Protein succinylation,hepatic metabolism,and liver diseases

Succinylation is a highly conserved post-translational modication that is processed via enzymatic and non-enzymatic mechanisms.Succinylation exhibits strong effects on protein stability,enzyme activity,and transcriptional regulation.Protein succinylation is extensively present in the liver,and increasing evidence has demonstrated that succinylation is closely related to hepatic metabolism.For instance,histone acetyltransferase 1 promotes liver glycolysis,and the sirtuin 5-induced desuccinylation is involved in the regulation of the hepatic urea cycle and lipid metabolism.Therefore,the effects of succinylation on hepatic glucose,amino acid,and lipid metabolism under the action of various enzymes will be discussed in this work.In addition,how succinylases regulate the progression of different liver diseases will be reviewed,including the desuccinylation activity of sirtuin 7,which is closely associated with fatty liver disease and hepatitis,and the actions of lysine acetyltransferase 2A and histone acetyltransferase 1 that act as succinyltransferases to regulate the succinylation of target genes that influence the development of hepatocellular carcinoma.In view of the diversity and significance of protein succinylation,targeting the succinylation pathway may serve as an attractive direction for the treatment of liver diseases.

SuccSite:Incorporating Amino Acid Composition and Informative k-spaced Amino Acid Pairs to Identify Protein Succinylation Sites

Protein succinylation is a biochemical reaction in which a succinyl group(-CO-CH2-CH2-CO-)is attached to the lysine residue of a protein molecule.Lysine succinylation plays important regulatory roles in living cells.However,studies in this field are limited by the difficulty in experimentally identifying the substrate site specificity of lysine succinylation.To facilitate this process,several tools have been proposed for the computational identification of succinylated lysine sites.In this study,we developed an approach to investigate the substrate specificity of lysine succinylated sites based on amino acid composition.Using experimentally verified lysine succinylated sites collected from public resources,the significant differences in position-specific amino acid composition between succinylated and non-succinylated sites were represented using the Two Sample Logo program.These findings enabled the adoption of an effective machine learning method,support vector machine,to train a predictive model with not only the amino acid composition,but also the composition of k-spaced amino acid pairs.After the selection of the best model using a ten-fold crossvalidation approach,the selected model significantly outperformed existing tools based on an independent dataset manually extracted from published research articles.Finally,the selected model was used to develop a web-based tool,SuccSite,to aid the study of protein succinylation.Two proteins were used as case studies on the website to demonstrate the effective prediction of succinylation sites.We will regularly update SuccSite by integrating more experimental datasets.SuccSite is freely accessible at http://csb.cse.yzu.edu.tw/SuccSite/.