title: Computational prediction of lysine acetylation proteome-wide.
abstract: Several studies have contributed to our knowledge of the enzymology underlying acetylation, including focused efforts to understand the molecular mechanism of substrate recognition by several acetyltransferases; however, conventional experiments to determine intrinsic features of substrate site specificity have proven challenging. In this chapter, I describe in detail a computational method that involves clustering analysis of protein sequences to predict protein acetylation based on the sequence characteristics of acetylated lysines within histones. This method illustrates that sequence composition has predictive power on datasets of acetylation marks, and can be used to predict other posttranslational modifications such as methylation and phosphorylation. Later in this chapter, other recent methods to predict lysine acetylation are described and together, these approaches combined with more traditional experimental methods, can be useful for identifying acetylated substrates proteome-wide.
authors: Broad Institute of MIT and Harvard, Cambridge, MA, USA.
authors: Amrita
authors: A
authors: Basu
mesh_terms: D000107:Acetylation; D000595:Amino Acid Sequence; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D006657:Histones / Q000737:chemistry* / Q000378:metabolism*; D006801:Humans; D008239:Lysine / Q000737:chemistry / Q000378:metabolism*; D011237:Predictive Value of Tests; D011499:Protein Processing, Post-Translational; D020543:Proteome / Q000378:metabolism*; D013379:Substrate Specificity
chemical_list: D006657:Histones; D020543:Proteome; D008239:Lysine