GPS-SUMO: A tool for the prediction of sumoylation sites and SUMO-interaction motifs

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GPS-SUMO

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A tool for the prediction of sumoylation sites and SUMO-interaction motifs

ID：10003Uploader：赵齐

2022.01.01

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http://sumosp.biocuckoo.org/

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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.

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DOI：10.1093/nar/gku383

PMID：24880689

PMCID：PMC4086084

GPS-SUMO: a tool for the prediction of sumoylation sites and SUMO-interaction motifs

Qi Zhao,Yubin Xie,Yueyuan Zheng,Shuai Jiang,Wenzhong Liu,Weiping Mu,Zexian Liu,Yong Zhao,Yu Xue,Jian RenNucleic Acids Research. 2014

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Documentation

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 (Geiss-Friedlander and Melchior, 2007 ;Hay, 2005 ;Muller,et al., 2001 ;Seeler and Dejean, 2003). Along with the accumulating research on its biological functions, there are abundant evidences that the aberrance of SUMO regulation is highly associated with various diseases, such as neurodegenerative diseases (Lee,et al.,2013; Eckermann, 2013), congenital heart defects (Wang, et al., 2011), diabetes (Zhao, 2007) and cancers (Seeler, et al., 2007). Therefore, the identification of SUMOylation Sites and SUMO-interaction Motifs (SIMs) in proteins is fundamental for understanding the biological functions and regulatory mechanisms of SUMOs, and provides potential targets for further diagnostic and therapeutic consideration. The process of proteins being covalently modified by SUMOs is called as sumoylation, which is one of the most important and ubiquitous post-translational modifications (PTMs) of proteins (Gill, 2005; Melchior, 2000). Previously, experimental studies suggested that most of sumoylation sites follow a canonical consensus motif of ψ-K-X-E (ψ, a hydrophobic amino acid, such as A, I, L, M, P, F, V or W; X, any amino acid residue) (Rodriguez, et al., 2001; Sampson, et al., 2001). However, our collective experimental data shows that approximately 40% (400 out of 983 sites) of known sumoylation sites do not conform to the above motif. In this regard, the current understanding of sumoylation recognition is still inadequate. Recently, it was reported SUMOs can non-covalently interact with other proteins through targeting specific SIMs (Hannich, et al., 2005; Hecker, et al., 2006; Kerscher, et al., 2006). For example, the SUMO Interaction of Daxx modulates its sumoylation and is critical for targeting Daxx to PML oncogenic domains (PODs) for the transcriptional repression (Lin, et al., 2006). Also, the non-covalent interaction of SUMO-2 and CoREST1, but not the sumoylation, is essential for organizing the transcriptional corepressor complex of LSD1/CoREST1/HDAC (Ouyang, et al., 2009). Previously, a series of SIMs were experimentally identified (Hannich, et al., 2005; Hecker, et al., 2006; Ouyang, et al., 2009; Minty, et al., 2000; Song, et al., 2004; Song, et al., 2005; Vogt and Hofmann, 2012). Although nearly ten types of SIMs were experimentally identified, each one can only recall a small proportion of known SIMs, and no one can present a major profile for SIMs. Because of these complicated features, systematic analysis of sumoylation and SUMO interaction is still a great challenge. In contrast with labor-intensive and time-consuming experimental identifications, in silico prediction of sumoylation sites and SIMs in proteins can greatly narrow down the number of candidates, and generate helpful information for further verification. In this work, by improving the prediction algorithm and adding the novel SIMs prediction feature, we developed an updated version of SUMOsp and renamed it as GPS-SUMO. From the scientific literature, we manually collected 983 sumoylation sites in 545 proteins and 151 known SIMs in 80 proteins as the non-redundant data sets, respectively. Subsequently, the fourth-generation GPS algorithm integrated with the Particle Swarm Optimization (PSO) (Eberhart and Kennedy, 1995; Kennedy and Eberhart, 1995) method was employed for training and predicting. For convenience, a user-friendly web interface was developed using PHP + JavaScript, and is freely available at http://sumosp.biocuckoo.org/online.php. This website is linked in ExPASy Proteomics Tools page.

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