International Journal of Computational Bioinformatics and In Silico Modeling
ABSTRACT: Zinc-regulated transporter 1 (tzn-1) is a membrane protein that is mainly involved in transport of zinc and iron across the membrane, thereby helping in maintenance of the homeostasis. The previous in silico studies suggests that deletion of this protein coding gene results in aconidiation. Even though tzn-1 gene is not related to conidial formation pathway, still it resulted in aconidiation. To understand this mechanism and its interacting partners the tertiary structure of the protein is essential and hence this study was initiated. In the present study Fold recognition based model of tzn-1 was built using Photosynthetic Reaction Center from Rhodopseudomonas viridis (1PRC_M) as a template. The model was refined by optimization and step wise energy minimization process. The refined model was placed into a POPC/TIP3P membrane system and was simulated for 20ns at 300K temperature with NPT ensemble. Amino acid residues Asp 183, His 185, His 189 and Glu 179, His 199, His 200 are the key active sites for binding of zinc ion. The Root Mean Square Fluctuation from dynamic simulations at the active sites is less and the overall structure quality is 81.2% with 96.2% amino acids in the favorable regions of Ramachandran plot. This study reports the simulated tertiary structure of tzn-1 and dynamics study in the membrane and aqueous environment.
KeyWords: Zinc transporter; Neurospora crassa; Fold recognition; Docking; Molecular Dynamics.
How to cite: P.V Divya Rupa et. al. Molecular modeling and dynamic simulations of zinc transporter (tzn-1) protein from Neurospora crassa. Int J Comput Bioinfo In Silico Model. 4(4) 2015: 683-690
ABSTRACT: Phenol and its derivatives are one of the largest groups of environmental pollutants. Their presence in many industrial effluents and broad application as antibacterial and antifungal agents has made it an alarming issue for environmental safety. Phenol hydroxylase plays an important role in biological and eco-friendly degradation of phenol like aromatic compounds. So, phenol hydroxylase is deliberated for a solution of environmental pollution occurred by aromatic compounds. In this study, we have focused on the characterization and homology modeling of phenol hydroxylase. The physicochemical properties of the selected phenol hydroxylase were analyzed by using ExPASy’s ProtParam tool and it was found that the molecular weight (M.Wt) ranges around 38500 Da. Isoelectric Points (pI) exhibits acidic nature and aliphatic index infers that 95% phenol hydroxylase are stable. The negative value of GRAVY indicates that there will be better interaction with water. Homology modeling of phenol hydroxylase taken from Pseudomonas sp. M1(Accession numbers: WP_009619663.1) was performed by I-TASER. The validation of 3D structure was done using RAMPAGE, PROCHECK and ERRAT.
KeyWords: Phenol hydroxylase, aromatic compounds, xenobiotics, assimilation, monooxygenase.
How to cite: Mahmudul Hasan et. al. Computational Study and Homology Modeling of Phenol Hydroxylase: Key Enzyme for Phenol Degradation. Int J Comput Bioinfo In Silico Model. 4(4) 2015: 691-698
ABSTRACT: Multiple myeloma (MM), which has the second most incidences among hematologic malignancy in Finland, is a cancer of plasma cells. This study is aimed to explore the molecular mechanisms of MM. Peripheral blood mono nuclear cells samples from ten MM and five healthy donors (HD) as well as bone marrow mesenchymal stromal cells samples from four MM and three HD were used to identify DEGs by ROTS statistical test. Pathway enrichment analysis of DEGs was conducted and the protein-protein interactions (Ppi) network was constructed, followed by functional enrichment analysis of Ppi network modules. Bioinformatics tool was used to predict the drug interaction which can reverse the gene expression in MM. The integration of these two GEO datasets resulted in 71 DEGs, including 59 up-regulated and 12 down-regulated genes. Up-regulated genes were enriched in 9 pathways such as Glucocorticoid Receptor Pathway, Direct p53 effectors, TNF alpha Signaling Pathway, RIG-I-like receptor signaling pathway, Apoptosis, while down-regulated genes were enriched in alk in cardiac myocytes, Mitotic cell cycle and TGF Beta Signaling Pathway. In Ppi analysis, A total of 106 nodes representing genes and 234 edges representing Ppi relationship was obtained. Genes in module A were enriched with 19 GO Terms such as COPI coated vesicle budding, Golgi vesicle budding, DNA replication initiation, DNA-dependent DNA replication. The DEGs, such as TANK, YOD1, TGFBR1 have potential to be used as the targets for MM. Kaempferol and phensuximide may be potential therapeutic antagonist for MM.
KeyWords: multiple myeloma, hematology, differential expression, carcinoma.
How to cite: Akash Kumar Rauniyar et. al. Integrated Bioinformatics analysis of differentially expressed genes (DEGs) of multiple myeloma (MM) datasets from gene expression omnibus (GEO). Int J Comput Bioinfo In Silico Model. 4(4) 2015: 699-708