Volume-4, Issue-1 January - February 2015
International Journal of Computational Bioinformatics and In Silico Modeling
ABSTRACT: Lynch Syndrome is a hereditary disorder caused by a mutation in a Mismatch Repair gene (MLH1, MSH2, MSH6, PMS2, and EPCAM-TACSTD1) which affected persons have a higher than normal chance of increasing Colorectal Cancer, Endometrial Cancer which also known as Hereditary Nonpolyposis Colorectal Cancer (HNPCC). The mutation of a single gene dramatically increases as well as the chances of contracting cancer increases deletion in mismatch repair gene responsible for lynch syndrome. All genes work in solving mistakes which causes by DNA which is copied in preparation for cell division. The damage in genes prohibit repair of DNA mistakes and as cells segregate, defects stack and uncontrollable cell growth may result in cancer. A total of 60 siRNA were identified in silico, in which 6 most potentially siRNA were selected that noted the therapeutic and have high potential of targeted gene silencing in lynch syndrome. These therapeutic siRNA will be useful to knockdown the translation process in targeted gene causing Lynch syndrome. The siRNA have great potential to control the cancerous disease.
KeyWords: siRNA, Lynch Syndrome, Mismatch repair gene, MLH1, MSH2, MSH6.
How to cite: Neha Singh et. al. In silico designing of therapeutic small interfering RNA (si RNA) for lynch syndrome silencing. Int J Comput Bioinfo In Silico Model. 4(1) 2015: 578-584
ABSTRACT: Staphylococcus aureus is a gram positive, coagulase positive coccus in the family staphyloccocaceae. It is an opportunistic organism that has emerged as one of the predominant pathogens in community and healthcare-associated infections with limited and less effective options for treatment in the face of a rising trend in the emergence of resistant strains. This fact has necessitated the search for alternative targets for development of new drugs. In this present study, a subtractive genomic (proteome) approach was used to identify potential drug targets in methicillin resistant Staphylococcus aureus using strain 252 (MSRA252). The complete proteome of MSRA 252 obtained from Uniprot database was subjected to CD-hit suite for clustering; NCBI BlastP suite against the human proteome to exclude homologous proteins; and sequence homology with Database of Essential Genes(DEG) to determine the indispensability of the proteins for the bacteria survival. The essential proteins were further analyzed to predict the metabolic pathways they were involved in using KEGG automatic annotation server (KAAS) and their subcellular locations using, Uniprot and PsortB suite subsequently. The sequence sorting, segregation and formatting was carried out using UFS Sequence Analysis Application after each successive step. The study identified 291 essential non homologous proteins to human out of 2640. Further analysis with KAAS revealed that 114 (33 predicted membrane-associated) of the essential non homologous proteins were involved in different metabolic pathways in the organism and 60 of these were implicated in pathways unique to the bacteria relative to human (host). The study revealed a number of putative, essential non homologous protein candidates that could be further explored for the development of alternative treatments and vaccines for methicillin resistant Staphylococcus aureus infections.
KeyWords: Subtractive genomics, BlastP, CD-hit, KAAS, MSRA 252.
How to cite: Ononamadu Chimaobi James et. al. In silico identification of putative drug targets in methicillin resistant Staphylococcus aureus: a subtractive genomic approach. Int J Comput Bioinfo In Silico Model. 4(1) 2015: 585-591
ABSTRACT: Recent advances in genomics have brought about significant reduction in cost and time for sequencing genomes which is vital for disease prediction, drug discovery and personalized medicine. Subsequently, there is tremendous increase in genomics research data where data storage plays a crucial role. Hence, there is a great need for efficient techniques for compressing genomes. A novel reference based genome compression method has been proposed in this paper for compressing DNA sequences based on referential compression.
KeyWords: Compression, DNA Sequence, Index, Reference Genome.
How to cite: M. Mary Shanthi Rani. A New Referential Method for Compressing Genomes. Int J Comput Bioinfo In Silico Model. 4(1) 2015: 592-596
ABSTRACT: The 7-methoxy-2-phenyl-1-benzofuran-5-carbaldehyde was synthesised by known literature method (Wittig reaction approach). To deduce the anticancer and antibacterial activity of the 7-methoxy-2-phenyl-1-benzofuran-5-carbaldehyde, it is docked with different biomarkers of cancer cell and bacteria. Grid was generated for each oncoproteins by specifying the active site amino acids. The binding model of best scoring analogue with each protein was assessed from their G-scores and disclosed by docking analysis using the XP visualizer tool. An analysis of the receptor-ligand interaction studies revealed that 7-methoxy-2-phenyl-1-benzofuran-5-carbaldehyde is most active against 1VOM and 4FNY biomarkers and have the features to prove themselves as anticancer drugs.
KeyWords: Benzofurans, Molecular docking, Anticancer, 1VOM, 4FNY, Wittig reaction.
How to cite: Bapu R. Thorat et al. Synthesis and Molecular Docking of 7-Methoxy-2-Phenyl-1-Benzofuran-5-Carbaldehyde. Int J Comput Bioinfo In Silico Model. 4(1) 2015: 597-606
ABSTRACT: Agriculture play central role in human civilization development. As the world population continues to grow, more and more agriculture products are required to meet people’s need. Advent of newer approaches of omics sciences and technologies will enable to address several issues and challenges faced by modern agriculture and also ensure food and nutritional security. Exploiting the potential of ‘OMICS’ technologies for agricultural productivity, plant protection, nutritional and medicinal purposes have currently been receiving a lot of attentions. This report highlighted the importance of ‘OMICS’ based research as future of Indian agriculture with very useful recommendations.
KeyWords: Omics, Agricultural Productivity.
How to cite: Anil Kumar. Science of Omics for Agricultural Productivity: Future Perspective - A national conference report. Int J Comput Bioinfo In Silico Model. 4(1) 2015: 607-610