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Protein kinases associated with the yeast phosphoproteome
Ross I Brinkworth, Alan L Munn, Bo?tjan Kobe
BMC Bioinformatics , 2006, DOI: 10.1186/1471-2105-7-47
Abstract: We used Predikin in combination with other bioinformatic tools, to predict which of 116 unique protein kinases in yeast phosphorylates each experimentally determined site in the phosphoproteome. The prediction was based on the match between the phosphorylated 7-residue sequence and the predicted substrate specificity of each kinase, with the highest weight applied to the residues or positions that contribute most to the substrate specificity. We estimated the reliability of the predictions by performing a parallel prediction on phosphopeptides for which the kinase has been experimentally determined.The results reveal that the functions of the protein kinases and their predicted phosphoprotein substrates are often correlated, for example in endocytosis, cytokinesis, transcription, replication, carbohydrate metabolism and stress response. The predictions link phosphoproteins of unknown function with protein kinases with known functions and vice versa, suggesting functions for the uncharacterized proteins. The study indicates that the phosphoproteins and the associated protein kinases represented in our dataset have housekeeping cellular roles; certain kinases are not represented because they may only be activated during specific cellular responses. Our results demonstrate the utility of our previously reported protein kinase substrate prediction approach (Predikin) as a tool for establishing links between kinases and phosphoproteins that can subsequently be tested experimentally.Saccharomyces cerevisiae expresses over 110 Ser/Thr protein kinases that have been classified into 7 groups [1] (Table 1, 1). Most human protein kinases have orthologues in yeast, including protein kinase A (PKA), protein kinase C (PKC), Akt (PKB), calcium/calmodulin-dependent kinase type II (CaMK2), 5'-AMP activated kinase (AMPK), cyclin-dependent kinases (CDKs), mitogen-activated protein kinases (MAPKs), glycogen synthase kinase 3β (GSK3β), p21-activated kinase (PAK1), polo-like kinase (PLK1
A high-accuracy consensus map of yeast protein complexes reveals modular nature of gene essentiality
G Traver Hart, Insuk Lee, Edward M Marcotte
BMC Bioinformatics , 2007, DOI: 10.1186/1471-2105-8-236
Abstract: Using an unsupervised probabilistic scoring scheme, we assigned a confidence score to each interaction in the matrix-model interpretation of the large-scale yeast mass-spectrometry data sets. The scoring metric proved more accurate than the filtering schemes used in the original data sets. We then took a high-confidence subset of these interactions and derived a set of complexes using MCL. The complexes show high correlation with existing annotations. Hierarchical organization of some protein complexes is evident from inter-complex interactions.We demonstrate that our scoring method can generate an integrated high-confidence subset of observed matrix-model interactions, which we subsequently used to derive an accurate map of yeast complexes. Our results indicate that essentiality is a product of the protein complex rather than the individual protein, and that we have achieved near saturation of the yeast high-abundance, rich-media-expressed "complex-ome."The molecular machines that carry out basic cellular processes are typically not individual proteins but protein complexes. Even in the relatively simple model organism Saccharomyces cerevisiae, most machines that process and store biological information are in fact large protein complexes comprised of many subunits.The path from measuring protein interactions to defining complexes has been well studied. Experimental and computational methods have provided over 50,000 putative yeast protein-protein interactions to date, although a substantial fraction of these may be spurious[1,2]. An array of analytical methods aimed at generating high-quality complexes from these data have been applied, including both unsupervised [3-5] and trained [6,7] techniques. Other genomic and proteomic data sets, such as gene expression, knockout phenotype, subcellular localization, and genetic interaction profiles, and phylogenetic profiles [5,6,8-10], have also been integrated with the raw interaction data in an effort to broaden and dee
Genome-Wide Association Data Reveal a Global Map of Genetic Interactions among Protein Complexes  [PDF]
Gregory Hannum equal contributor,Rohith Srivas equal contributor,Aude Guénolé,Haico van Attikum,Nevan J. Krogan,Richard M. Karp,Trey Ideker
PLOS Genetics , 2009, DOI: 10.1371/journal.pgen.1000782
Abstract: This work demonstrates how gene association studies can be analyzed to map a global landscape of genetic interactions among protein complexes and pathways. Despite the immense potential of gene association studies, they have been challenging to analyze because most traits are complex, involving the combined effect of mutations at many different genes. Due to lack of statistical power, only the strongest single markers are typically identified. Here, we present an integrative approach that greatly increases power through marker clustering and projection of marker interactions within and across protein complexes. Applied to a recent gene association study in yeast, this approach identifies 2,023 genetic interactions which map to 208 functional interactions among protein complexes. We show that such interactions are analogous to interactions derived through reverse genetic screens and that they provide coverage in areas not yet tested by reverse genetic analysis. This work has the potential to transform gene association studies, by elevating the analysis from the level of individual markers to global maps of genetic interactions. As proof of principle, we use synthetic genetic screens to confirm numerous novel genetic interactions for the INO80 chromatin remodeling complex.
