Construction of human activity‐based phosphorylation networks

Newman, Robert H.; Hu, Jianfei; Rho, Hee-Sool; Xie, Zhi; Woodard, Crystal; Neiswinger, John; Cooper, Christopher L.; Shirley, Matthew D.; Clark, Hillary M; Hu, Shaohui; Hwang, Woochang; Jeong, Jun Seop; Wu, George Y.; Lin, Jimmy; Gao, Xinxin; Ni, Qing Zhe; Goel, Renu; Xia, Shuli; Ji, Hongkai; Dalby, Kevin N.; Birnbaum, Morris J.; Cole, Philip A.; Knapp, Stefan; Ryazanov, Alexey G.; Zack, Donald J.; Blackshaw, Seth; Pawson, Tony; Gingras, Anne‐Claude; Desiderio, Stephen; Pandey, Akhilesh; Turk, Benjamin E.; Zhang, Jinpeng; Zhu, Heng; Qian, Jiang

doi:10.1038/msb.2013.12

Cited by 152 publications

(171 citation statements)

References 35 publications

Supporting

Mentioning

170

Contrasting

Order By: Relevance

“…Experimental and predicted protein p-sites from PhosPhAt have been used to assess conservation of p-sites in single-nucleotide polymorphisms (Riaño-Pachón et al, 2010) or phosphorylation hot spots (Christian et al, 2012). In other (nonplant) organisms, large-scale p-proteomics data sets have been successfully used to analyze kinase-specific phosphorylation (Linding et al, 2008;Xue et al, 2008;Newman et al, 2013), and in combination with protein-protein interaction data (Yachie et al, 2011;Song et al, 2012), this information has served to identify the evolution of protein regulation through protein phosphorylation (Boekhorst et al, 2008;Beltrao et al, 2009;Gnad et al, 2010;Pearlman et al, 2011). On a smaller scale, a cross-species comparison were performed between rice (Oryza sativa) and Arabidopsis (Nakagami et al, 2010), allowing for a discussion of the role of conserved protein p-sites.…”

Section: Introductionmentioning

confidence: 99%

Meta-Analysis of Arabidopsis thaliana Phospho-Proteomics Data Reveals Compartmentalization of Phosphorylation Motifs

et al. 2014

View full text Add to dashboard Cite

ORCID ID: 0000-0001-9536-0487 (K.J.v.W.)Protein (de)phosphorylation plays an important role in plants. To provide a robust foundation for subcellular phosphorylation signaling network analysis and kinase-substrate relationships, we performed a meta-analysis of 27 published and unpublished in-house mass spectrometry-based phospho-proteome data sets for Arabidopsis thaliana covering a range of processes, (non)photosynthetic tissue types, and cell cultures. This resulted in an assembly of 60,366 phospho-peptides matching to 8141 nonredundant proteins. Filtering the data for quality and consistency generated a set of medium and a set of high confidence phospho-proteins and their assigned phospho-sites. The relation between single and multiphosphorylated peptides is discussed. The distribution of p-proteins across cellular functions and subcellular compartments was determined and showed overrepresentation of protein kinases. Extensive differences in frequency of pY were found between individual studies due to proteomics and mass spectrometry workflows. Interestingly, pY was underrepresented in peroxisomes but overrepresented in mitochondria. Using motif-finding algorithms motif-x and MMFPh at high stringency, we identified compartmentalization of phosphorylation motifs likely reflecting localized kinase activity. The filtering of the data assembly improved signal/noise ratio for such motifs. Identified motifs were linked to kinases through (bioinformatic) enrichment analysis. This study also provides insight into the challenges/pitfalls of using large-scale phospho-proteomic data sets to nonexperts.

