PUTATIVE DRUG TARGET IDENTIFICATION FOR SEPTIC ARTHRITIS THROUGH DATA MINING APPROACH

Abstract

Septic arthritis is the purulent invasion of a joint by an infectious agent which produces arthritis. The main organisms having great potential to infect human beings as well as other mammals are Staphylococcus aureus, Streptococcus pneumoniaeand Streptococcus pyrogens. In Silico comparative analysis ofall the pathways of host Homo sapiens and pathogens was performed by using KEGG and Protein BLAST. 25, 20 and 16 unique pathways were identified for Staphylococcus aureus, Streptococcus pyrogensand Streptococcus pneumonia respectively. Out of these we identified 3 enzymes for Staphylococcus aureus, 4 for Streptococcus pneumoniae and 1 for Streptococcus pyrogens, which are non-homologous to Homo sapiens proteins. The enzymes essential for survival of the pathogens were found out by DEG database. Further CELLO analysis results showed that 50% enzymes are found to be Extracellular, 25% to be cytoplasmic and 25% to be membranous for Staphylococcus aureus. For Streptococcus pneumoniae, 50% enzymes are found to be Extracellular, 12% cytoplasmic, 13% membranous and 25% as cell wall proteins. 100% enzymes were found to be membranous for Streptococcus pyrogens. Finally, the enzymes from DEG were submitted in Drug Bank database to identify approve drug targets. This Data Mining approach found that mostly the enzymes which can act as targets belong to extracellular level in Staphylococcus aureus, Streptococcus pneumoniae and membranous in Streptococcus pyrogens. This finding gives an understanding of these enzymes’ interaction with human protein protein interaction at extracellular and membrane level.