A Review on Picrosides Targeting NFκB and its Proteins for Treatment of Breast Cancer.

Breast cancer is the most frequently diagnosed disease causing most deaths in women worldwide. Chemotherapy and neo-adjuvant therapy are the standard method of treatment in early stages of breast cancer. However drug resistance in breast cancer limit the use of these methods for treatment. Research focus is now shifted towards identifying natural phytochemicals with lower toxicity. This review illustrates the NF κB interaction with different signaling pathways in normal condition, breast cancer and other cancer and thus represent a potential target for treatment. No reports are available on the action of picrosides on NFκB and its associated proteins for anticancer activity. In the present review, potential interaction of picrosides with NF-κB and its associated proteins is reviewed for anticancer action. Further, an important facet of this review entails the ADMET analysis of Picroside, elucidating key ADMET properties which serves to underscore the crucial characteristics of Picroside as a potential drug for treating breast cancer. Furthermore, in silico analysis of Picrosides was executed in order to get potential binding modes between ligand (Picrosides II) and NFκB.

Integrative analysis discovers Imidurea as dual multitargeted inhibitor of CD69, CD40, SHP2, lysozyme, GATA3, cCBL, and S-cysteinase from SARS-CoV-2 and M. tuberculosis.

Two of the deadliest infectious diseases, COVID-19 and tuberculosis (TB), have combined to establish a worldwide pandemic, wreaking havoc on economies and claiming countless lives. The optimised, multitargeted medications may diminish resistance and counter them together. Based on computational expression studies, 183 genes were co-expressed in COVID-19 and TB blood samples. We used the multisampling screening algorithms on the top ten co-expressed genes (CD40, SHP2, Lysozyme, GATA3, cCBL, SIVmac239 Nef, CD69, S-adenosylhomocysteinase, Chemokine Receptor-7, and Membrane Protein). Imidurea is a multitargeted inhibitor for COVID-19 and TB, as confirmed by extensive screening and post-filtering utilising MM\GBSA algorithms. Imidurea has shown docking and MM\GBSA scores of -8.21 to -4.75 Kcal/mol and -64.16 to -29.38 Kcal/mol, respectively. The DFT, pharmacokinetics, and interaction patterns suggest that Imidurea may be a drug candidate, and all ten complexes were tested for stability and bond strength using 100 ns for all MD atoms. The modelling findings showed the complex's repurposing potential, with a cumulative deviation and fluctuation of <2 Å and significant intermolecular interaction, which validated the possibilities. Finally, an inhibition test was performed to confirm our in-silico findings on SARS-CoV-2 Delta variant infection, which was suppressed by adding imidurea to Vero E6 cells after infection.

Microbial Biofilm Inhibition Using Magnetic Cross-Linked Polyphenol Oxidase Aggregates.

Microbial biofilm accumulation poses a serious threat to the environment, presents significant challenges to different industries, and exhibits a large impact on public health. Since there has not been a conclusive answer found despite various efforts, the potential green and economical methods are being focused on, particularly the innovative approaches that employ biochemical agents. In the present study, we propose a bio-nanotechnological method using magnetic cross-linked polyphenol oxidase aggregates (PPO m-CLEA) for inhibition of microbial biofilm including multidrug resistant bacteria. Free PPO solution showed only 55-60% biofilm inhibition, whereas m-CLEA showed 70-75% inhibition, as confirmed through microscopic techniques. The carbohydrate and protein contents in biofilm extracellular polymeric substances (EPSs) were reduced significantly. The m-CLEA demonstrated reusability up to 5 cycles with consistent efficiency in biofilm inhibition. Computational work was also done where molecular docking of PPO with microbial proteins associated with biofilm formation was conducted, resulting in favorable binding scores and inter-residual interactions. Overall, both in vitro and in silico results suggest that PPO interferes with microbial cell attachment and EPS formation, thereby preventing biofilm colonization.

