Abstract
Small ubiquitin-like modifiers (SUMOs) regulate a variety of cellular processes through two distinct mechanisms, including covalent sumoylation and non-covalent SUMO interaction. The complexity of SUMO regulations has greatly hampered the large-scale identification of SUMO substrates or interaction partners on a proteome-wide level. In this work, we developed a new tool called GPS-SUMO for the prediction of both sumoylation sites and SUMO-interaction motifs (SIMs) in proteins. To obtain an accurate performance, a new generation group-based prediction system (GPS) algorithm integrated with Particle Swarm Optimization approach was applied. By critical evaluation and comparison, GPS-SUMO was demonstrated to be substantially superior against other existing tools and methods. With the help of GPS-SUMO, it is now possible to further investigate the relationship between sumoylation and SUMO interaction processes. A web service of GPS-SUMO was implemented in PHP + JavaScript and freely available at http://sumosp.biocuckoo.org.
Highlights
By covalently modifying specific lysine residues in protein substrates, or by non-covalently interacting with proteins, small ubiquitin-like modifiers (SUMOs) play an essential role in the regulation of a variety of biological processes, including gene expression, DNA repair, chromosome assembly, and cellular signaling [1,2,3,4]
With a new generation group-based prediction system (GPS) algorithm integrated with PSO method, the sumoylation site prediction performance was greatly improved
By integrating a core hydrophobic motif of [IVL]{3,5}, GPS-SUMO achieves a precise prediction on SUMO-interaction motifs (SIMs)
Summary
By covalently modifying specific lysine residues in protein substrates, or by non-covalently interacting with proteins, small ubiquitin-like modifiers (SUMOs) play an essential role in the regulation of a variety of biological processes, including gene expression, DNA repair, chromosome assembly, and cellular signaling [1,2,3,4]. 983 sumoylation sites in 545 proteins and 137 known SIMs in 80 proteins as the non-redundant data sets, respectively. 912 sumoylation sites in 510 protein (Supplementary Table S1) and 137 SIMs in 80 proteins (Supplementary Table S2) were retained as the non-redundant training data sets.
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