METHYLATION PROFILING OF TUMOR SUPPRESSOR GENES INVOLVED IN LUNG CANCER

The causes of cancer are due to mutations of key proteins involved in cell cycle regulation, DNA repair enzymes and in some cases inactivation of tumor suppressor genes leads to the growth of tumors. The expression rates of TSGs vary in different stages of cancer as well in various cases of cancers. The inhibitions of TSGs are due to methylation of the DNA. In our present study, we found the genes which are methylated in different types of lung cancers and identified the methylation frequencies. Using the pubmeth database, we have identified the total number of genes undergoing methylation in the various lung cancers. From the total number of genes, we have identified the tumor suppressor genes which are in the methylated state leading to the inactivation of expression and promoting the tumor growth. The further focus on tumor suppressor genes which are methylated is necessary to find a novel way to activate them in the cancerous stage

Insilico Proteome Screening to Identify Prospective Drug Targets in Bacillus anthracis

Various Insilico based genome screening methods helped us in identifying the key drug targets for  a pathogen. The accuracy of the predictions are systematically based on the benchmarks at different stages of methodology and the kind of dataset which is considered for the study. In the current study, we made an effort to screen the entire proteome of Bacillus anthracis for identification of putative drug targets. B. anthracis is the causautive agent for anthrax disease. Instead of genome sequence, the metabolically classified proteome of B. anthracis from JCVI-CMR database was considered for the present study. The entire proteome is been categorized into 25 different metabolisms and in each sub-categorised metabolisms respective protein sequences were retrieved and subjected to screening against Database of Essential Genes (DEG) and Human-Basic Local Alignment Search Tool (H-BLAST) databases. In total 136 essential genes/proteins were obtained from the DEGp (protein) screening whereas 145 Non-Human Homologs (NHHs) were predicted. The identified 145 NHHs are further subjected to criteria based selection to identify the most suitable, functional, putative drug targets. The 8 common hits of both DEG and H-BLAST were considered to be better potential targets as they justify the criteria of being an essential gene/protein, non-human homolog, availability of the 3D structure in PDB and having a significant functional role in the cellular biochemical processes. 

COMPUTATIONAL INTERACTION OF ENTOMOPATHOGENIC FUNGAL SECONDARY METABOLITES WITH PROTEINS INVOLVED IN HUMAN XENOBIOTIC DETOXIFICATION

Objective: Entomopathogenic fungi are rich source of secondary metabolites which posses both pharmacological and insecticidal activity. It is essential to assess metabolite toxicity of chemically diverse toxic metabolites of entomopathogenic fungus. Human acetylcholine esterase, cytochrome p450 and glutathione S-transferase are important enzymes involved in human xenobiotic detoxification. Methods: In this study, in silico interaction of 13 selected secondary metabolites of entomopathogenic fungi with the target human proteins were carried out using Molegro Virtual Docker 4.0.2. Results: This study reveals serinocyclin-A, have shown highest binding energy (176.07 KJ mol-1) with glutathione S-transferase followed by helvolic acid, cytochalasin B and beauverolide H have shown considerable inhibition among the metabolites tested. Conclusion: The study concludes that serinocyclin-A, helvonic acid, cytochalasin B and beauverolide among 13 secondary metabolites tested were found to be more toxic and may inhibit the human metabolic pathways

Towards an understating of signal transduction protein interaction networks

Protein network analysis has witnessed a number of advancements in the past for understanding molecular characteristics for important network topologies in biological systems. The signaling pathway regulates cell cycle progression and anti-apoptotic molecules. This pathway is also involved in maintaining cell survival by modulating the activity of apoptosis through RAS, P13K, AKT and BAD activities. The importance of protein-protein interactions to improve usability of the interactome by scoring and ranking interaction data for proteins in signal transduction networks is illustrated using available data and resources