An extensive review on lung cancer therapeutics using machine learning techniques: state-of-the-art and perspectives.

There are over 100 types of human cancer, accounting for millions of deaths every year. Lung cancer alone claims over 1.8 million lives per year and is expected to surpass 3.2 million by 2050, which underscores the urgent need for rapid drug development and repurposing initiatives. The application of AI emerges as a pivotal solution to developing anti-cancer therapeutics. This state-of-the-art review aims to explore the various applications of AI in lung cancer therapeutics. Predictive models can analyse large datasets, including clinical data, genetic information, and treatment outcomes, for novel drug design and to generate personalised treatment recommendations, potentially optimising therapeutic strategies, enhancing treatment efficacy, and minimising adverse effects. A thorough literature review study was conducted based on articles indexed in PubMed and Scopus. We compiled the use of various machine learning approaches, including CNN, RNN, GAN, VAEs, and other AI techniques, enhancing efficiency with accuracy exceeding 95%, which is validated through a computer-aided drug design process. AI can revolutionise lung cancer therapeutics, streamlining processes and saving biological scientists' time and effort-however, further research is needed to overcome challenges and fully unlock AI's potential in Lung Cancer Therapeutics.

Multisampling-based docking reveals Imidazolidinyl urea as a multitargeted inhibitor for lung cancer: an optimisation followed multi-simulation and in-vitro study.

Lung Cancer is one of the deadliest cancers, responsible for more than 1.80 million deaths annually worldwide, and it is on the priority list of WHO. In the current scenario, when cancer cells become resistant to the drug, making it less effective leaves the patient in vulnerable conditions. To overcome this situation, researchers are constantly working on new drugs and medications that can help fight drug resistance and improve patients' outcomes. In this study, we have taken five main proteins of lung cancer, namely RSK4 N-terminal kinase, guanylate kinase, cyclin-dependent kinase 2, kinase CK2 holoenzyme, tumour necrosis factor-alpha and screened the prepared Drug Bank library with 1,55,888 compounds against all using three Glide-based docking algorithms namely HTVS, standard precision and extra precise with a docking score ranging from -5.422 to -8.432 Kcal/mol. The poses were filtered with the MM\GBSA calculations, which helped to identify Imidazolidinyl urea C11H16N8O8 (DB14075) as a multitargeted inhibitor for lung cancer, validated with advanced computations like ADMET, interaction pattern fingerprints, and optimised the compound with Jaguar, producing satisfied relative energy. All five complexes were performed with MD Simulation for 100 ns with NPT ensemble class, producing cumulative deviation and fluctuations < 2 Å and a web of intermolecular interaction, making the complexes stable. Further, the in-vitro analysis for morphological imaging, Annexin V/PI FACS assay, ROS and MMP analysis caspase3//7 activity were performed on the A549 cell line producing promising results and can be an option to treat lung cancer at a significantly cheaper state.Communicated by Ramaswamy H. Sarma.

In-silico and in-vitro study reveals ziprasidone as a potential aromatase inhibitor against breast carcinoma.

Aromatase enzyme plays a fundamental role in the development of estrogen receptors, and due to this functionality, the enzyme has gained significant attention as a therapeutic for reproductive disorders and cancer diseases. The currently employed aromatase inhibitors have severe side effects whereas our novel aromatase inhibitor is more selective and less toxic, therefore has greater potential to be developed as a drug. The research framework of this study is to identify a potent inhibitor for the aromatase target by profiling molecular descriptors of the ligand and to find a functional pocket in the target by docking and MD simulations. For assessing cellular and metabolic activities as indicators of cell viability and cytotoxicity, in-vitro studies were performed by using the colorimetric MTT assay. Aromatase activities were determined by a fluorometric method. Cell morphology was assessed by phase-contrast light microscopy. Flow cytometry and Annexin V-FITC/PI staining assay determined cell cycle distribution and apoptosis. This study reports that CHEMBL708 (Ziprasidone) is the most promising compound that showed excellent aromatase inhibitory activity. By using better drug design methods and experimental studies, our study identified a novel compound that could be effective as a high-potential drug candidate against aromatase enzyme. We conclude that the compound ziprasidone effectively blocks the cell cycle at the G1-S phase and induces cancer cell death. Further, in-vivo studies are vital for developing ziprasidone as an anticancer agent. Lastly, our research outcomes based on the results of the in-silico experiments may pave the way for identifying effective drug candidates for therapeutic use in breast cancer.

