cancer: An RNA-seq-Based Study.

Background: Gastric cancer has been ranked the third leading cause of cancer death worldwide. Its detection at the early stage is difficult because patients mostly experience vague and non-specific symptoms in the early stages. Methods: The RNA-seq datasets of both gastric cancer and normal samples were considered and processed. The obtained differentially expressed genes were then subjected to functional enrichment analysis and pathway analysis. An implicit atomistic molecular dynamics simulation was executed on the selected protein receptor for 50 ns. The electrostatics, surface potential, radius of gyration, and macromolecular energy frustration landscape were computed. Results: We obtained a large number of DEGs; most of them were down-regulated, while few were up-regulated. A DAVID analysis showed that most of the genes were prominent in the KEGG and Reactome pathways. The most prominent GAD disease classes were cancer, metabolic, chemdependency, and infection. After an implicit atomistic molecular dynamics simulation, we observed that DLC1 is electrostatically optimized, stable, and has a reliable energy frustration landscape, with only a few maximum energy frustrations in the loop regions. It has a good functional and binding affinity mechanism. Conclusions: Our study revealed that DLC1 could be used as a potential druggable target for specific subsets of gastric cancer.

In Silico Analysis and Comparative Molecular Docking Study of FDA Approved Drugs with Transforming Growth Factor Beta Receptors in Oral Submucous Fibrosis.

Abstract: Oral sub-mucous fibrosis (OSMF) is a severe crippling malignant disorder which affects the oral mucosa. The transforming growth factor beta (TGF-ß) is one of the cytokines involved with the cell proliferation, cell growth and apoptosis. Several traditional and synthetic medications have been tried in OSMF. This study attempts to identify the FDA approved drugs (including both synthetic and herbal medications) with least side effects, highest efficacy and robust dynamic mechanism for the treatment of OSMF. A ligand library comprising of FDA approved drug compounds was prepared using ChEMBL database. Molecular docking was carried out using GOLD suite 5.2.2. The docked complexes which had the highest binding affinities and lowest energy were deployed to a molecular dynamic simulation using MDweb server. Further, SwissADME  was used to study ADME, physicochemistry, drug-likeness, pharmacokinetics and medicinal chemistry friendliness properties. Our docking results suggest that ligands-Curcumin, Curcumin Pyrazole and Demethoxycurcumin, which are all herbal in nature, have a better binding affinity and the best docking scores for both TGF-ß type I and TGF-ß type II receptors. The molecular dynamics study discerns that the structures have become more stable with less energy. The pharmacokinetics and pharmacodynamics analysis, physicochemical properties and toxicity prediction suggest that Curcumin is the optimal lead compound and holds the potential to be used as an effective drug for the treatment of OSMF. Curcumin, a FDA approved herbal compound, can be used as an effective drug for the treatment of OSMF.

Macrophage-Targeted Punicalagin Nanoengineering to Alleviate Methotrexate-Induced Neutropenia: A Molecular Docking, DFT, and MD Simulation Analysis.

Punicalagin is the most bioactive pomegranate polyphenol with high antioxidant and free-radical scavenging activity and can potentially cure different ailments related to the cardiovascular system. The current research work was envisioned to predict the targeting efficiency of punicalagin (PG) nanoparticles to the macrophages, more specifically to bone marrow macrophages. For this, we selected mannose-decorated PLGA-punicalagin nanoparticles (Mn-PLGA-PG), and before formulating this nanocarrier in laboratory settings, we predicted the targeting efficiency of this nanocarrier by in silico analysis. The analysis proceeded with macrophage mannose receptors to be acquainted with the binding affinity and punicalagin-based nanocarrier interactions with this receptor. In silico docking studies of macrophage mannose receptors and punicalagin showed binding interactions on its surface. PG interacted with hydrogen bonds to the charged residue ASP668 and GLY666 and polar residue GLN760 of the Mn receptor. Mannose with a docking score of -5.811 Kcal/mol interacted with four hydrogen bonds and the mannose receptor of macrophage, and in PLGA, it showed a -4.334 Kcal/mol docking score. Further, the analysis proceeded with density functional theory analysis (DFT) and HOMO-LUMO analysis, followed by an extensive 100 ns molecular dynamics simulation to analyse the trajectories showing the slightest deviation and fluctuation. While analysing the ligand and protein interaction, a wonderful interaction was found among the atoms of the ligand and protein residues. This computational study confirms that this nanocarrier could be a promising lead molecule to regulate the incidence of drug-induced neutropenia. Furthermore, experimental validation is required before this can be stated with complete confidence or before human use.

BESFA: bioinformatics based evolutionary, structural & functional analysis of prostrate, Placenta, Ovary, Testis, and Embryo (POTE) paralogs.

The POTE family comprises 14 paralogues and is primarily expressed in Prostrate, Placenta, Ovary, Testis, Embryo (POTE), and cancerous cells. The prospective function of the POTE protein family under physiological conditions is less understood. We systematically analyzed their cellular localization and molecular docking analysis to elucidate POTE proteins' structure, function, and Adaptive Divergence. Our results suggest that group three POTE paralogs (POTEE, POTEF, POTEI, POTEJ, and POTEKP (a pseudogene)) exhibits significant variation among other members could be because of their Adaptive Divergence. Furthermore, our molecular docking studies on POTE protein revealed the highest binding affinity with NCI-approved anticancer compounds. Additionally, POTEE, POTEF, POTEI, and POTEJ were subject to an explicit molecular dynamic simulation for 50ns. MM-GBSA and other essential electrostatics were calculated that showcased that only POTEE and POTEF have absolute binding affinities with minimum energy exploitation. Thus, this study's outcomes are expected to drive cancer research to successful utilization of POTE genes family as a new biomarker, which could pave the way for the discovery of new therapies.

