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A bioinformatics based approach to identify targets for potential drug treatment of tuberculosis

Urvashi Bhattacharyya

Protein interaction networks
Protein interaction networks   (Photo: Kunchur Guruprasad)

Finding a cure for tuberculosis (TB) is among the oldest medical challenges, with earliest efforts dating back to as far as five thousand years ago. Even modern programmes like vaccination and extensive drug treatments fail to contain the epidemic proportions of TB. India alone faces an infection rate of more than 2 million per year, with a worldwide incidence of more than 9 million per year. Rise of multi-drug resistant strains of M. tuberculosis compounds the problem of preventing TB outbreaks. Continued efforts in finding novel drugs or identifying drug targets to treat TB are therefore vital. 

In a new study, scientists from the Centre for Cellular and Molecular Biology (CCMB), Hyderabad, identified 94 potential new drug targets using bioinformatic tools. The study, performed by Settu Sridhar, Pallabini Dash and Kunchur Guruprasad was published in the journal Gene. One of the benefits of using a bio-informatic based approach is that intermolecular interactions can be studied or simulated on a computer, shortening the time to study them, as well as increasing the range of interactions that can be assessed. It is thus possible to discover proteins of the TB bacteria that could be likely drug targets. To do this, the team first identified conserved protein sequences from 23 strains of M. tuberculosis and short-listed those that do not share highly homologous sequences common to the human proteins. The 1478 proteins so obtained were thus specific to M. Tuberculosis. The proteins were then ranked based on the number of interacting partners they had—the researchers filtered out those protein hubs that had maximum interactions with other proteins in the cell. The reasoning is based on the fact that in a highly inter-connected network, when a crucial link is blocked, it can lead to a failure in functioning of that network. Potential drug targets are typically proteins that (a) have been essential to the survival of bacteria and therefore conserved or (b) are nodes with which a large number of other proteins interact. This approach allowed the team to narrow down their search for possible targets by identifying 17 clusters comprising 140 protein interactions. Since 46 out of these 140 proteins were already identified, the team focused on the remaining 94 potential targets and analysed their structure and function. 

Next, using a drug-target interaction network known as ‘TB-drugome’, the authors went on to identify 10 ‘druggable’ targets among the 94 proteins. This meant that some treatments already available in the market, like rifampin, aliretinoin, trifluoperazine, etc. could be re-tasked or switched towards these 10 proteins for therapeutic purposes. Guruprasad explains “This approach has commercial pharmaceutical implications since many drugs are known to hit multiple targets. Our work may be useful for deconvolution of the hits.” On the role of bioinformatics in discovering targets, he says, “Bioinformatics today has provided us with data and software tools that enable us to make informed choices in a much more rational way that is useful for drug discovery. It gives us hope for innovation in healthcare in the times to come.”

And hope we must. Rapidly evolving pathogens that evade not only the immune system but also develop resistance to man-made drugs require constant scientific efforts on our part to combat them. TB is not alone in developing multi-drug resistant strains. Indiscriminate use of antibiotics has led to an increase in other drug resistant bacterial strains such as E.coli as well. Thus, apart from scientific advancements, a proper implementation of antibiotic regime is also the need of the hour.