Journal of
Computational Biology and Bioinformatics Research

  • Abbreviation: J. Comput. Biol. Bioinform. Res
  • Language: English
  • ISSN: 2141-2227
  • DOI: 10.5897/JCBBR
  • Start Year: 2009
  • Published Articles: 39

Full Length Research Paper

In silico studies of multi drug resistance (MDR) genetic markers of Plasmodium species

Clarence Suh Yah1* and Segun Fatumo2
1School of Chemical and Metallurgical Engineering, University of the Witwatersrand, Johannesburg,  Private Bag 3, Wits 2050, South Africa. 2Bioinformatics Unit, Department of Computer and Information Sciences, College of Science and Technology, Covenant University, Ota, Ogun State, Nigeria.
Email: [email protected], [email protected]

  •  Accepted: 07 December 2009
  •  Published: 31 March 2010


Multi-drug resistance malaria species has been and still is the cause of much morbidity and mortality of malaria throughout the tropics. This epidemic has devastated large populations likewise posed a serious barrier to economic growth in developing countries. The major obstacles however, is it prevention and treatment due to emerging multi-drug resistant (MDR) species. Therefore, anti-malarial drug development needs to continue so that novel and highly effective anti-malarial can be plugged into recommended strategies and vaccine development.The sequencing of the various MDR genes of Plasmodium has contributed tremendously to the understanding of the MDR malaria parasites. The current research therefore, engaged the use of an in-silico approach to seek the strategies in analyzing as well as offering some likely solutions to malaria therapies. Four Plasmodium species: 2 from rodents (Plasmodiumchabaudi and Plasmodium yoelii) and 2 from human (Plasmodium vivax and Plasmodiumfalciparum) multi drug resistance genes were compared using bioinformatics tools. The phylogenetic relationships and species identification of the MDR genes of the parasites were downloaded from web base resources and performed as confirmed by the ClustalX programs. The results showed a variation in the up/down stream algorithms alignment of their phylogenetic relationships. This therefore, showed that some resistance genes within a population may vary within the same drug. The results showed a significant difference of p < 0.001 with a 95% CI. Through these efforts, our goal was to better understand how drug resistance occurs. This knowledge therefore, will facilitate the rationale to design new effective as well as check the emerging of multi-drug resistant Plasmodium strains.


Key words: In silico, comparison, multi drug, resistance genes, Plasmodium.