show abstract

Section: Introductionmentioning

confidence: 99%

Meta-Analysis of Arabidopsis thaliana Phospho-Proteomics Data Reveals Compartmentalization of Phosphorylation Motifs

et al. 2014

View full text Add to dashboard Cite

show abstract

“…As alternatives to synthetic peptide-based approaches proteome-derived peptide libraries constructed from tryptic digests of human cell lysates were used to probe CK2 kinase specificity (Wang et al, 2013), and a micro array of 4191 full-length human proteins was used to assay kinase-substrate relationships for 289 kinases (Newman et al, 2013). To bypass endogenous kinase activity, motifs were also revealed by mass spectrometry for recombinant serine/threonine kinases PKA and CK2 expressed in bacteria (Chou et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Defining Human Tyrosine Kinase Phosphorylation Networks Using Yeast as an In Vivo Model Substrate

et al. 2017

View full text Add to dashboard Cite

Systematic assessment of tyrosine kinase-substrate relationships is fundamental to a better understanding of cellular signaling and its profound alterations in human diseases such as cancer. In human cells, complex signaling networks, feedback loops, conditional activity and intra-kinase redundancy combine to confound systematic attempts to address this topic. We leveraged the model organism S. cerevisiae to individually express human non-receptor tyrosine kinases (NRTKs) and exploited the full yeast proteome as an in vivo model substrate. For 16 NRTKs, we recorded 3,279 kinase-substrate relationships involving 1,351 yeast pY-sites. From this data we generated a set of new linear kinase motifs and assigned ~1,300 known human pY-sites to specific NRTKs. Furthermore, experimentally defined pY-sites for each individual kinase were shown to cluster within the yeast interactome network irrespective of linear motif information. We therefore applied a network inference approach to predict kinase-substrate relationships for more than 3,500 human proteins, marking a substantial step forward in our understanding of kinase biology.. Corwin et al., revised3 7/25/2017 3

show abstract

“…However, upon changing conditions, PTMs do show massive dynamic responses with hundreds to thousand dynamic PTM-site changes [62,68,69], providing a strong argument for their functional importance. Predicting putative enzyme-site relationships, for example, using an integrative Bayesian approach [70], clustering dynamic changes throughout the cell cycle [62] or in stem cell development [69], or examining evolutionary conservation and structural relationships [64 ,65 ] can all be informative in identifying PTM functionality. However, as the MS studies described above highlight [59 ,60 ], an additional appropriate data filter for prioritizing the functionally most relevant PTM-signals is interaction network context.…”

Section: Discussionmentioning

confidence: 99%

Studying post-translational modifications with protein interaction networks

Woodsmith

Stelzl

2014

Current Opinion in Structural Biology

View full text Add to dashboard Cite

At least 46 interactome studies, broad at proteome scale or biologically more focused, have together mapped about 75,000 human protein-protein interactions (PPIs). Many of the studies addressed local interactome data paucity analyzing specific homeostatic and regulatory systems, with recent focus demonstrating the involvement of post-translational protein modification (PTM) enzyme families in a wide range of cellular functions. These datasets provided insight into binding mechanisms, the dynamic modularity of complexes or delineated combinatorial enzymatic cascades. Furthermore, the combined study of PPI and PTM dynamics has begun to reveal conditional rewiring of molecular networks through PTMmediated recognition events. Taken together these studies highlight the utility of local and global interaction networks to functionally prioritize the many changing PTMs mapped in human cells.

show abstract

Construction of human activity‐based phosphorylation networks

Abstract: A high-resolution map of human phosphorylation networks was constructed by integrating experimentally determined kinase-substrate relationships with other resources, such as in vivo phosphorylation sites.

Cited by 152 publications

References 35 publications

Meta-Analysis of Arabidopsis thaliana Phospho-Proteomics Data Reveals Compartmentalization of Phosphorylation Motifs

Meta-Analysis of Arabidopsis thaliana Phospho-Proteomics Data Reveals Compartmentalization of Phosphorylation Motifs

Defining Human Tyrosine Kinase Phosphorylation Networks Using Yeast as an In Vivo Model Substrate

Studying post-translational modifications with protein interaction networks

Contact Info

Product

Resources

About