In silico approach to understand epigenetics of POTEE in ovarian cancer.

Ovarian cancer is the third leading cause of cancer-related deaths in India. Epigenetics mechanisms seemingly plays an important role in ovarian cancer. This paper highlights the crucial epigenetic changes that occur in POTEE that get hypomethylated in ovarian cancer. We utilized the POTEE paralog mRNA sequence to identify major motifs and also performed its enrichment analysis. We identified 6 motifs of varying lengths, out of which only three motifs, including CTTCCAGCAGATGTGGATCA, GGAACTGCC, and CGCCACATGCAGGC were most likely to be present in the nucleotide sequence of POTEE. By enrichment and occurrences identification analyses, we rectified the best match motif as CTTCCAGCAGATGT. Since there is no experimentally verified structure of POTEE paralog, thus, we predicted the POTEE structure using an automated workflow for template-based modeling using the power of a deep neural network. Additionally, to validate our predicted model we used AlphaFold predicted POTEE structure and observed that the residual stretch starting from 237-958 had a very high confidence per residue. Furthermore, POTEE predicted model stability was evaluated using replica exchange molecular dynamic simulation for 50 ns. Our network-based epigenetic analysis discerns only 10 highly significant, direct, and physical associators of POTEE. Our finding aims to provide new insights about the POTEE paralog.

Differential expression analysis in ovarian cancer: A functional genomics and systems biology approach.

BACKGROUND: Ovarian cancer is one of the rarest lethal oncologic diseases that have hardly any specific biomarkers. The availability of high-throughput genomic data and advancement in bioinformatics tools allow us to predict gene biomarkers and apply systems biology approaches to get better diagnosis, and prognosis of the disease with a tentative drug that may be repurposed. OBJECTIVE: To perform genome-wide association studies using microarray gene expression of ovarian cancer and identify gene biomarkers, construction and analyze networks, perform survival analysis, and drug interaction studies for better diagnosis, prognosis, and treatment of ovarian cancer. METHOD: The gene expression profiles of both healthy and serous ovarian cancer epithelial samples were considered. We applied a series of bioinformatics methods and tools, including fold-change statistics for differential expression analysis, DisGeNET and NCBI-Gene databases for gene-disease association mapping, DAVID 6.8 for GO enrichment analysis, GeneMANIA for network construction, Cytoscape 3.8 with its plugins for network visualization, analysis, and module detection, the UALCAN for patient survival analysis, and PubChem, DrugBank and DGIdb for gene-drug interaction. RESULTS: We identified 8 seed genes that were subjected for drug-gene interaction studies. Because of over-expression in all the four stages of ovarian cancer, we discern that genes HMGA1 and PSAT1 are potential therapeutic biomarkers for its diagnosis at an early stage (stage I). Our analysis suggests that there are 11 drugs common in the seed genes. However, hypermethylated seed genes HMGA1 and PSAT1 showcased a good interaction affinity with drugs cisplatin, cyclosporin, bisphenol A, progesterone, and sunitinib, and are crucial in the proliferation of ovarian cancer. CONCLUSION: Our study reveals that HMGA1 and PSAT1 can be deployed for initial screening of ovarian cancer and drugs cisplatin, bisphenol A, cyclosporin, progesterone, and sunitinib are effective in curbing the epigenetic alteration.

Phytochemicals from Ayurvedic plants as potential medicaments for ovarian cancer: an in silico analysis.

Ovarian cancer is one of the highly prominent gynecological malignancies after breast cancer. Although myriad literature is available, there is no specific biomarker available for the personalized treatment strategy. The unavailability of effective drug therapy for ovarian cancer calls for an urgent push in its development from the multidisciplinary scientific community. Indian Ayurvedic medicine pharmacology is widely appreciated and accepted for its immense healthcare benefits. Bioinformatics and cheminformatics approaches can be effectively used to screen phytochemicals present in the Indian Ayurvedic plants against ovarian cancer target receptors. Recent studies discern that POTE, a cancer-testis antigen (CTA) family, plays a crucial role in the proliferation and progression of cancers including ovarian cancer. Specifically, POTEE paralog has been observed to be hypermethylated in ovarian cancer. This study undertakes an in silico analysis of Indian Ayurvedic plants for their anticancer efficacy against ovarian cancer proliferation target receptor POTEE. Structures of 100 phytochemicals from 11 Ayurvedic plants were screened with ADME criteria, and qualified phytochemicals were subjected to molecular docking and interaction analysis. Only 6 phytochemicals having a high affinity to the target receptor (POTEE) were then subjected to an all-atom replica exchange molecular dynamics simulation for 50 ns. Binding affinities of 6 phytochemicals cedeodarin, deodarin, hematoxylin, matairesinol, quercetin, and taxifolin with POTEE were -8.1, -7.7, -7.7, -7.9, -8.0, and - 7.7 kcal/mol, respectively, and their RMSD were recorded as zero. This study concludes that phytochemicals present in Indian Ayurvedic plants namely Cedrus deodara and Asparagus racemosus possess inhibitory effects against ovarian cancer proliferation receptor POTEE.