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

Abstract

BackgroundLeishmaniasis 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.ResultsWe 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.ConclusionWe 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.

Prediction of protein–protein interactions between Ralstonia solanacearum and Arabidopsis thaliana

“…Diverse data types or properties, such as gene ontology (GO) annotations (Wu et al 2006), protein sequence similarity (Matthews et al 2001), protein domain interactions (Ng et al 2003), and protein structural information (Ogmen et al 2005), have been frequently utilized to construct PPI prediction methods. Two widely implemented methods are probably the interolog and the domain-based methods (Shoemaker and Panchenko 2007;Florez et al 2010).…”

Current Advances in Computational Strategies for Drug Discovery in Leishmaniasis

“…These specific interactions will be only detected when more data becomes available, which will also allow existent predictions to be validated. In our recent study (Florez., et al 2010), the protein interaction network in Leishmania major was predicted using only the parasite protein sequences and several protein interaction databases, in particular iPfam (Finn., et al 2005), PSIMAP (Park., et al 2005) and PEIMAP. These databases included protein-protein interactions defined by analysis of structures of protein complexes and experimental data extracted from literature, including highthroughput experiments.…”

Analysis of Protein Interaction Networks to Prioritize Drug Targets of Neglected-Diseases Pathogens

“…Florez et al, (2010) built an in silico PIN of L. major by combining information of PSIMAP, PEIMAP, iPfam databases, and using the interologs method (Florez et al, 2010). They predicted 33,861 interactions for 1,366 proteins, and also analyzed the PIN by calculating topology parameters such as connectivity and betweenness centrality detecting 142 potential and specific drug targets without human orthologs (Fig.…”

“…The authors thank BioMed Central for allowing the reproduction of figures 8 and 9 (Florez et al, 2010, Navratil et al, 2011. Mario A. Rodríguez-Pérez and Xianwu Guo holds a scholarship from Comisión de Operación y Fomento de Actividades Académicas (COFAA)/IPN.…”