Dataset Information

DeepMito: accurate prediction of protein sub-mitochondrial localization using convolutional neural networks.

ABSTRACT:

Motivation

The correct localization of proteins in cell compartments is a key issue for their function. Particularly, mitochondrial proteins are physiologically active in different compartments and their aberrant localization contributes to the pathogenesis of human mitochondrial pathologies. Many computational methods exist to assign protein sequences to subcellular compartments such as nucleus, cytoplasm and organelles. However, a substantial lack of experimental evidence in public sequence databases hampered so far a finer grain discrimination, including also intra-organelle compartments.

Results

We describe DeepMito, a novel method for predicting protein sub-mitochondrial cellular localization. Taking advantage of powerful deep-learning approaches, such as convolutional neural networks, our method is able to achieve very high prediction performances when discriminating among four different mitochondrial compartments (matrix, outer, inner and intermembrane regions). The method is trained and tested in cross-validation on a newly generated, high-quality dataset comprising 424 mitochondrial proteins with experimental evidence for sub-organelle localizations. We benchmark DeepMito towards the only one recent approach developed for the same task. Results indicate that DeepMito performances are superior. Finally, genomic-scale prediction on a highly-curated dataset of human mitochondrial proteins further confirms the effectiveness of our approach and suggests that DeepMito is a good candidate for genome-scale annotation of mitochondrial protein subcellular localization.

Availability and implementation

The DeepMito web server as well as all datasets used in this study are available at http://busca.biocomp.unibo.it/deepmito. A standalone version of DeepMito is available on DockerHub at https://hub.docker.com/r/bolognabiocomp/deepmito. DeepMito source code is available on GitHub at https://github.com/BolognaBiocomp/deepmito.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Savojardo C

PROVIDER: S-EPMC6956790 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

DeepMito: accurate prediction of protein sub-mitochondrial localization using convolutional neural networks.

Savojardo Castrense C Bruciaferri Niccolò N Tartari Giacomo G Martelli Pier Luigi PL Casadio Rita R

Bioinformatics (Oxford, England) 20200101 1

<h4>Motivation</h4>The correct localization of proteins in cell compartments is a key issue for their function. Particularly, mitochondrial proteins are physiologically active in different compartments and their aberrant localization contributes to the pathogenesis of human mitochondrial pathologies. Many computational methods exist to assign protein sequences to subcellular compartments such as nucleus, cytoplasm and organelles. However, a substantial lack of experimental evidence in public seq ...[more]

PMID: 31218353

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Dataset Information

DeepMito: accurate prediction of protein sub-mitochondrial localization using convolutional neural networks.

Motivation

Results

Availability and implementation

Supplementary information

Publications

DeepMito: accurate prediction of protein sub-mitochondrial localization using convolutional neural networks.

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