title: Protein network prediction and topological analysis in Leishmania major as a tool for drug target selection.
abstract: 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 detected 142 potential drug targets after homology filtering with the human proteome. Further experiments can be done to validate these targets.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Programa de Estudio y Control de Enfermedades Tropicales-PECET, Universidad de Antioquia, Calle 62 No 52-59, Lab. 632, Medellín, Colombia.
authors.forename: Andrés F
authors.initials: AF
authors.lastname: Flórez
authors.forename: Daeui
authors.initials: D
authors.lastname: Park
authors.forename: Jong
authors.initials: J
authors.lastname: Bhak
authors.forename: Byoung-Chul
authors.initials: BC
authors.lastname: Kim
authors.forename: Allan
authors.initials: A
authors.lastname: Kuchinsky
authors.forename: John H
authors.initials: JH
authors.lastname: Morris
authors.forename: Jairo
authors.initials: J
authors.lastname: Espinosa
authors.forename: Carlos
authors.initials: C
authors.lastname: Muskus
mesh_terms: D000981:Antiprotozoal Agents / Q000737:chemistry; D019295:Computational Biology / Q000379:methods*; D055808:Drug Discovery; D006801:Humans; D018320:Leishmania major / Q000187:drug effects / Q000378:metabolism*; D007896:Leishmaniasis / Q000188:drug therapy; D025941:Protein Interaction Mapping / Q000379:methods*; D020543:Proteome / Q000737:chemistry / Q000378:metabolism; D015800:Protozoan Proteins / Q000737:chemistry / Q000378:metabolism*
chemical_list: D000981:Antiprotozoal Agents; D020543:Proteome; D015800:Protozoan Proteins