Computational Annotation for Hypothetical Proteins of Mycobacterium Tuberculosis

Abstract

AbstractThere is rising death of humans worldwide by reason of tuberculosis. The current sequencing of the Mycobacterium tuberculosis genome holds assure for the development of new vaccines and the design of new drugs. In this view, the functions prediction of genomic sequences for hypothetical proteins will invigorate our knowledge with reference to the identification of new drugs for tuberculosis. There are various function prediction methods available based on the on the assumption. The process accurate annotation for genes in newly sequenced genomes currently has been based on sequence similarity. In this work about 250 hypothetical proteins of Mycobacterium tuberculosis taken functions were predicted using Bioinformatics web tools, BLAST, INTERPROSCAN, PFAM and COGs.