International Journal of Computational Bioinformatics and In Silico Modeling
ABSTRACT: Gene expression profiling provides unprecedented opportunities to study patterns of gene expression regulation. Rheumatoid arthritis is an autoimmune disease that causes chronic joint inflammation. The dominant local cell populations in joints affected by rheumatoid arthritis are synovial and cartilage cells. Synovial cells can be divided into fibroblast-like and macrophage-like synoviocytes. Macrophages are rich sources and major producers of pro-inflammatory cytokines including Tumor necrosis factor (TNF), Interleukin-1 (IL-1), Interleukin-6 (IL-6), Interleukin-8 (IL-8), and Granulocyte macrophage colony-stimulating factor (GMCSF). Macrophages are also producers of prostaglandins and leukotriene’s, nitric oxide, and other pro-inflammatory mediators with local and systemic effects. For understanding the differentially expressed genes in synovial macrophages for Rheumatoid Arthritis, analysis of gene expression profiling data derived from micro-array technology was done. The dataset was downloaded by the publically available server GEO database, the raw data file was normalized and compared using dChip. The normalized file has a set of 12,558 genes. The differentially expressed genes were identified on the basis of 1.5 fold change. Genes with fold change values ≥ 1.5 were categorized as up-regulated and those genes with fold change values ≤ -1.5 were categorized as down-regulated genes. 2375 genes were found to be up-regulated and 1657 genes were found to be down-regulated. These differentially expressed genes were again studied by applying clustering and correlation analysis. Gene-gene correlation analysis of the individual gene clusters gave promising highly correlated gene interactions (Correlation coefficient≥ 0.9). Functional characterization of the highly up regulated and down regulated genes gave an insight for developing novel targets for rheumatoid Arthritis.
KeyWords: Synovial macrophages, Joint disorder, Rheumatoid Arthritis, Expression Based Studies, Microarray.
How to cite: Sachidanand Singh and J.J Vennila. Int J Comput Bioinfo In Silico Model. 3(2) 2014: 332-336
ABSTRACT: Computational analysis and predictions based on biological data have increasingly becoming an essential and integral part of modern biology. As every biological research is now coupled with bioinformatics, there is a growing need to develop versatile bioinformatics software packages, which are efficient and incorporate the latest developments in this field. A number of such packages are available in the public domain. Though several such packages are available for public, yet few are reliable and easy to use. Hence, this attempt was undertaken to create a new software package which could be versatile for students. Thus, we describe BOSS Bishop Open Software Suite: A Bioinformatics software package for sequence Analysis. The Package is developed with a PERL programming and DOS prompt.
KeyWords: Software packages; Sequence analysis; BOSS; PERL
How to cite: Maruthamuthu Rajadurai et al. Int J Comput Bioinfo In Silico Model. 3(2) 2014: 337-339
ABSTRACT: Virus is transmitted to human by mosquitoes. The virus has the potential to expand globally and is responsible for a wide range of clinical manifestations which result in an economic burden due to the unavailability of specific drugs. The purpose of this work was to study the detailed structure of 127 amino acids long non-structural protein NS4A from Dengue serotype 3 with the aim to find its potential binding sites to be exploited for potential inhibitors. For 3D structure prediction “MODELER 9V10 was used, and [PDB: 2WZ9] having 33% identity was selected as a template. Stereochemistry of the models was evaluated by PROCHECK and ProSA. All protein structures and models were visualized and analyzed with the help of DS Visualizer. The secondary structure of protein was determined by using different secondary structure prediction servers DPM, DSC, GOR4, HNNC, PHD, Predator, SIMPA96 and SOPM. OCTOPUS was used for trans-membrane topology of the protein. The antigenic residues were predicted using the antigenicity plotting server. The conserved residues and their percentages were calculated using the “online CDD Server”. The ligand binding sites were predicted using “online server Q-Finder”. Patch Dock Server was used for docking.
The NS4A protein of dengue serotype 3 shows 48.03% conserved residues. The consensus secondary structure shows close similarity with the predicted 3D model having eight α helices and four β pleated sheets with no β bridge. Comparative analysis with thioredoxin domain of human TXNL2 indicates that the β sheets are similar in nature in both of these proteins, and also have significant numbers of conserved residues. NS4A is hydrophobic in nature with trans-membrane domains and has ten catalytic sites. Docking with these binding sites shows that Albaconazole and zafirlukast interact with the protein through three and seven hydrogen bonds respectively. The predicted 3D structure of the NS4A with ten potential active binding sites was targeted by Albaconazole and zafirlukast. The drugs showed effective binding and may be suggested as potential inhibitors for NS4A.
