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Prokaryotic ubiquitin-like protein (Pup) is the first identified prokaryotic protein that is functionally analogous to ubiquitin. Recent studies have shed light on the Pup activation and conjugation to target proteins to be a signal for the selective degradation proteins in Mycobacterium tuberculosis (Mtb). By covalently conjugating the Pup, pupylation functions as a critical post-translational modification (PTM) conserved in actinomycetes. Detecting pupylation sites is crucial and fundamental for understanding the molecular mechanisms of Pup. Yet comparative studies with other PTM suggest that the development of accurate and complete repertories of pupylation is still in its early stages. Unbiased screening for pupylation sites by experimental methods is time consuming and expensive; in silico prediction can provide highly potential candidates and reduce the number of potential candidates that require further in vivo or in vitro confirmation. Here, we present an effective classifier of PupPred for predicting pupylation sites, which shows better performance than existing classifiers. Importantly, this work not only investigates the sequential, structural and evolutionary hallmarks around pupylation sites but also compares the differences of pupylation and ubiquitylation from the environmental, conservative and functional characterization of substrates. These prediction and analysis results may be helpful for further experimental investigation of degradation proteins in prokaryotes. Finally, the PupPred server is available at http://bioinfo.ncu.edu.cn/PupPred.aspx. [ABSTRACT FROM AUTHOR]

Protein methylation is predominantly found on lysine and arginine residues, and carries many important biological functions, including gene regulation and signal transduction. Given their important involvement in gene expression, protein methylation and their regulatory enzymes are implicated in a variety of human disease states such as cancer, coronary heart disease and neurodegenerative disorders. Thus, identification of methylation sites can be very helpful for the drug designs of various related diseases. In this study, we developed a method called PMeS to improve the prediction of protein methylation sites based on an enhanced feature encoding scheme and support vector machine. The enhanced feature encoding scheme was composed of the sparse property coding, normalized van der Waals volume, position weight amino acid composition and accessible surface area. The PMeS achieved a promising performance with a sensitivity of 92.45%, a specificity of 93.18%, an accuracy of 92.82% and a Matthew's correlation coefficient of 85.69% for arginine as well as a sensitivity of 84.38%, a specificity of 93.94%, an accuracy of 89.16% and a Matthew's correlation coefficient of 78.68% for lysine in 10-fold cross validation. Compared with other existing methods, the PMeS provides better predictive performance and greater robustness. It can be anticipated that the PMeS might be useful to guide future experiments needed to identify potential methylation sites in proteins of interest. The online service is available at http://bioinfo.ncu.edu.cn/inquiries_PMeS.aspx.

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.