Design, synthesis, X-ray crystal structures, anticancer, DNA binding, and molecular modelling studies of pyrazole-pyrazoline hybrid derivatives.

We have designed and synthesized three pyrazole analogs (4, 5a, 5b), pyrazole-based chalcones (6a-6d) and (8a-8h), and N-formyl/acetyl 1,3,5-trisubstituted pyrazoline analogs (7a-7d), (9a-9d). FT-IR, 1H, 13C NMR, and mass spectrometry techniques were used to describe the structures of all the synthesized analogs. The single crystal X-ray method was used to identify the molecular structure of derivatives 4 and 5a. All synthesized analogs were screened by MTT assay on two cancer cell lines, the human lung cancer cell line (A549) and cervical cancer cell line (HeLa). Among all compounds, analog 9d demonstrates significant anticancer activity against HeLa (IC50 = 23.6 µM) and A549 (IC50 = 37.59 µM). The non-interactive interaction of active compound (9d) with Calf thymus DNA (Ct-DNA) has been investigated through various methods, such as UV-vis absorption, emission, cyclic voltammetry and circular dichroism. The DPPH (2,2-diphenyl-1-picrylhydrazyl) free radical has been used to measure the antioxidant capacity of the pyrazoline derivative (9d). The outcomes showed that active analog has significant antioxidant activity. In addition, MD simulation of the EGFR tyrosine kinase protein-ligand complex was performed at a time scale of 100 ns. The MMGBSA data of ligand-protein complex are showed stable interactions up to 100 ns.

In silico screening, synthesis, characterization and biological evaluation of novel anticancer agents as potential COX-2 inhibitors.

BACKGROUND: Cyclooxygenase enzyme is frequently overexpressed in various types of cancer and found to play a crucial role in poor prognosis in cancer patients. In current research, we have reported the new COX-2 inhibitors for cancer treatment using computer-aided drug design and experimental validation. METHODS: A total of 12,795 compounds from the different databases were used to screen against the COX-2 enzyme. It perceived three new compounds with better binding affinity to the enzyme. Afterwards, physicochemical properties and in silico bioactivity were assessed for efficacy, safety, and structural features required for binding. The molecules were synthesized and confirmed by spectroscopic techniques. Later on, molecules were evaluated for their anti-cancer activity using MCF-7, MDA-MB-231 and SiHa cancer cell lines. RESULTS: Compound ZINC5921547 and ZINC48442590 (4a, and 4b) reduced the MCF-7, MDA-MB-231, and SiHa cells proliferation potently than parent compounds. The PG-E2 estimation shown, both compounds act through the COX-2 PGE2 axis. Compound 4a and 4b block the cell cycle at G1-S phase and induce cancer cell death. CONCLUSIONS: We concluded that compounds 4a and 4b effectively promotes cancer cell death via COX-2 PGE2 axis, and further in vivo studies can be evaluated for development in both compounds as anticancer agents. The compilation of this information will help us to generate better outcome through robust computational methods. The high-quality experimental results may pave the way for identifying effective drug candidates for cancer treatment.

Discovering Deleterious Single Nucleotide Polymorphisms of Human AKT1 Oncogene: An In Silico Study.

