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 predicti

SUBMITTER: Li Y 

PROVIDER: S-EPMC4104576 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature