Bioinformatics approaches in tuberculosis vaccination strategies by whole genome sequence

• Published in: 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW) pp. 289 - 293
• Main Authors: Ai Ling Ong, Chee Keong Kwoh
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-12-2010
• Subjects: Artificial neural networks; Bioinformatics; Genomics; Microorganisms; Mycobacterium tuberculosis; Phylogeny; Proteins; Vaccine; Vaccines
• ISBN: 1424483034; 9781424483037
• DOI: 10.1109/BIBMW.2010.5703814

Following the availability of whole genome sequence of Mycobacterium tuberculosis (MTB) in public database, the anticipation of its benefits would definitely falls into the contribution towards development of improved vaccine or drug discovery to fight against the fatal disease, tuberculosis (TB). This research aimed to scan the whole genome sequence of MTB by application of bioinformatics approaches to identify most potential candidates for novel or improved TB vaccine since the formal vaccine, BCG has some drawbacks. The whole genome of MTB is scanned to search for potential ORF by performing whole genome BLAST search and data retrieval from SWISSPROT. It is then followed by a series of filtering and screening to fulfil the criteria for selection of rational vaccine targets. Among the filtered results, 196 proteins records had been found and most of them are surface exposed or membrane bound proteins or antigen, which are the important criteria for potential vaccine candidates. This research had yield potential vaccine targets for further vaccination development that will save many efforts of researchers in this field.