Position-specific analysis and prediction for protein lysine acetylation based on multiple features

• Published in: PloS one 2012-11, Vol.7 (11), p.e49108-e49108
• Main Authors: Suo, Sheng-Bao, Qiu, Jian-Ding, Shi, Shao-Ping, Sun, Xing-Yu, Huang, Shu-Yun, Chen, Xiang, Liang, Ru-Ping
• Format: Article
• Language: English
• Subjects: Acetates; Acetylation; Amino acid composition; Amino Acid Sequence; Amino acids; Apoptosis; Binding Sites; Biochemistry; Bioinformatics; Biological activity; Biology; Chemical Phenomena; Chemistry; Chromatin; Computational Biology - methods; Computer Science; Conserved Sequence; Correlation coefficient; Correlation coefficients; Entropy; Evolution, Molecular; Gene regulation; Kinases; Lysine; Lysine - metabolism; Metabolism; Methods; Model accuracy; Molecular modelling; Physicochemical properties; Physics; Post-translation; Post-translational modifications; Protein Processing, Post-Translational; Proteins; Proteins - chemistry; Proteins - metabolism; Residues; Signaling; Substrates; Support vector machines; Transcription; User-Computer Interface
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0049108

Protein lysine acetylation is a type of reversible post-translational modification that plays a vital role in many cellular processes, such as transcriptional regulation, apoptosis and cytokine signaling. To fully decipher the molecular mechanisms of acetylation-related biological processes, an initial but crucial step is the recognition of acetylated substrates and the corresponding acetylation sites. In this study, we developed a position-specific method named PSKAcePred for lysine acetylation prediction based on support vector machines. The residues around the acetylation sites were selected or excluded based on their entropy values. We incorporated features of amino acid composition information, evolutionary similarity and physicochemical properties to predict lysine acetylation sites. The prediction model achieved an accuracy of 79.84% and a Matthews correlation coefficient of 59.72% using the 10-fold cross-validation on balanced positive and negative samples. A feature analysis showed that all features applied in this method contributed to the acetylation process. A position-specific analysis showed that the features derived from the critical neighboring residues contributed profoundly to the acetylation site determination. The detailed analysis in this paper can help us to understand more of the acetylation mechanism and can provide guidance for the related experimental validation.