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Sci Rep. 2014 Jul 21;4:5765. doi: 10.1038/srep05765.

Accurate in silico identification of species-specific acetylation sites by integrating protein sequence-derived and functional features.

Author information

1
National Engineering Laboratory for Industrial Enzymes and Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.
2
Department of Biochemistry and Molecular Biology, Faculty of Medicine, Monash University, Melbourne, Victoria 3800, Australia.
3
State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China.
4
Faculty of Information Technology, Monash University, Melbourne, Victoria 3800, Australia.
5
1] National Engineering Laboratory for Industrial Enzymes and Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China [2] Department of Biochemistry and Molecular Biology, Faculty of Medicine, Monash University, Melbourne, Victoria 3800, Australia [3] Faculty of Information Technology, Monash University, Melbourne, Victoria 3800, Australia.

Abstract

Lysine acetylation is a reversible post-translational modification, playing an important role in cytokine signaling, transcriptional regulation, and apoptosis. To fully understand acetylation mechanisms, identification of substrates and specific acetylation sites is crucial. Experimental identification is often time-consuming and expensive. Alternative bioinformatics methods are cost-effective and can be used in a high-throughput manner to generate relatively precise predictions. Here we develop a method termed as SSPKA for species-specific lysine acetylation prediction, using random forest classifiers that combine sequence-derived and functional features with two-step feature selection. Feature importance analysis indicates functional features, applied for lysine acetylation site prediction for the first time, significantly improve the predictive performance. We apply the SSPKA model to screen the entire human proteome and identify many high-confidence putative substrates that are not previously identified. The results along with the implemented Java tool, serve as useful resources to elucidate the mechanism of lysine acetylation and facilitate hypothesis-driven experimental design and validation.

PMID:
25042424
PMCID:
PMC4104576
DOI:
10.1038/srep05765
[Indexed for MEDLINE]
Free PMC Article

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