KinasePhos 2.0: a web server for identifying protein kinase-specific phosphorylation sites based on sequences and coupling patterns

Wong, Yuen Yee; Lee, Tzong-Yi; Liang, Han-Kuen; Huang, Chia-Mao; Wang, Ting-Yuan; Yang, Yihao; Chu, Chin‐Chou; Huang, Hsien-Da; Ko, Ming-Tat; Hwang, Jiann Yang

doi:10.1093/nar/gkm322

Cited by 322 publications

(267 citation statements)

References 23 publications

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“…For example, DBN-1 is a possible target for phosphorylation by Src-1 (ref. 38). Association of DBN-1 and LEV-11 with distinct parts of actin filaments at muscle contraction suggests that DBN-1 and tropomyosin compete for the binding to F-actin.…”

Section: Discussionmentioning

confidence: 99%

Drebrin-like protein DBN-1 is a sarcomere component that stabilizes actin filaments during muscle contraction

et al. 2015

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Actin filament organization and stability in the sarcomeres of muscle cells are critical for force generation. Here we identify and functionally characterize a Caenorhabditis elegans drebrin-like protein DBN-1 as a novel constituent of the muscle contraction machinery. In vitro, DBN-1 exhibits actin filament binding and bundling activity. In vivo, DBN-1 is expressed in body wall muscles of C. elegans. During the muscle contraction cycle, DBN-1 alternates location between myosin-and actin-rich regions of the sarcomere. In contracted muscle, DBN-1 is accumulated at I-bands where it likely regulates proper spacing of a-actinin and tropomyosin and protects actin filaments from the interaction with ADF/cofilin. DBN-1 loss of function results in the partial depolymerization of F-actin during muscle contraction. Taken together, our data show that DBN-1 organizes the muscle contractile apparatus maintaining the spatial relationship between actin-binding proteins such as a-actinin, tropomyosin and ADF/cofilin and possibly strengthening actin filaments by bundling.

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Section: Discussionmentioning

confidence: 99%

Drebrin-like protein DBN-1 is a sarcomere component that stabilizes actin filaments during muscle contraction

et al. 2015

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“…The SNP4 mutation leads to a substitution of serine for proline at position 365 in the predicted C-terminal fibrinogen-like domain. The KinasePhos 2.0 web server was used to predict phosphorylation sites of the ANGPTL3 (Wong et al, 2007). The result showed that serine-365 (S365) is a potential phosphorylation site.…”

Section: Discussionmentioning

confidence: 99%

Tissue expression and predicted protein structures of the bovine ANGPTL3 and association of novel SNPs with growth and meat quality traits

Chen

Yang

et al. 2015

Animal

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Angiopoietin-like protein 3 (ANGPTL3) is a secreted protein that regulates lipid, glucose and energy metabolism. This study was conducted to better understand the effect of ANGPTL3 on important economic traits in cattle. First, transcript profiles for ANGPTL3 were measured in nine different Jiaxian cattle tissues. Second, polymorphisms were identified in the complete coding region and promoter region of the bovine ANGPTL3 gene in 707 cattle samples. Finally, an association study was carried out utilizing these single nucleotide polymorphisms (SNPs) to determine the effect of these SNPs on the growth and meat quality traits. Quantitative real-time PCR analysis showed that ANGPTL3 was mainly expressed in the liver. The promoter of the bovine ANGPTL3 contained several putative transcription factor binding sites (SF1, HNF-1, LXRα, NFκβ, HNF-3 and C/EBP). In total, four SNPs of the bovine ANGPTL3 gene were identified by direct sequencing. SNP1 (rs469906272: g. − 38T > C) was identified in the promoter, SNP2 (rs451104723:g.104A > T) and SNP3 (rs482516226: g.509A > G) were identified in exon 1, and SNP4 (rs477165942: g.8661T > C) was identified in exon 6. Changes in predicted protein structures due to non-synonymous SNPs were analyzed. Haplotype frequencies and linkage disequilibrium were also investigated. Analysis of four SNPs in cattle from different native Chinese breeds (Nanyang (NY) and Jiaxian (JX)) and commercial breeds (Angus (AG), Hereford (HF), Limousin (LM), Luxi (LX), Simmental (ST) and Jinnan (JN)) revealed a significant association with growth traits (including: BW and hipbone width) and meat quality traits (including: Warner-Bratzler shear force and ribeye area). Therefore, implementation of these four mutations in selection indices in the beef industry may be beneficial in selecting individuals with superior growth and meat quality traits.

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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%

“…This is done in order to avoid a biased prediction performance. With reference to previous phosphorylation prediction methods [10], [12]- [15], a smaller number of negative fragments are obtained to match the number of positive fragments. This resulted to an equal number of positive and negative S, T, and Y fragments respectively in the three data sets as shown in Table I.…”

Section: A Data Constructionmentioning

confidence: 99%

“…Furthermore, the combined model using all pThr MDD-clustered SVMs yields a sensitivity of 0.95, a specificity of 0.90, an accuracy of 0.93, and the MCC of 0.73. To further demonstrate the effectiveness of the proposed method, the independent testing set is used to make a comparison between the performances of three popular kinase-specific phosphorylation site prediction tools, PPSP [15], KinasePhos 2.0 [10], and GPS 2.1 [21]. Without any pr ior information of catalytic kinases for the testing data, all of the kinase-specific models in the prediction tools are chosen for predicting the phosphorylation sites.…”

Section: Independent Testingmentioning

confidence: 99%

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

Lu¹,

Bretaña²,

Chang³

et al. 2013

IJBBB

Self Cite

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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 2.0: a web server for identifying protein kinase-specific phosphorylation sites based on sequences and coupling patterns

Cited by 322 publications

References 23 publications

Drebrin-like protein DBN-1 is a sarcomere component that stabilizes actin filaments during muscle contraction

Drebrin-like protein DBN-1 is a sarcomere component that stabilizes actin filaments during muscle contraction

Tissue expression and predicted protein structures of the bovine ANGPTL3 and association of novel SNPs with growth and meat quality traits

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

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