Fuyi Li1, Chen Li1,2, Tatiana T Marquez-Lago3, André Leier3, Tatsuya Akutsu4, Anthony W Purcell1, A Ian Smith1,5, Trevor Lithgow6, Roger J Daly1, Jiangning Song1,7, Kuo-Chen Chou8. 1. Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Clayton, VIC, Australia. 2. Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland. 3. Department of Genetics, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA. 4. Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto, Japan. 5. ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, VIC, Australia. 6. Biomedicine Discovery Institute and Department of Microbiology, Monash University, Clayton, VIC, Australia. 7. Monash Centre for Data Science, Monash University, Clayton, VIC, Australia. 8. Gordon Life Science Institute, Boston, MA, USA.
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.
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.
Authors: Fuyi Li; Yanan Wang; Chen Li; Tatiana T Marquez-Lago; André Leier; Neil D Rawlings; Gholamreza Haffari; Jerico Revote; Tatsuya Akutsu; Kuo-Chen Chou; Anthony W Purcell; Robert N Pike; Geoffrey I Webb; A Ian Smith; Trevor Lithgow; Roger J Daly; James C Whisstock; Jiangning Song Journal: Brief Bioinform Date: 2019-11-27 Impact factor: 11.622
Authors: Shutao Mei; Fuyi Li; André Leier; Tatiana T Marquez-Lago; Kailin Giam; Nathan P Croft; Tatsuya Akutsu; A Ian Smith; Jian Li; Jamie Rossjohn; Anthony W Purcell; Jiangning Song Journal: Brief Bioinform Date: 2020-07-15 Impact factor: 11.622