BACKGROUND: AKT1 is a serine/threonine kinase necessary for the mediation of apoptosis, angiogenesis, metabolism, and cell proliferation in both normal and cancerous cells. The mutations in the AKT1 gene have been associated with different types of cancer. Further, the AKT1 gene mutations are also reported to be associated with other diseases such as Proteus syndrome and Cowden syndromes. Hence, this study aims to identify the deleterious AKT1 missense SNPs and predict their effect on the function and structure of the AKT1 protein using various computational tools. METHODS: Extensive in silico approaches were applied to identify deleterious SNPs of the human AKT1 gene and assessment of their impact on the function and structure of the AKT1 protein. The association of these highly deleterious missense SNPs with different forms of cancers was also analyzed. The in silico approach can help in reducing the cost and time required to identify SNPs associated with diseases. RESULTS: In this study, 12 highly deleterious SNPs were identified which could affect the structure and function of the AKT1 protein. Out of the 12, four SNPs-namely, G157R, G159V, G336D, and H265Y-were predicted to be located at highly conserved residues. G157R could affect the ligand binding to the AKT1 protein. Another highly deleterious SNP, R273Q, was predicted to be associated with liver cancer. CONCLUSIONS: This study can be useful for pharmacogenomics, molecular diagnosis of diseases, and developing inhibitors of the AKT1 oncogene.

Identification of 5-nitroindazole as a multitargeted inhibitor for CDK and transferase kinase in lung cancer: a multisampling algorithm-based structural study.

Lung cancer is the second most common cancer, which is the leading cause of cancer death worldwide. The FDA has approved almost 100 drugs against lung cancer, but it is still not curable as most drugs target a single protein and block a single pathway. In this study, we screened the Drug Bank library against three major proteins- ribosomal protein S6 kinase alpha-6 (6G77), cyclic-dependent protein kinase 2 (1AQ1), and insulin-like growth factor 1 (1K3A) of lung cancer and identified the compound 5-nitroindazole (DB04534) as a multitargeted inhibitor that potentially can treat lung cancer. For the screening, we deployed multisampling algorithms such as HTVS, SP and XP, followed by the MM\GBSA calculation, and the study was extended to molecular fingerprinting analysis, pharmacokinetics prediction, and Molecular Dynamics simulation to understand the complex's stability. The docking scores against the proteins 6G77, 1AQ1, and 1K3A were - 6.884 kcal/mol, - 7.515 kcal/mol, and - 6.754 kcal/mol, respectively. Also, the compound has shown all the values satisfying the ADMET criteria, and the fingerprint analysis has shown wide similarities and the water WaterMap analysis that helped justify the compound's suitability. The molecular dynamics of each complex have shown a cumulative deviation of less than 2 Å, which is considered best for the biomolecules, especially for the protein-ligand complexes. The best feature of the identified drug candidate is that it targets multiple proteins that control cell division and growth hormone mediates simultaneously, reducing the burden of the pharmaceutical industry by reducing the resistance chance.

Cyclooxygenase-2 as a therapeutic target against human breast cancer: A comprehensive review.

Cyclooxygenase-2 (COX-2) is a key aspect of the physiology and pathogenesis of various cancer types. Overexpression of this enzyme is responsible for the elevated prostaglandin production and characteristic feature of breast cancer. Inhibition of COX-2 derived prostanoids facilitates anti-inflammatory, analgesic, and antipyretic effects of non-steroid anti-inflammation drugs. The overexpression of COX-2 is associated with inflammation, pain, and fever. The present study provides the updated relevant literature describing the role of well-characterized isoforms of cyclooxygenase with particular emphasis on COX-2, mechanism of action, the effect of the drug, combinatorial drugs, and microarray-based differential expression analysis and network analysis. We have discussed the currently used combinatorial treatments and their challenges in breast cancer. This article is categorized under: Cancer > Computational Models Cancer > Molecular and Cellular Physiology.

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.

SARS-CoV-2 Variants Show a Gradual Declining Pathogenicity and Pro-Inflammatory Cytokine Stimulation, an Increasing Antigenic and Anti-Inflammatory Cytokine Induction, and Rising Structural Protein Instability: A Minimal Number Genome-Based Approach.

