Large-scale prediction and analysis of protein sub-mitochondrial localization with DeepMito
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RESEARCH
Open Access
Large-scale prediction and analysis of protein sub-mitochondrial localization with DeepMito Castrense Savojardo1 , Pier Luigi Martelli1*, Giacomo Tartari1,2 and Rita Casadio1,2 From Annual Meeting of the Bioinformatics Italian Society (BITS 2019) Palermo, Italy. 26-28 June 2019
* Correspondence: pierluigi. [email protected] 1 Department of Pharmacy and Biotechnology (FaBiT), Biocomputing Group, University of Bologna, Bologna, Italy Full list of author information is available at the end of the article
Abstract Background: The prediction of protein subcellular localization is a key step of the big effort towards protein functional annotation. Many computational methods exist to identify high-level protein subcellular compartments such as nucleus, cytoplasm or organelles. However, many organelles, like mitochondria, have their own internal compartmentalization. Knowing the precise location of a protein inside mitochondria is crucial for its accurate functional characterization. We recently developed DeepMito, a new method based on a 1-Dimensional Convolutional Neural Network (1D-CNN) architecture outperforming other similar approaches available in literature. Results: Here, we explore the adoption of DeepMito for the large-scale annotation of four sub-mitochondrial localizations on mitochondrial proteomes of five different species, including human, mouse, fly, yeast and Arabidopsis thaliana. A significant fraction of the proteins from these organisms lacked experimental information about sub-mitochondrial localization. We adopted DeepMito to fill the gap, providing complete characterization of protein localization at sub-mitochondrial level for each protein of the five proteomes. Moreover, we identified novel mitochondrial proteins fishing on the set of proteins lacking any subcellular localization annotation using available state-of-the-art subcellular localization predictors. We finally performed additional functional characterization of proteins predicted by DeepMito as localized into the four different sub-mitochondrial compartments using both available experimental and predicted GO terms. All data generated in this study were collected into a database called DeepMitoDB (available at http://busca.biocomp.unibo.it/deepmitodb), providing complete functional characterization of 4307 mitochondrial proteins from the five species. (Continued on next page)
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