Characterization and Prediction of Lysine (K)- Acetyl-Transferase Specific Acetylation Sites*□S

Lysine acetylation is a well-studied post-translational modification on both histone and nonhistone proteins. More than 2000 acetylated proteins and 4000 lysine acetylation sites have been identified by large scale mass spectrometry or traditional experimental methods. Although over 20 lysine (K)-acetyl-transferases (KATs) have been characterized, which KAT is responsible for a given protein or lysine site acetylation is mostly unknown. In this work, we collected KAT-specific acetylation sites manually and analyzed sequence features surrounding the acetylated lysine of substrates from three main KAT families (CBP/p300, GCN5/PCAF, and the MYST family). We found that each of the three KAT families acetylates lysines with different sequence features. Based on these differences, we developed a computer program, Acetylation Set Enrichment Based method to predict which KATfamilies are responsible for acetylation of a given protein or lysine site. Finally, we evaluated the efficiency of our method, and experimentally detected four proteins that were predicted to be acetylated by two KAT families when one representative member of the KAT family is over expressed. We conclude that our approach, combined with more traditional experimental methods, may be useful for identifying KAT families responsible for acetylated substrates proteome-wide. Molecular & Cellular Proteomics 11: 10.1074/mcp.M111. 011080, 1–9, 2012. In order to function properly, natural proteins suffer from various post-translational modifications, among which acetylation plays critical roles in protein stability, gene expression regulation, protein-protein interactions, and cellular metabolism (1–5). Acetylation occurs mainly on lysine residues and has been extensively studied on histone Nterminal lysine (6, 7). Further study shows that lysine residues of nonhistone proteins, such as p53 and ER can also be acetylated (8, 9). Although the number of nonhistone protein acetylation reports is rapidly expanding, it is difficult to detect the acetylation of all lysine residues in the whole proteome by traditional experiments because of the huge number of lysine residues. There are over 600,000 lysine residues in the human proteome according to UniProtKB/ Swiss-Prot (version 188). To find more acetylated proteins new technologies have been developed, such as immune-precipitation combined with mass spectrometric analysis. At least three groups successfully used this technology to get precise acetylation information from about two thousand proteins (3, 5, 10). Their research greatly increased the number of known acetylated proteins and expanded our knowledge of acetylation. However, the number of discovered acetylation sites is still small compared with the huge amount of lysine residues in the whole proteome. In addition, acetylation is a dynamic and condition-dependent process, as different cells and the same cells in different conditions have various acetylation profiles. Because it is impossible to determine the acetylation status in every cell and condition, researchers took full advantage of known information and developed in silico methods to predict acetylation. The most widely used method is the support vector machine method which can classify new sequences by learning the features of sequence contexts surrounding the acetylated lysines. Several acetylation predict tools were based on this method and each achieved a good performance (11–13). Besides support vector machine, other methods such as meta-analysis and sequence clustering have also been used to predict acetylation sites (14, 15). All these predictions provided valuable insights for further acetylation identification. These previous efforts, both mass spectrometry and in silico prediction, focus on the acetylation itself, and no methods give any information about which (K)-acetyl-transferases From the ‡Department of Biomedical Informatics, Peking University Health Science Center, Beijing 100191, China; §Institute of Systems Biomedicine, Peking University Health Science Center, Beijing 100191, China; ¶Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Biochemistry and Molecular Biology, Peking University Health Science Center, Beijing 100191, China; MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China; **College of Computer Science and Technology, Jilin University, Changchun 130012, China; ‡‡Advanced Computing Research Laboratory, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China; §§Graduate University of Chinese Academy of Sciences, Beijing 100049, China; ¶¶The Center for Life Science, Peking University, Beijing 100871, China Received May 14, 2011, and in revised form, August 13, 2011 Published, MCP Papers in Press, September 30, 2011, DOI 10.1074/mcp.M111. 011080 Research