Predicting subcellular localization of proteins using protein-protein interaction data

Garapati, Hita Sony; Male, Gurranna; Mishra, Krishnaveni

doi:10.1016/j.ygeno.2020.01.007

Cited by 13 publications

(7 citation statements)

References 33 publications

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“…As a result, the mitochondrial of six proteins, Ybl55c, Ybr200w-a, Ycr095w-a, Ydr246w-a, Yjl213w, and Ylr415c, had been confirmed. In addition, the mitochondrial localization of Yar035c-a and Ydr124w was newly confirmed after our analysis (Garapati et al, 2020;Vakirlis et al, 2020). Thus, 8 of the 35 UPMs that we identified in this study have also been confirmed in other studies to have mitochondrial localization.…”

Section: Characteristics Of Upmssupporting

confidence: 85%

Identification of uncharacterized proteins potentially localized to mitochondria (UPMs) in Saccharomyces cerevisiae using a fluorescent protein unstable in the cytoplasm

Horiuchi

Namba

Saeki

et al. 2021

Yeast

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Eukaryotic cells are composed of organelles, and each organelle contains proteins that play a role in its function. Therefore, the localization of a protein, especially to organelles, is a clue to infer the function of that protein. In this study, we attempted to identify novel mitochondrially localized proteins in the budding yeast Saccharomyces cerevisiae using a fluorescent protein (GFPdeg) that is rapidly degraded in the cytoplasm. Of the budding yeast proteins predicted to localize to mitochondria by the prediction tool Deeploc-1.0, those with known mitochondrial localization or functional relevance were eliminated, and 95 proteins of unknown function were selected as candidates for analysis. By forced expression of GFPdeg fusion proteins with these proteins and observation of their localization, we identified 35 uncharacterized proteins potentially localized to mitochondria (UPMs) including 8 previously identified proteins that localize to mitochondria. Most of these had no N-terminal mitochondrial localization signal and were evolutionarily young "emerging genes" that exist only in S. cerevisiae. Some of these genes were found to be upregulated during the postdiauxic shift phase when mitochondria are being developed, suggesting that they are actually involved in some mitochondrial function.

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Section: Characteristics Of Upmssupporting

confidence: 85%

Identification of uncharacterized proteins potentially localized to mitochondria (UPMs) in Saccharomyces cerevisiae using a fluorescent protein unstable in the cytoplasm

Horiuchi

Namba

Saeki

et al. 2021

Yeast

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show abstract

“…Some proteins are also distributed to multiple cellular locations, depending on the sorting signal ( Briesemeister et al, 2010 ). The subcellular localization is also known to be statistically correlated with the protein function, and its gene expression levels ( Garapati et al, 2020 ). A significant number of novel proteins have been identified using sequencing technologies, but their subcellular location remains unknown.…”

Section: Resultsmentioning

confidence: 99%

Deciphering the Crosstalk Mechanisms of Wheat-Stem Rust Pathosystem: Genome-Scale Prediction Unravels Novel Host Targets

Kataria

Kaundal

2022

Front. Plant Sci.

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Triticum aestivum (wheat), a major staple food grain, is affected by various biotic stresses. Among these, fungal diseases cause about 15–20% of yield loss, worldwide. In this study, we performed a comparative analysis of protein-protein interactions between two Puccinia graminis races (Pgt 21-0 and Pgt Ug99) that cause stem (black) rust in wheat. The available molecular techniques to study the host-pathogen interaction mechanisms are expensive and labor-intensive. We implemented two computational approaches (interolog and domain-based) for the prediction of PPIs and performed various functional analysis to determine the significant differences between the two pathogen races. The analysis revealed that T. aestivum-Pgt 21-0 and T. aestivum-Pgt Ug99 interactomes consisted of ∼90M and ∼56M putative PPIs, respectively. In the predicted PPIs, we identified 115 Pgt 21-0 and 34 Pgt Ug99 potential effectors that were highly involved in pathogen virulence and development. Functional enrichment analysis of the host proteins revealed significant GO terms and KEGG pathways such as O-methyltransferase activity (GO:0008171), regulation of signal transduction (GO:0009966), lignin metabolic process (GO:0009808), plastid envelope (GO:0009526), plant-pathogen interaction pathway (ko04626), and MAPK pathway (ko04016) that are actively involved in plant defense and immune signaling against the biotic stresses. Subcellular localization analysis anticipated the host plastid as a primary target for pathogen attack. The highly connected host hubs in the protein interaction network belonged to protein kinase domain including Ser/Thr protein kinase, MAPK, and cyclin-dependent kinase. We also identified 5,577 transcription factors in the interactions, associated with plant defense during biotic stress conditions. Additionally, novel host targets that are resistant to stem rust disease were also identified. The present study elucidates the functional differences between Pgt 21-0 and Pgt Ug99, thus providing the researchers with strain-specific information for further experimental validation of the interactions, and the development of durable, disease-resistant crop lines.

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“…After conversion, a k-NN classifier is used for prediction. An idea similar to k-NN is used in [104] , where a physical interaction network was obtained from BioGRID [73] , and GO Cellular Component annotation was mapped onto the network, if available, for the corresponding protein (node). For a query protein, the percentage of its interactors associated with each target localization is calculated.…”

Section: Classification Algorithmsmentioning

confidence: 99%

Computational methods for protein localization prediction

Jiang

Wang

et al. 2021

Computational and Structural Biotechnology Journal

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The accurate annotation of protein localization is crucial in understanding protein function in tandem with a broad range of applications such as pathological analysis and drug design. Since most proteins do not have experimentally-determined localization information, the computational prediction of protein localization has been an active research area for more than two decades. In particular, recent machine-learning advancements have fueled the development of new methods in protein localization prediction. In this review paper, we first categorize the main features and algorithms used for protein localization prediction. Then, we summarize a list of protein localization prediction tools in terms of their coverage, characteristics, and accessibility to help users find suitable tools based on their needs. Next, we evaluate some of these tools on a benchmark dataset. Finally, we provide an outlook on the future exploration of protein localization methods.

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Predicting subcellular localization of proteins using protein-protein interaction data

Cited by 13 publications

References 33 publications

Identification of uncharacterized proteins potentially localized to mitochondria (UPMs) in Saccharomyces cerevisiae using a fluorescent protein unstable in the cytoplasm

Identification of uncharacterized proteins potentially localized to mitochondria (UPMs) in Saccharomyces cerevisiae using a fluorescent protein unstable in the cytoplasm

Deciphering the Crosstalk Mechanisms of Wheat-Stem Rust Pathosystem: Genome-Scale Prediction Unravels Novel Host Targets

Computational methods for protein localization prediction

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