Drug target repositioning in neglected tropical diseases using a tripartite network-based approach

ARIEL BERENSTEIN; MARIA PAULA MAGARIÑOS; ARIEL CHERNOMORETZ; FERNAN AGUERO

Congreso; 4to. Congreso Argentino de Bioinformática y Biología Computacional (4CAB2C) y 4ta. Conferencia Internacional de la Sociedad Iberoamericana de Bioinformática (SolBio); 2013

Soc. Iberoamericana de Bioinformatica & Asoc Arg. Bioinformatica y Biologia Computacional

Neglected tropical diseases (NTDs) are human infectious diseases that occur in tropical or subtropical regions andare often associated with poverty. Recently, the availability of open chemical information has increased with theadvent of public domain chemical resources. In our laboratory, our goal is to prioritize and identify candidate drugtargets, and candidate drug-like molecules to foster drug development in Trypanosoma cruzi (causative agent ofChagas disease), taking advantage of the availability of drug-target data from other model organisms that havebeen extensively studied, like human, yeast, and mouse.Chemical datasets were obtained from open databases and high throughput screenings. Starting from these data,we built a tripartite network considering three disjoint set of vertexes with approximately 1.7 10 5 drugs and 1.7 105proteins across more than 150 species, organized in three different planes (Fig. 1A). Three different classes oftarget similarity criteria were considered: sharing of PFAM domains, clustering in the same ortholog group(OrthoMCL algorithm), and belonging to the same metabolic pathway. A bipartite projection was made using amodified version of the Zhou method [2] over the protein plane (Fig. 1b). In the resulting monopartite protein-protein network, proteins are linked if and only if, they share at least one relevant biological relation. Finally, inorder to get a prioritization list of potential targets, a voting scheme was performed using all known sets of drug-targets associations.