Comment on Yu et al., "High Quality Binary Protein Interaction Map of the Yeast Interactome Network." Science 322, 104 (2008)  [PDF]
Aaron Clauset
Physics , 2009,
Abstract: We test the claim by Yu et al. -- presented in Science 322, 104 (2008) -- that the degree distribution of the yeast (Saccharomyces cerevisiae) protein-interaction network is best approximated by a power law. Yu et al. consider three versions of this network. In all three cases, however, we find the most likely power-law model of the data is distinct from and incompatible with the one given by Yu et al. Only one network admits good statistical support for any power law, and in that case, the power law explains only the distribution of the upper 10% of node degrees. These results imply that there is considerably more structure present in the yeast interactome than suggested by Yu et al., and that these networks should probably not be called "scale free."
ProKinO: An Ontology for Integrative Analysis of Protein Kinases in Cancer  [PDF]
Gurinder Gosal, Krys J. Kochut, Natarajan Kannan
PLOS ONE , 2011, DOI: 10.1371/journal.pone.0028782
Abstract: Background Protein kinases are a large and diverse family of enzymes that are genomically altered in many human cancers. Targeted cancer genome sequencing efforts have unveiled the mutational profiles of protein kinase genes from many different cancer types. While mutational data on protein kinases is currently catalogued in various databases, integration of mutation data with other forms of data on protein kinases such as sequence, structure, function and pathway is necessary to identify and characterize key cancer causing mutations. Integrative analysis of protein kinase data, however, is a challenge because of the disparate nature of protein kinase data sources and data formats. Results Here, we describe ProKinO, a protein kinase-specific ontology, which provides a controlled vocabulary of terms, their hierarchy, and relationships unifying sequence, structure, function, mutation and pathway information on protein kinases. The conceptual representation of such diverse forms of information in one place not only allows rapid discovery of significant information related to a specific protein kinase, but also enables large-scale integrative analysis of protein kinase data in ways not possible through other kinase-specific resources. We have performed several integrative analyses of ProKinO data and, as an example, found that a large number of somatic mutations (~288 distinct mutations) associated with the haematopoietic neoplasm cancer type map to only 8 kinases in the human kinome. This is in contrast to glioma, where the mutations are spread over 82 distinct kinases. We also provide examples of how ontology-based data analysis can be used to generate testable hypotheses regarding cancer mutations. Conclusion We present an integrated framework for large-scale integrative analysis of protein kinase data. Navigation and analysis of ontology data can be performed using the ontology browser available at: http://vulcan.cs.uga.edu/prokino.
Quantitative maps of genetic interactions in yeast - Comparative evaluation and integrative analysis
Rolf O Lindén, Ville-Pekka Eronen, Tero Aittokallio
BMC Systems Biology , 2011, DOI: 10.1186/1752-0509-5-45
Abstract: Using large-scale data matrices from epistatic miniarray profiling (E-MAP), genetic interaction mapping (GIM), and synthetic genetic array (SGA) approaches, we carried out here a systematic comparative evaluation among these quantitative maps of genetic interactions in yeast. The relatively low association between the original interaction measurements or their customized scores could be improved using a matrix-based modelling framework, which enables the use of single- and double-mutant fitness estimates and measurements, respectively, when scoring genetic interactions. Toward an integrative analysis, we show how the detections from the different screening approaches can be combined to suggest novel positive and negative interactions which are complementary to those obtained using any single screening approach alone. The matrix approximation procedure has been made available to support the design and analysis of the future screening studies.We have shown here that even if the correlation between the currently available quantitative genetic interaction maps in yeast is relatively low, their comparability can be improved by means of our computational matrix approximation procedure, which will enable integrative analysis and detection of a wider spectrum of genetic interactions using data from the complementary screening approaches.The recent advances in experimental biotechnologies have made it possible to start screening genome-wide datasets of quantitative genetic interactions in model organisms such as yeast [1-3]. High-throughput genetic screening approaches, such as those based on epistatic miniarray profiling (E-MAP) [4-7], genetic interaction mapping (GIM) [8], and synthetic genetic array (SGA) [9-11], have provided systematic means to global investigation of quantitative relationship between genotype and phenotype, with potential implications for a wide range of biological phenomena, including, for instance, modularity, essentiality, redundancy, buffering, epi
