KinasePhos: a web tool for identifying protein kinase-specific phosphorylation sites

Huang, Hsien Da; Lee, Tzong-Yi; Tzeng, Shih Wei; Horng, Jorng-Tzong

doi:10.1093/nar/gki471

Cited by 297 publications

(261 citation statements)

References 9 publications

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“…We predicted a region with putative NLS in aa 143-159 using PSORT II system (http:// psort.hgc.jp/form2.html; Nakai and Horton, 1999), and a cluster of three potential phosphorylation sites (Ser137, Ser141 and Ser142) near the putative NLS using Disphos (http://www.ist.temple.edu/disphos; Iakoucheva et al, 2004), NetPhos (http://www.cbs.dtu.dk/services/NetPhos; Blom et al, 1999) and KinasePhos (http://kinasephos. mbc.nctu.edu.tw; Huang et al, 2005) programs. Based on these pieces of evidence, a series of SIP mutants (Figures 4a and b) were constructed and their effects on the cellular localization of SIP were examined.…”

Section: Resultsmentioning

confidence: 99%

“…In our study, both curcumin and PKC activator (PMA) cause SIP phosphorylation in MOLT-4 but not MOLT-4/AraG cells. Furthermore, NetPhosK (Blom et al, 2004) and KinasePhos (Huang et al, 2005) programs predict the Ser141 residue of SIP as the phosphorylation site of Figure 6 The Ser141Ala mutation attenuates the nuclear accumulation and the apoptosis-promoting effect of SIP in different cells treated with diverse apoptosis stimuli. The indicated cells were transfected with plasmids expressing EGFP-fused wild-type or mutant SIP for 24 h, incubated for 12 h with 25 mg/ml etoposide (ETO), 500 ng/ml doxorubicin (DOXO), 25 mM nitro mustard (NM), 100 J/m 2 ultraviolet irradiation (UV) or 35 mM curcumin (CUR), then applied to analyses for SIP localization and cell apoptosis.…”

Section: Discussionmentioning

confidence: 99%

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Identification of Siah-interacting protein as a potential regulator of apoptosis and curcumin resistance

Luo

Yang

et al. 2010

Oncogene

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The mechanism underlying curcumin (diferuloylmethane) resistance is still largely unknown. Here we employed proteomic approach to identify the Siah-interacting protein (SIP) as a candidate for detailed study, because the spot intensity of SIP on a two-dimensional gel displayed 70-90% reduction in curcumin-sensitive cells, but remained unchanged in curcumin-resistant sublines, after curcumin treatment. Both gain-and loss-of-function studies revealed that SIP promoted curcumin-induced apoptosis. Moreover, SIP underwent phosphorylation and nuclear translocation in curcumin-sensitive but not resistant cells, upon curcumin exposure. The nuclear translocation of SIP was remarkably impaired when a putative nuclear localization sequence (NLS, amino acid (aa) 143-159) was deleted or the serine 141 was mutated into alanine, whereas truncation of the N-terminal region (aa 1-43) obviously increased the nuclear import of SIP. In accordance with their nuclear localization, N-terminal truncation significantly enhanced the proapoptotic effect of SIP, whereas NLS deletion or Ser141Ala mutation attenuated the apoptosis-promoting activity of both wildtype-and N-terminal truncated-SIP. These data suggest that SIP plays a role in apoptosis and curcumin resistance, and the function of SIP may be regulated by different motifs, such as the NLS, N-terminal region and serine 141. Our findings provide new insights into the biological significance of SIP and the mechanisms of drug resistance.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Identification of Siah-interacting protein as a potential regulator of apoptosis and curcumin resistance

Luo

Yang

et al. 2010

Oncogene

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“…Recently Linding et al (9) developed NetworKIN and constructed a human phosphorylation network, which has gained diversified interest not only for human phosphorylation network prediction but also for general implication in cell biology. To predict kinase-specific phosphorylation sites, several on-line Web services have been implemented using various algorithms, including our previous work of GPS (10,11) and PPSP (12), NetPhosK (13), ScanSite (14), KinasePhos (15,16), PredPhospho (17), Predikin (18), PhoScan (19), pkaPS (6), etc.…”

