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INTRODUCTION:
The 1998 Nobel
Prize in Physiology or Medicine was awarded for seminal
discoveries that nitric
oxide (NO) is a freely-diffusible
signalling molecule and second messenger, regulates the
production of cyclic GMP (cGMP), and plays essential roles
in the cardiovascular system. Later, flood of studies
challenged this fundamental view by observing that NO
could spatially and temporally target specific cysteine
thiols and transition metals of proteins, a reversibly
post-translational modification (PTM) termed as S-nitrosylation
(Foster,
et al., 2009; Foster,
et al., 2003; Hess,
et al., 2005; Hess,
et al., 2001; Stamler,
et al., 2001; Tannenbaum
and White, 2006). Although enzymatic mechanisms of
protein S-nitrosylation were still elusive, several
enzymes were proven to facilitate S-nitrosylation
or de-nitrosylation reactions. For example, Cu, Zn superoxide
dismutase (SOD) and thioredoxin (TRX) could promote S-nitrosylation,
while protein disulfide isomerase (PDI) might regulate
de-nitrosylation (Hess,
et al., 2005; Tannenbaum
and White, 2006). Current progresses proposed that
S-nitrosylation could modulate proteins' stabilities
(Li,
et al., 2007), activities (Tsang,
et al., 2009) and trafficking (Hernlund,
et al., 2009; Ozawa,
et al., 2008), and play important roles in
a variety of biological processes, including transcriptional
regulation (Li,
et al., 2007), cell signalling (Whalen,
et al., 2007), apoptosis (Tsang,
et al., 2009), chromatin remodeling (Nott,
et al., 2008) and so on. Moreover, aberrant
S-nitrosylation has been implicated in numerous
diseases and cancers (Foster,
et al., 2009; Foster,
et al., 2003; Tsang,
et al., 2009). In this regard, experimental
identification of S-nitrosylated proteins with
their sites will be a foundation of understanding the
molecular mechanisms and regulatory roles of S-nitrosylation.
In this work,
we manually collected 467
experimentally verified S-nitrosylation sites
in 302 unique
proteins from scientific literature. Previously, we developed
an algorithm of GPS
2.0 (Group-based Prediction System)
for prediction of kinase-specific phosphorylation sites
(Xue,
et al., 2008). Here, we greatly improved
the method and released GPS
3.0 algorithm. Then we developed a novel
computational software of GPS-SNO
1.0 for prediction of S-nitrosylation
sites. The leave-one-out validation and 4-, 6-, 8-, 10-fold
cross-validations were calculated to evaluate the prediction
performance and system robustness. By comparison, the
performance of GPS 3.0 algorithm was much better than
several other approaches, with an accuracy of 75.70%,
a sensitivity of 55.32% and a specificity of 80.11% under
the low threshold. As applications of GPS-SNO 1.0, we
also collected 485 potentially S-nitrosylated
substrates from PubMed. These proteins were detected from
large-scale or small-scale studies, while the exact S-nitrosylation
sites were still not experimentally determined. Successfully,
we predicted 371 (~76%) of these targets with at lease
one potential S-nitrosylation site. These prediction
results might be a useful reservoir for further experimental
verification. Finally, the online service and local packages
of GPS-SNO 1.0 were implemented in JAVA 1.4.2 and freely
available at: http://sno.biocuckoo.org/.
GPS-SNO
1.0 User Interface
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