1. ToxoNet: A high confidence map of protein-protein interactions in Toxoplasma gondii.
- Author
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Swapna LS, Stevens GC, Sardinha-Silva A, Hu LZ, Brand V, Fusca DD, Wan C, Xiong X, Boyle JP, Grigg ME, Emili A, and Parkinson J
- Subjects
- Computational Biology, Protein Interaction Mapping methods, Proteome metabolism, Databases, Protein, Machine Learning, Cluster Analysis, Toxoplasma metabolism, Protozoan Proteins metabolism, Protozoan Proteins chemistry, Protein Interaction Maps physiology
- Abstract
The apicomplexan intracellular parasite Toxoplasma gondii is a major food borne pathogen that is highly prevalent in the global population. The majority of the T. gondii proteome remains uncharacterized and the organization of proteins into complexes is unclear. To overcome this knowledge gap, we used a biochemical fractionation strategy to predict interactions by correlation profiling. To overcome the deficit of high-quality training data in non-model organisms, we complemented a supervised machine learning strategy, with an unsupervised approach, based on similarity network fusion. The resulting combined high confidence network, ToxoNet, comprises 2,063 interactions connecting 652 proteins. Clustering identifies 93 protein complexes. We identified clusters enriched in mitochondrial machinery that include previously uncharacterized proteins that likely represent novel adaptations to oxidative phosphorylation. Furthermore, complexes enriched in proteins localized to secretory organelles and the inner membrane complex, predict additional novel components representing novel targets for detailed functional characterization. We present ToxoNet as a publicly available resource with the expectation that it will help drive future hypotheses within the research community., Competing Interests: The authors have declared that no competing interests exist., (Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.)
- Published
- 2024
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