We report a computational approach that integrates structural bioinformatics, molecular modelling and systems biology to construct a drug-target network on a structural proteome-wide scale. The approach has been applied to the genome of Mycobacterium tuberculosis (M.tb), the causative agent of one of today's most widely spread infectious diseases. The resulting drug-target interaction network for all structurally characterized approved drugs bound to putative M.tb receptors, we refer to as the ‘TB-drugome’. The TB-drugome reveals that approximately one-third of the drugs examined have the potential to be repositioned to treat tuberculosis and that many currently unexploited M.tb receptors may be chemically druggable and could serve as novel anti-tubercular targets. Furthermore, a detailed analysis of the TB-drugome has shed new light on the controversial issues surrounding drug-target networks [1]–[3]. Indeed, our results support the idea that drug-target networks are inherently modular, and further that any observed randomness is mainly caused by biased target coverage. The TB-drugome (http://funsite.sdsc.edu/drugome/TB) has the potential to be a valuable resource in the development of safe and efficient anti-tubercular drugs. More generally the methodology may be applied to other pathogens of interest with results improving as more of their structural proteomes are determined through the continued efforts of structural biology/genomics.

Malaria is a disease contracted by over 200 million people each year, mostly in developing countries. The primary causative agent, Plasmodium falciparum (P. falciparum) has shown increased resistance to existing drugs, hence new treatments are needed quickly. To this end we performed a high-throughput systems-level analysis, mapping existing FDA drugs with the potential for repurposing against targets from the P. falciparum structural proteome. The resulting P. falciparum drugome (P.falciparum-drugome) was used to prioritize potential new anti-malaria candidate targets and highlight some novel FDA approved drugs that have apparent anti-malaria effects for possible use as multi-target therapeutics.is now widely accepted 2 . Stated another way, a drug response is a consequence of complex interactions between multiple intracellular and extracellular components. Thus designing drugs assuming multiple targets is becoming the new rational approach to drug discovery, but requires a system level view of drug action. Systems pharmacology provides that view through a systematic understanding of drug action by integrating systems biology (including biological network analysis 3 ), bioinformatics and cheminformatics approaches.Simultaneously, network analysis approaches of a different kind have proven useful for organizing high-dimensional biological datasets and extracting meaningful information. Such network approaches in systems pharmacology can provide a global view of drug relationships, identify new drug targets as well as therapeutic strategies, and improve our understanding of the side effects and alternative uses of current drugs 3-4 . Here we exploit such a network approach in determining the P. falciparum drugome, a structural proteome-wide drug-target interaction network that forms a basis for exploring alternative treatments for malaria.Malaria is one of the most devastating and widespread tropical parasitic diseases and is most prevalent in developing countries. The World Health Organization has estimated that over 200 million cases of malaria occur annually 5 ; in 2010, around 655,000 deaths were reported, but this is likely a significant underestimate. Approximately 87% of these deaths were children under the age of five 6 . To reduce the number of malaria infections and to reduce the death toll is an urgent priority.Malaria is caused by the Plasmodium parasite, which is transmitted by a mosquito vector. In humans, the parasites multiply in the liver, and then infect red blood cells. There are five species that are known to infect humans: P. falciparum, Plasmodium vivax, Plasmodium ovalae, Plasmodium malariae and Plasmodium knowelsi. Among these five species, the parasite P. falciparum is the most dangerous, with the highest rates of complications and mortality.Currently, there are no approved vaccines available and given increasing drug resistance, finding novel anti-malarial drugs and associated targets appears the most efficient way to fight malaria 7 .Previously Gamo et al. 8 screened nearly 2 million com...

Chagas disease is a major cardiovascular affliction primarily endemic to Latin American countries, affecting some ten to twelve million people worldwide. The currently available drugs, Benznidazole and Nifurtimox, are ineffecteive in the disease's chronic stages and induce severe side effects. In an attempt to improve this situation we use an in silico drug repurposing strategy to correlate drug-protein interactions with positive clinical outcomes. The strategy involves a protein functional site similarity search, along with computational docking studies and, given the findings, a phosphatidylinositol (PIP) strip test to determine the activity of Posaconazole, a recently developed antifungal triazole, in conjunction with Tiam1, a Rho GTPase Guanine Nucleotide Exchange Factor. The results from both computational and in vitro studies indicate possible inhibition of phosphoinositides via Posaconazole, preventing Rho GTPase-induced proliferation of T. cruzi, the etiological agent of Chagas Disease. SynopsisWhile Benznidazole and Nifurtimox are effective in the acute phase of Chagas' disease, their inefficacy in the disease's chronic phase and severe side effects motivate the search for more novel treatment pathways. Antifungal triazoles have shown promise in murine models of Chagas. Using a computational platform, the authors find that these drugs may be used to combat Chagas Disease through a previously unknown mechanism. If correct, this model could be used to motivate the production of new vaccines for Chagas. Furthermore, the model demonstrates the efficacy of computational testing in the preliminary search for effective drug candidates.

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