Hyper-transmissibility with decreased disease severity is a typical characteristic of the SARS-CoV-2 Omicron variant. To understand this phenomenon, we used various bioinformatics approaches to analyze randomly selected genome sequences (one each) of the Gamma, Delta, and Omicron variants submitted to NCBI from December 15 to 31, 2021. We report that the pathogenicity of SARS-CoV-2 variants decreases in the order of Wuhan > Gamma > Delta > Omicron; however, the antigenic property follows the order of Omicron > Gamma > Wuhan > Delta. The Omicron spike RBD shows lower pathogenicity but higher antigenicity than other variants. The reported decreased disease severity by the Omicron variant may be due to its decreased pro-inflammatory and IL-6 stimulation and increased IFN-γ and IL-4 induction efficacy. The mutations in the N protein are probably associated with this decreased IL-6 induction and human DDX21-mediated increased IL-4 production for Omicron. Due to the mutations, the stability of S, M, N, and E proteins decreases in the order of Omicron > Gamma > Delta > Wuhan. Although a stronger spike RBD-hACE2 binding of Omicron increases its transmissibility, the lowest stability of its spike protein makes spike RBD-hACE2 interaction weak for systemic infection and for causing severe disease. Finally, the highest instability of the Omicron E protein may also be associated with decreased viral maturation and low viral load, leading to less severe disease and faster recovery. Our findings will contribute to the understanding of the dynamics of SARS-CoV-2 variants and the management of emerging variants. This minimal genome-based method may be used for other similar viruses avoiding robust analysis.

In-silico analysis reveals Quinic acid as a multitargeted inhibitor against Cervical Cancer.

The cervix is the lowermost part of the uterus that connects to the vagina, and cervical cancer is a malignant cervix tumour. One of this cancer's most important risk factors is HPV infection. In the approach to finding an effective treatment for this disease, various works have been done around genomics and drug discovery. Finding the major altered genes was one of the most significant studies completed in the field of cervical cancer by TCGA (The Cancer Genome Atlas), and these genes are TGFBR2, MED1, ERBB3, CASP8, and HLA-A. The greatest genomic alterations were found in the PI3K/MAPK and TGF-Beta signalling pathways, suggesting that numerous therapeutic targets may come from these pathways in the future. We, therefore, conducted a combined enrichment analysis of genes gathered from various works of literature for this study. The final six key genes from the list were obtained after enrichment analysis using GO, KEGG, and Reactome methods. The six proteins against the identified genes were then subjected to a docking-based screening against a library of 6,87,843 prepared natural compounds from the ZINC15 database. The most stable compound was subsequently discovered through virtual screening to be the natural substance Quinic acid, which also had the highest binding affinity for all six proteins and a better docking score. To examine their stability, the study was extended to MM/GBSA and MD simulations on the six docked proteins, and comparative docking-based calculations led us to identify the Quinic Acid as a multitargeted compound. The overall deviation of the compound was less than 2 Å for all the complexes considered best for the biological molecules, and the simulation interaction analysis reveals a huge web of interaction during the simulation.Communicated by Ramaswamy H. Sarma.

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.

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.

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.

Modular network inference between miRNA-mRNA expression profiles using weighted co-expression network analysis.

Connecting transcriptional and post-transcriptional regulatory networks solves an important puzzle in the elucidation of gene regulatory mechanisms. To decipher the complexity of these connections, we build co-expression network modules for mRNA as well as miRNA expression profiles of breast cancer data. We construct gene and miRNA co-expression modules using the weighted gene co-expression network analysis (WGCNA) method and establish the significance of these modules (Genes/miRNAs) for cancer phenotype. This work also infers an interaction network between the genes of the turquoise module from mRNA expression data and hubs of the turquoise module from miRNA expression data. A pathway enrichment analysis using a miRsystem web tool for miRNA hubs and some of their targets, reveal their enrichment in several important pathways associated with the progression of cancer.

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.