As a special class of post-translational modifications (PTMs), numerous proteins could be covalently modified by a variety of lipids, including myristate (C14), palmitate (C16), farnesyl (C15), geranylgeranyl (C20) and glycosylphosphatidylinositol (GPI), etc (Casey, 1995; Nadolski and Linder, 2007; Resh, 2006). Although most of lipid modifications are irreversible, protein S-palmitoylation, also called as thioacylation or S-acylation, could reversibly attach 16-carbon saturated fatty acids to specific cysteine residues in protein substrates through thioester linkages (Bijlmakers and Marsh, 2003; Dietrich and Ungermann, 2004; el-Husseini Ael and Bredt, 2002; Greaves and Chamberlain, 2007; Linder and Deschenes, 2007; Nadolski and Linder, 2007; Resh, 2006; Resh, 2006; Roth, et al., 2006; Smotrys and Linder, 2004; Wan, et al., 2007). Palmitoylation will enhance the surface hydrophobicity and membrane affinity of protein substrates, and play important roles in modulating proteins' trafficking (Draper, et al., 2007; Linder and Deschenes, 2007), stability (Linder and Deschenes, 2007), and sorting (Greaves and Chamberlain, 2007), etc. Also, protein palmitoylation has been involved in numerous cellular processes, including signaling (Casey, 1995; Kurayoshi, et al., 2007; Resh, 2006), apoptosis (Chakrabandhu, et al., 2007; Feig, et al., 2007), and neuronal transmission (Roth, et al., 2006; Stowers and Isacoff, 2007), etc. Although many efforts have been made in this field, the molecular mechanism underlying protein palmitoylation still remain to be inexplicit.

During the past decade, we have developed a series of high-performance protein palmitoylation sites predictors. In 2006, CSS-Palm 1.0 was designed by Zhou et al. as the first program for palmitoylation site prediction (Zhou, et al., 2006). With a clustering and scoring strategy algorithm (CSS), the CSS-Palm 1.0 was able to accurately predict protein palmitoylation site. Later, Ren et al. updated the CSS-Palm 1.0 into CSS-Palm 2.0 by introducing a matrix mutation approach, which made considerable improvements over the previous version in terms of prediction capacity and efficiency (Ren, et al., 2008). In 2011, CSS-Palm 3.0 was released with application of Group-based Prediction System (GPS) algorithm (Xue, et al., 2011). In recent years, experimentally identified palmitoylation sites have been significantly expanded hence efforts should be exerted on up-grading the performance of palmitoylation site prediction. In 2013, CSS-Palm 4.0 was released, which include a fourth-generation of GPS algorithm and the latest training data set containing 583 palmitoylation sites from 277 distinct proteins. Notably, in the fourth-generation GPS algorithm, we integrated Particle Swarm Optimize (PSO) to improve the convergence speed and training accuracy. To evaluate the prediction performance and system robustness of CSS-Palm 4.0, the leave- one-out validation and 4-, 6-, 8-, 10-fold cross-validations were performed. By comparison with our previous versions, the performance of CSS-Palm 4.0 was greatly improved. Finally, the standalone version of CSS-Palm 4.0 was implemented in Java SE 6 with high speed. The CSS-Palm 4.0 could predict out potential palmitoylation sites for ~1,000 proteins (with an average length of ~1000aa) within two minutes. Also, with PHP and JavaScript, the online version was developed.Taken together, we proposed that the CSS-Palm 4.0 will be a great help for experimentalists. The CSS-Palm 4.0 is freely available at: http://csspalm.biocuckoo.org while the previous version is also provided.

This website is linked in ExPASy Proteomics Tools page.

The CSS-Palm paper is included in the 50 most frequently cited articles of PEDS


CSS-Palm 4.0 User Interface

For publication of results please cite the following article:

 CSS-Palm 2.0: an updated software for palmitoylation sites prediction
 Jian Ren, Longping Wen, Xinjiao Gao, Changjiang Jin, Yu Xue and Xuebiao Yao.
 Protein Engineering, Design and Selection.2008 21(11):639-644

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