Dataset Information

Quokka: a comprehensive tool for rapid and accurate prediction of kinase family-specific phosphorylation sites in the human proteome.

ABSTRACT:

Motivation

Kinase-regulated phosphorylation is a ubiquitous type of post-translational modification (PTM) in both eukaryotic and prokaryotic cells. Phosphorylation plays fundamental roles in many signalling pathways and biological processes, such as protein degradation and protein-protein interactions. Experimental studies have revealed that signalling defects caused by aberrant phosphorylation are highly associated with a variety of human diseases, especially cancers. In light of this, a number of computational methods aiming to accurately predict protein kinase family-specific or kinase-specific phosphorylation sites have been established, thereby facilitating phosphoproteomic data analysis.

Results

In this work, we present Quokka, a novel bioinformatics tool that allows users to rapidly and accurately identify human kinase family-regulated phosphorylation sites. Quokka was developed by using a variety of sequence scoring functions combined with an optimized logistic regression algorithm. We evaluated Quokka based on well-prepared up-to-date benchmark and independent test datasets, curated from the Phospho.ELM and UniProt databases, respectively. The independent test demonstrates that Quokka improves the prediction performance compared with state-of-the-art computational tools for phosphorylation prediction. In summary, our tool provides users with high-quality predicted human phosphorylation sites for hypothesis generation and biological validation.

Availability and implementation

The Quokka webserver and datasets are freely available at http://quokka.erc.monash.edu/.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Li F

PROVIDER: S-EPMC6289136 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Publications

Quokka: a comprehensive tool for rapid and accurate prediction of kinase family-specific phosphorylation sites in the human proteome.

Li Fuyi F Li Chen C Marquez-Lago Tatiana T TT Leier André A Akutsu Tatsuya T Purcell Anthony W AW Ian Smith A A Lithgow Trevor T Daly Roger J RJ Song Jiangning J Chou Kuo-Chen KC

Bioinformatics (Oxford, England) 20181201 24

<h4>Motivation</h4>Kinase-regulated phosphorylation is a ubiquitous type of post-translational modification (PTM) in both eukaryotic and prokaryotic cells. Phosphorylation plays fundamental roles in many signalling pathways and biological processes, such as protein degradation and protein-protein interactions. Experimental studies have revealed that signalling defects caused by aberrant phosphorylation are highly associated with a variety of human diseases, especially cancers. In light of this, ...[more]

PMID: 29947803

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Project description:Reversible protein phosphorylation is one of the most pervasive post-translational modifications, regulating diverse cellular processes in various organisms. High throughput experimental studies using mass spectrometry have identified many phosphorylation sites, primarily from eukaryotes. However, the vast majority of phosphorylation sites remain undiscovered, even in well studied systems. Because mass spectrometry-based experimental approaches for identifying phosphorylation events are costly, time-consuming, and biased toward abundant proteins and proteotypic peptides, in silico prediction of phosphorylation sites is potentially a useful alternative strategy for whole proteome annotation. Because of various limitations, current phosphorylation site prediction tools were not well designed for comprehensive assessment of proteomes. Here, we present a novel software tool, Musite, specifically designed for large scale predictions of both general and kinase-specific phosphorylation sites. We collected phosphoproteomics data in multiple organisms from several reliable sources and used them to train prediction models by a comprehensive machine-learning approach that integrates local sequence similarities to known phosphorylation sites, protein disorder scores, and amino acid frequencies. Application of Musite on several proteomes yielded tens of thousands of phosphorylation site predictions at a high stringency level. Cross-validation tests show that Musite achieves some improvement over existing tools in predicting general phosphorylation sites, and it is at least comparable with those for predicting kinase-specific phosphorylation sites. In Musite V1.0, we have trained general prediction models for six organisms and kinase-specific prediction models for 13 kinases or kinase families. Although the current pretrained models were not correlated with any particular cellular conditions, Musite provides a unique functionality for training customized prediction models (including condition-specific models) from users' own data. In addition, with its easily extensible open source application programming interface, Musite is aimed at being an open platform for community-based development of machine learning-based phosphorylation site prediction applications. Musite is available at http://musite.sourceforge.net/.

Dataset Information

Quokka: a comprehensive tool for rapid and accurate prediction of kinase family-specific phosphorylation sites in the human proteome.

Motivation

Results

Availability and implementation

Supplementary information

Publications

Quokka: a comprehensive tool for rapid and accurate prediction of kinase family-specific phosphorylation sites in the human proteome.

Similar Datasets

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