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confidence: 99%

GPS 2.0, a Tool to Predict Kinase-specific Phosphorylation Sites in Hierarchy

Xue

Ren

Gao

et al. 2008

Molecular & Cellular Proteomics

604

610

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Identification of protein phosphorylation sites with their cognate protein kinases (PKs) is a key step to delineate molecular dynamics and plasticity underlying a variety of cellular processes. Although nearly 10 kinase-specific prediction programs have been developed, numerous PKs have been casually classified into subgroups without a standard rule. For large scale predictions, the false positive rate has also never been addressed. In this work, we adopted a well established rule to classify PKs into a hierarchical structure with four levels, including group, family, subfamily, and single PK. In addition, we developed a simple approach to estimate the theoretically maximal false positive rates. The on-line service and local packages of the GPS (Group-based Prediction System) 2.0 were implemented in Java with the modified version of the Group-based Phosphorylation Scoring algorithm. As the first stand alone software for predicting phosphorylation, GPS 2.0 can predict kinase-specific phosphorylation sites for 408 human PKs in hierarchy. A large scale prediction of more than 13,000 mammalian phosphorylation sites by GPS 2.0 was exhibited with great performance and remarkable accuracy. Using Aurora-B as an example, we also conducted a proteome-wide search and provided systematic prediction of Aurora-B-specific substrates including protein-protein interaction information. Thus, the GPS 2.0 is a useful tool for predicting protein phosphorylation sites and their cognate kinases and is freely available on line. Molecular & Cellular Proteomics 7:1598 -1608, 2008.Post-translational modification of proteins provides reversible means to regulate the function of a protein in space and time. Recently computational studies of post-translational modifications (PTMs) 1 of proteins have attracted much attention. Various PTMs regulate the functions and dynamics of proteins through specific modifications and are implicated in almost all cellular processes. In contrast to the labor-intensive and expensive experimental methods, in silico prediction of PTM-specific substrates with their sites has emerged as a popular alternative approach. To date, more than 32 computational prediction tools have been developed (1). In the field of computational PTMs, protein phosphorylation is the most studied example. To predict general phosphorylation sites, several tools have been developed, such as DISPHOS (2), NetPhos (3), NetPhosYeast (4), and GANNPhos (5). As the need for performing large scale predictions and constructing reliable phosphorylation networks evolves, robust prediction of kinase-specific phosphorylation sites has become necessary and challenging. For example, Neuberger et al. ) developed NetworKIN and constructed a human phosphorylation network, which has gained diversified interest not only for human phosphorylation network prediction but also for general implication in cell biology. To predict kinase-specific phosphorylation sites, several on-line Web services have been implemented using various algorithms, including our previ...

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“…This resulted to the same number of phosphorylated virus proteins from virPTM, 12 phosphorylated proteins with 24 pSer, and 10 pThr from UniProtKB as well as 4 phosphorylated proteins with 2 pSer, and 2 pTyr from Phospho.ELM. In order to investigate the surrounding amino acids composition, with reference to KinasePhos [10], [11], sequence fragments are extracted using a window size of 11 centered on the phosphorylated residue. Fragments centered on phosphorylated residues are obtained and regarded as positive data while fragments centered on non-phosphorylated residues are regarded as negative data.…”

Section: A Data Constructionmentioning

confidence: 99%

Exploiting Two-Layered Support Vector Machine to Predict Phosphorylation Sites on Virus Proteins

Lu¹,

Bretaña²,

Chang³

et al. 2013

IJBBB

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Abstract-Protein phosphorylation in viruses plays crucial regulatory roles in enhancing progression, replication, and inhibition of host cell functions. Due to the difficulty of mass spectrometry-based identification of viral phosphorylation sites, we are motivated to develop a new method to investigate the substrate motifs and identify protein phosphorylation sites on viruses. The experimentally verified phosphorylation data were extracted from a public resource and a recursively statistical method is applied to cluster whole data set of phosphorylated sequences into subgroups containing remarkably sequence motifs around the phosphorylation sites. Two-layered Support Vector Machine (SVM) is then applied to learn a predictive model by integrating the detected sequence motifs. A five-fold cross validation evaluation on the SVM model yields an average accuracy of 0.88 for Serine and 0.83 for Threonine. Furthermore, the independent testing data collected from UniProtKB and Phospho.ELM indicates that the proposed method is comparable with three popular kinase-specific phosphorylation site prediction tools. The cross validation and independent testing demonstrated that the sequence motifs are informative for the prediction of potential kinases for virus protein phosphorylation sites. Furthermore, the proposed method is a practical means of preliminary analysis for virus phosphorylation dynamics.Index Terms-Virus, protein phosphorylation, substrate motif, support vector machine.

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KinasePhos: a web tool for identifying protein kinase-specific phosphorylation sites

Cited by 297 publications

References 9 publications

Identification of Siah-interacting protein as a potential regulator of apoptosis and curcumin resistance

Identification of Siah-interacting protein as a potential regulator of apoptosis and curcumin resistance

GPS 2.0, a Tool to Predict Kinase-specific Phosphorylation Sites in Hierarchy

Exploiting Two-Layered Support Vector Machine to Predict Phosphorylation Sites on Virus Proteins

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