Protein network prediction and topological analysis in Leishmania major as a tool for drug target selection

Background: Leishmaniasis is a virulent parasitic infection that causes a worldwide disease burden. Most treatments have toxic side-effects and efficacy has decreased due to the emergence of resistant strains. The outlook is worsened by the absence of promising drug targets for this disease. We have taken a computational approach to the detection of new drug targets, which may become an effective strategy for the discovery of new drugs for this tropical disease. Results: We have predicted the protein interaction network of Leishmania major by using three validated methods: PSIMAP, PEIMAP, and iPfam. Combining the results from these methods, we calculated a high confidence network (confidence score > 0.70) with 1,366 nodes and 33,861 interactions. We were able to predict the biological process for 263 interacting proteins by doing enrichment analysis of the clusters detected. Analyzing the topology of the network with metrics such as connectivity and betweenness centrality, we detec...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Andrés F. Flórez, Daeui Park, Jong Bhak, Byoung-Chul Kim, Allan Kuchinsky, John H. Morris, Jairo Espinosa, Carlos Muskus
• Keywords: BMCBI 2010, Disease, Drug Targets, Protein Interaction Network