title: Accurate in silico identification of species-specific acetylation sites by integrating protein sequence-derived and functional features.
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.
authors.affiliation: 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.
authors.forename: Yuan
authors.initials: Y
authors.lastname: Li
authors.affiliation: 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.
authors.forename: Mingjun
authors.initials: M
authors.lastname: Wang
authors.affiliation: 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.
authors.forename: Huilin
authors.initials: H
authors.lastname: Wang
authors.affiliation: Department of Biochemistry and Molecular Biology, Faculty of Medicine, Monash University, Melbourne, Victoria 3800, Australia.
authors.forename: Hao
authors.initials: H
authors.lastname: Tan
authors.affiliation: State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China.
authors.forename: Ziding
authors.initials: Z
authors.lastname: Zhang
authors.affiliation: Faculty of Information Technology, Monash University, Melbourne, Victoria 3800, Australia.
authors.forename: Geoffrey I
authors.initials: GI
authors.lastname: Webb
authors.affiliation: 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.
authors.forename: Jiangning
authors.initials: J
authors.lastname: Song
mesh_terms: D000107:Acetylation; D000595:Amino Acid Sequence; D000818:Animals; D019540:Area Under Curve; D003198:Computer Simulation; D006801:Humans; D011499:Protein Processing, Post-Translational; D020543:Proteome / Q000737:chemistry* / Q000378:metabolism; D012372:ROC Curve; D020539:Sequence Analysis, Protein; D013045:Species Specificity
chemical_list: D020543:Proteome