Open Access Highly Accessed Research article

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

Andrés F Flórez1, Daeui Park2, Jong Bhak2, Byoung-Chul Kim2, Allan Kuchinsky3, John H Morris4, Jairo Espinosa5 and Carlos Muskus1*

Author Affiliations

1 Programa de Estudio y Control de Enfermedades Tropicales-PECET, Universidad de Antioquia, Calle 62 No 52-59, Lab. 632, Medellín, Colombia

2 Korean BioInformation Center (KOBIC), KRIBB, Daejeon, 305-806, Korea

3 Agilent Technologies, Santa Clara, California, USA

4 Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California, USA

5 Grupo de Automática-GAUNAL, Universidad Nacional Sede Medellín, Medellín, Colombia

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BMC Bioinformatics 2010, 11:484  doi:10.1186/1471-2105-11-484

Published: 27 September 2010



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.


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 detected 142 potential drug targets after homology filtering with the human proteome. Further experiments can be done to validate these targets.


We have constructed the first protein interaction network of the Leishmania major parasite by using a computational approach. The topological analysis of the protein network enabled us to identify a set of candidate proteins that may be both (1) essential for parasite survival and (2) without human orthologs. These potential targets are promising for further experimental validation. This strategy, if validated, may augment established drug discovery methodologies, for this and possibly other tropical diseases, with a relatively low additional investment of time and resources.