Exploiting maximal dependence decomposition to identify conserved motifs from a group of aligned signal sequences

Lee, Tzong-Yi; Lin, Zong-Qing; Hsieh, Sheng‐Jen; Bretaña, Neil Arvin; Lu, Cheng-Tsung

doi:10.1093/bioinformatics/btr291

Cited by 108 publications

(88 citation statements)

References 26 publications

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“…This high diversity of the phosphorylation motifs extracted is consistent with the involvement of a large number of kinases. Indeed, the Pro-directed motifs are specific for MAPK and CDC2, the basic motifs are specific for PKA, PKB and PKC, and the acidic motifs are specific for the CK2 and ATM kinase groups 25 . The distribution of phosphorylation motif classes differed between TW and OW.…”

Section: Phosphorylation Sites Overrepresented In the Different Typesmentioning

confidence: 99%

Quantitative Proteomic and Phosphoproteomic Approaches for Deciphering the Signaling Pathway for Tension Wood Formation in Poplar

Mauriat

Leplé²,

Claverol

et al. 2015

J. Proteome Res.

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Trees adjust their growth following forced changes in orientation to re-establish a vertical position. In angiosperms, this adjustment involves the differential regulation of vascular cambial activity between the lower (opposite wood) and upper (tension wood) sides of the leaning stem. We investigated the molecular mechanisms leading to the formation of differential wood types through a quantitative proteomic and phosphoproteomic analysis on poplar subjected to a gravitropic stimulus. We identified and quantified 675 phosphopeptides, corresponding to 468 phosphoproteins, and 3 763 nonphosphorylated peptides, corresponding to 1 155 proteins, in the differentiating xylem of straight-growing trees (control) and trees subjected to a gravitational stimulus during 8 weeks. About 1% of the peptides were specific to a wood type (straight, opposite, or tension wood). Proteins quantified in more than one type of wood were more numerous: a mixed linear model showed 389 phosphopeptides and 556 proteins to differ in abundance between tension wood and opposite wood. Twenty-one percent of the phosphoproteins identified here were described in their phosphorylated form for the first time. Our analyses revealed remarkable developmental molecular plasticity, with wood type-specific phosphorylation events, and highlighted the involvement of different proteins in the biosynthesis of cell wall components during the formation of the three types of wood.

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Section: Phosphorylation Sites Overrepresented In the Different Typesmentioning

confidence: 99%

Quantitative Proteomic and Phosphoproteomic Approaches for Deciphering the Signaling Pathway for Tension Wood Formation in Poplar

Mauriat

Leplé²,

Claverol

et al. 2015

J. Proteome Res.

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show abstract

“…In order to find any possible up-regulated kinase motifs, we searched the presented data set using Motif-X algorithm. It should be noted that the information about linking a particular kinase to a phosphorylation motif is limited in plants and consequently the information is often extrapolated from other model organisms, mostly human (65). Two searches were performed looking up either for phosphoserine or for phosphothreonine (supplemental Fig.…”

Section: Discussionmentioning

confidence: 99%

Phosphoproteomics Profiling of Tobacco Mature Pollen and Pollen Activated in vitro

Fíla

Radau

Matros

et al. 2016

Molecular & Cellular Proteomics

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Tobacco mature pollen has extremely desiccated cytoplasm, and is metabolically quiescent. Upon re-hydration it becomes metabolically active and that results in later emergence of rapidly growing pollen tube. These changes in cytoplasm hydration and metabolic activity are accompanied by protein phosphorylation. In this study, we subjected mature pollen, 5-min-activated pollen, and 30-min-activated pollen to TCA/acetone protein extraction, trypsin digestion and phosphopeptide enrichment by titanium dioxide. The enriched fraction was subjected to nLC-MS/MS. We identified 471 phosphopeptides that carried 432 phosphorylation sites, position of which was exactly matched by mass spectrometry. These 471 phosphopeptides were assigned to 301 phosphoproteins, because some proteins carried more phosphorylation sites. Of the 13 functional groups, the majority of proteins were put into these categories: transcription, protein synthesis, protein destination and storage, and signal transduction. Many proteins were of unknown function, reflecting the fact that male gametophyte contains many specific proteins that have not been fully functionally annotated. The quantitative data highlighted the dynamics of protein phosphorylation during pollen activation; the identified phosphopeptides were divided into seven groups based on the regulatory trends. The major group comprised mature pollen-specific phosphopeptides that were dephosphorylated during pollen activation. Several phosphopeptides representing the same phosphoprotein had different regulation, which pinpointed the complexity of protein phosphorylation and its clear functional context. Collectively, we showed the first phosphoproteomics data on activated pollen where the position of phosphorylation sites was clearly demonstrated and regulatory kinetics was resolved. Molecular & Cellular

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“…MDD is a methodology that groups a set of aligned signal sequences to moderate a large group into subgroups that capture the most significant dependencies between positions. Previous studies [12], [16] have proposed the grouping of protein sequences into smaller groups prior to computationally identifying PTM sites. For this study, MDD is applied using MDDLogo [16].…”

Section: B Motif Detectionmentioning

confidence: 99%

“…Previous studies [12], [16] have proposed the grouping of protein sequences into smaller groups prior to computationally identifying PTM sites. For this study, MDD is applied using MDDLogo [16]. MDD adopts chi-square test to evaluate the dependence of amino acid occurrence between two positions, Ai and Aj, which surround the phosphorylation site.…”

Section: B Motif Detectionmentioning

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

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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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Exploiting maximal dependence decomposition to identify conserved motifs from a group of aligned signal sequences

Abstract: francis@saturn.yzu.edu.tw

Cited by 108 publications

References 26 publications

Quantitative Proteomic and Phosphoproteomic Approaches for Deciphering the Signaling Pathway for Tension Wood Formation in Poplar

Quantitative Proteomic and Phosphoproteomic Approaches for Deciphering the Signaling Pathway for Tension Wood Formation in Poplar

Phosphoproteomics Profiling of Tobacco Mature Pollen and Pollen Activated in vitro

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

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