The Nobel Prize in Chemistry 2004 was award to Aaron Ciechanover, Avram Hershko and Irwin Rose for their discovery of ubiquitin-mediated protein degradation. (Vogel, G. et al., 2004). Numerous subsequent studies showed that the selective degradation by ubiquitination provided a critical mechanism in eukaryotes to regulate the cellular processes such as cell cycle and division, immune response and inflammation and signal transduction. Recently, prokaryotic ubiquitin-like protein (PUP) was identified as the tagging system in prokaryotes (Pearce, M. J. et al., 2008), which was coupled to its targets through deamidation by dop (PUP deamidase/depupylase) and following conjugation catalyzed by PafA (PUP--protein ligase) (Striebel, F. et al., 2009). Although the detail of pup-proteasome system needs further characterization, the discovery of degradation mechanism opens the door to investigate the dynamic protein regulation in Mycobacterium, which could be targeted by pathogen-specific drugs. (Salgame, P. et al., 2008). In this regards, experimental identification of pupylated substrates with their sites could provide fundamental insights to understanding the cellular processes in Mycobacterium.

After Pearce et al. opened up research on pupylation with the first identified site in FabD (Malonyl CoA-acyl carrier protein transacylase) (Pearce, M. J. et al., 2008), Festa and colleagues firstly expanded the mycobacterial "pupylome" with 50 substrates with 60 pupylation sites in Mycobacterium tuberculosis (Festa, R. A. et al., 2010). Furthermore, proteome-wide discoveries of pupylation targets in model organism Mycobacterium smegmatis by Watrous et al. and Poulsen et al. provided the overall properties of PUP and the selective degradation mediated by pupylation was found to be dynamic (Watrous, J. et al., 2010; Poulsen, C. et al., 2010). Since it is labour-intensive and time-consuming to experimentally identify pupylation sites, computational prediction could be an alternative and promising approach for its convenience.

In this work, we manually collected 127 experimentally indentified protein pupylation sites in 109 unique proteins from scientific literature. A previously self-developed GPS (Group-based Prediction System) algorithm was employed with great improvement. We calculated the leave-one-out validation and 4-, 6-, 8-, 10-fold cross-validations to evaluate the prediction performance and system robustness. The leave-one-out validation result is accuracy (Ac) of 78.85%, sensitivity (Sn) of 63.78%, and specificity (Sp) of 80.21%. The online service and stand-alone packages of GPS-PUP 1.0 were implemented in JAVA 1.4.2 and freely available at: http://pup.biocuckoo.org/.


GPS-PUP 1.0 User Interface

For publication of results please cite the following article:

 GPS-PUP: Computational prediction of pupylation sites
 Zexian Liu, Qian Ma, Jun Cao, Xinjiao Gao, Jian Ren and Yu Xue.
 Molecular BioSystems, 2011, 7(10): 2737-2740.

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