KeyWords: Dengue virus, Non Structural Protein 4A, ligand binding sites, trans-membrane, antigenic sites, Albaconazole, Zafirlukast
How to cite: Abid Ali et al. Int J Comput Bioinfo In Silico Model. 3(2) 2014: 340-346
ABSTRACT: Dyslexia, or reading disability, is an inheritable trait and a few candidate genes have been identified in human to be associated with dyslexia. The transcription rates of genes are often controlled by regulatory elements such as enhancers and silencers that could be close by or far away. In this paper, we use an alignment-free method to search for regulatory regions that potentially regulate dyslexia genes in human. Our approach is based on the premise that related genes are regulated under similar pathways, and thus we search for regions nearby dyslexia genes having high similarity with each other in an alignment-free manner. The result is that we found new potential 100-bp regulatory regions for three dyslexia genes.
KeyWords: dyslexia, regulatory elements, pattern matching, sequence alignment
How to cite: Chi-Hang Fred Fung and Mary M.Y. Waye. Int J Comput Bioinfo In Silico Model. 3(2) 2014: 347-353
ABSTRACT: Alginate Lyase is an enzyme intended to catalyze the degradation of alginate, which facilitate to have its medical importance being useful to reduce cystic fibrosis complications, along with its industrial use. Although some sources of alginate lyase were found to be effective during laboratory test but they were inappropriate for medical use, mainly due to the complexity of their insufficient production. In this study, we were deliberate to find new efficient sources to solve the problem, and thus alginate lyases from different source of organisms were analyzed through bioinformatics tools for phylogenetic analysis, homology & motif analysis for both therapeutic and industrial purposes. We also found a conserved “alginate_lyase2” domain uniformly observed in alginate lyase sequences. Among the alginate lyase-producing bacterial strain, suggesting that Pseudoalteromonas sp. SM0524 is a good producer of alginate lyases and also showed activities toward both poly G (α-L-guluronic acid) and poly M (β-D-mannuronic acid), indicating that it is a bifunctional alginate lyase. So the homology model of the wild type alginate lyase (AlyPA) of marine bacterium Pseudoalteromonas sp. SM0524 was built using the crystal structure of the Family 18 alginate lyase from Alteromonas sp.272. This structural model may reveal the insight of binding mode with its substrate which provide a suitable base for the future rational design of new mutated AlyPA structures with improved catalytic activity that can be a possible solution for cystic fibrosis complications and may be an efficient tool to produce novel alginate dimers and tetramers.
KeyWords: Alginate; Cystic Fibrosis; Alginate lyase; AlyPA; Homology Model
How to cite: Jahed Ahmed et. al. Int J Comput Bioinfo In Silico Model. 3(2) 2014: 354-361
ABSTRACT: The Gyrase A gene of Mycobacterium tuberculosis has been shown to be responsible for the quinolone drug resistance. The Gyrase gene sequences we submitted in NCBI were retrieved. Accession No: EU835533, EU835534, EU835535. (Mtb1, Mtb2, Mtb3) and BLAST analysis revealed 95% identity with the available Mycobacterium tuberculosis Gyrase A gene. These sequences were also compared with the available Mycobacterium Gyrase gene of Indian isolates. The Gyrase A proteins were modelled using Modeller 9v3 version and the Ramachandran plot evaluation of all the models showed 94% in favourable region. The resistant strain (EU512947) reported from Coimbatore was also modelled and the three dimensional structure comparison of Mtb1, Mtb2, Mtb3 with the resistant Coimbatore isolate Gyrase coding protein was done using STRAP. The molecular similarity of the GyraseA protein of Mtb3 and EU512947 suggests all possible means of conversion this strain to a resistant Mycobacterial species.
KeyWords: Mycobacterium tuberculosis; Drug resistance; Gyrase gene
How to cite: A. Sasikalaveni et. al. Int J Comput Bioinfo In Silico Model. 3(2) 2014: 362-368