HIV Drug Resistance Research: KaKs Calculation Tool 1.0

It's a beautiful sunny Easter day in the historical town of Gettysburg. Days like these admittedly make any Malawian in Diaspora reminisce eating raw sugarcanes with fellow mboba [acquaintances]. Despite the elevated homesickness levels, I stumbled upon and immersed myself in a genomics article by Lamei Chen and Christopher Lee from the Department of Chemistry and Biochemistry at the UCLA, Los Angeles.

Related Fact:

HIV mutates within weeks of new drug discovery. However, the detection of mutation requires a combination of lengthy clinical research, some molecular biology (eg.DNA sequencing) and biochemistry(eg. obtaining viral samples).

Chen and Lee carried out a research that established a faster, probably cost-effective way of identifying relevant drug resistance mutations in HIV-I. The team distinguished HIV-I drug resistance by studying mutations using computational analysis tools to measure evolutionary selection pressure inorder to detect primary mutations versus accessory ones. Viral fitness versus drug resistance mutations.

So, Whats the fuss all about deal?

For my bionformatics term project, I implemented and developed a web-based naive version of the KaKs Calculator 1.0, a primary tool that Chen and Lee used in their research. By using KaKs values, the team compared trends between treated and untreated (HIV) datasets. Their study concluded that KaKs analysis could be an effective way of distinguishing drug-resistance that associated with mutations from viral fitness mutations. According to their research, this could be a helpful way to understand the roles of different mutations in the development of drug resistance.

I love it when computer science and Biology become incompatible. Go bioinformatics! Click on the Technocentrics logo to go to my web-based KaKs Calculation Tool.

Reference:

Chen L, Lee C: Distinguishing HIV-1 drug resistance, accessory, and viral fitness mutations using conditional selection pressure analysis of treated versus untreated patient samples. Biol Direct 2006, 1:14.

Fumbani Chibaka

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