ArticleBMC bioinformatics2010
Protein network prediction and topological analysis in Leishmania major as a tool for drug target selection.
Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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35 citing papers in PubMed, 95 citations in OpenAlex.
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- Repurposing of known drugs for leishmaniasis treatment using bioinformatic predictions, in vitro validations and pharmacokinetic simulations.Journal of computer-aided molecular design · 2019Article
- Computer aided analysis of disease linked protein networks.Bioinformation · 2019Article
- Understanding Leishmania parasites through proteomics and implications for the clinic.Expert review of proteomics · 2018Review
- Building protein-protein interaction networks for Leishmania species through protein structural information.BMC bioinformatics · 2018Article
- Prediction ofGalen medical journal · 2018Article
- Protein-Protein Interaction Network Analysis for a Biomarker Panel Related to Human Esophageal AdenocarcinomaAsian Pacific journal of cancer prevention : APJCP · 2017Article
- A study of the Immune Epitope Database for some fungi species using network topological indices.Molecular diversity · 2017Article
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Corrections and comments
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Authors and funding
8 authors at 5 institutions in 3 countries.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.