ArticleBMC bioinformatics2018
Building protein-protein interaction networks for Leishmania species through protein structural information.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Recent developments of sequence-based prediction of protein-protein interactions.Biophysical reviews · 2022Review
- Gene-gene interaction analysis incorporating network information via a structured Bayesian approach.Statistics in medicine · 2021Article
- SystematicFrontiers in chemistry · 2021Article
- Perspectives From Systems Biology to Improve Knowledge ofFrontiers in cellular and infection microbiology · 2021Article
- Evolution of Sequence-based Bioinformatics Tools for Protein-protein Interaction Prediction.Current genomics · 2020Review
- Article
- Protein-protein interactions of the nicotinamide/nicotinate mononucleotide adenylyltransferase of Leishmania braziliensis.Memorias do Instituto Oswaldo Cruz · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundSystematic analysis of a parasite interactome is a key approach to understand different biological processes. It makes possible to elucidate disease mechanisms, to predict protein functions and to select promising targets for drug development. Currently, several approaches for protein interaction prediction for non-model species incorporate only small fractions of the entire proteomes and their interactions. Based on this perspective, this study presents an integration of computational methodologies, protein network predictions and comparative analysis of the protozoan species Leishmania braziliensis and Leishmania infantum. These parasites cause Leishmaniasis, a worldwide distributed and neglected disease, with limited treatment options using currently available drugs.
resultsThe predicted interactions were obtained from a meta-approach, applying rigid body docking tests and template-based docking on protein structures predicted by different comparative modeling techniques. In addition, we trained a machine-learning algorithm (Gradient Boosting) using docking information performed on a curated set of positive and negative protein interaction data. Our final model obtained an AUC = 0.88, with recall = 0.69, specificity = 0.88 and precision = 0.83. Using this approach, it was possible to confidently predict 681 protein structures and 6198 protein interactions for L. braziliensis, and 708 protein structures and 7391 protein interactions for L. infantum. The predicted networks were integrated to protein interaction data already available, analyzed using several topological features and used to classify proteins as essential for network stability.
conclusionsThe present study allowed to demonstrate the importance of integrating different methodologies of interaction prediction to increase the coverage of the protein interaction of the studied protocols, besides it made available protein structures and interactions not previously reported.
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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 Socratic graph.