Integrative Analysis of the Mitochondrial Proteome in Yeast  [PDF]
Holger Prokisch,Curt Scharfe,David G. Camp II,Wenzhong Xiao,Lior David,Christophe Andreoli,Matthew E. Monroe,Ronald J. Moore,Marina A. Gritsenko,Christian Kozany,Kim K. Hixson,Heather M. Mottaz,Hans Zischka,Marius Ueffing,Zelek S. Herman,Ronald W. Davis,Thomas Meitinger,Peter J. Oefner,Richard D. Smith,Lars M. Steinmetz
PLOS Biology , 2012, DOI: 10.1371/journal.pbio.0020160
Abstract: In this study yeast mitochondria were used as a model system to apply, evaluate, and integrate different genomic approaches to define the proteins of an organelle. Liquid chromatography mass spectrometry applied to purified mitochondria identified 546 proteins. By expression analysis and comparison to other proteome studies, we demonstrate that the proteomic approach identifies primarily highly abundant proteins. By expanding our evaluation to other types of genomic approaches, including systematic deletion phenotype screening, expression profiling, subcellular localization studies, protein interaction analyses, and computational predictions, we show that an integration of approaches moves beyond the limitations of any single approach. We report the success of each approach by benchmarking it against a reference set of known mitochondrial proteins, and predict approximately 700 proteins associated with the mitochondrial organelle from the integration of 22 datasets. We show that a combination of complementary approaches like deletion phenotype screening and mass spectrometry can identify over 75% of the known mitochondrial proteome. These findings have implications for choosing optimal genome-wide approaches for the study of other cellular systems, including organelles and pathways in various species. Furthermore, our systematic identification of genes involved in mitochondrial function and biogenesis in yeast expands the candidate genes available for mapping Mendelian and complex mitochondrial disorders in humans.
Integrative Analysis of the Mitochondrial Proteome in Yeast  [PDF]
Holger Prokisch equal contributor,Curt Scharfe equal contributor,David G Camp II equal contributor,Wenzhong Xiao equal contributor,Lior David,Christophe Andreoli,Matthew E Monroe,Ronald J Moore,Marina A Gritsenko,Christian Kozany,Kim K Hixson,Heather M Mottaz,Hans Zischka,Marius Ueffing,Zelek S Herman,Ronald W Davis,Thomas Meitinger,Peter J Oefner,Richard D Smith,Lars M Steinmetz
PLOS Biology , 2004, DOI: 10.1371/journal.pbio.0020160
Abstract: In this study yeast mitochondria were used as a model system to apply, evaluate, and integrate different genomic approaches to define the proteins of an organelle. Liquid chromatography mass spectrometry applied to purified mitochondria identified 546 proteins. By expression analysis and comparison to other proteome studies, we demonstrate that the proteomic approach identifies primarily highly abundant proteins. By expanding our evaluation to other types of genomic approaches, including systematic deletion phenotype screening, expression profiling, subcellular localization studies, protein interaction analyses, and computational predictions, we show that an integration of approaches moves beyond the limitations of any single approach. We report the success of each approach by benchmarking it against a reference set of known mitochondrial proteins, and predict approximately 700 proteins associated with the mitochondrial organelle from the integration of 22 datasets. We show that a combination of complementary approaches like deletion phenotype screening and mass spectrometry can identify over 75% of the known mitochondrial proteome. These findings have implications for choosing optimal genome-wide approaches for the study of other cellular systems, including organelles and pathways in various species. Furthermore, our systematic identification of genes involved in mitochondrial function and biogenesis in yeast expands the candidate genes available for mapping Mendelian and complex mitochondrial disorders in humans.
An Integrative Model of Ion Regulation in Yeast  [PDF]
Ruian Ke ,Piers J. Ingram,Ken Haynes
PLOS Computational Biology , 2013, DOI: 10.1371/journal.pcbi.1002879
Abstract: Yeast cells are able to tolerate and adapt to a variety of environmental stresses. An essential aspect of stress adaptation is the regulation of monovalent ion concentrations. Ion regulation determines many fundamental physiological parameters, such as cell volume, membrane potential, and intracellular pH. It is achieved through the concerted activities of multiple cellular components, including ion transporters and signaling molecules, on both short and long time scales. Although each component has been studied in detail previously, it remains unclear how the physiological parameters are maintained and regulated by the concerted action of all components under a diverse range of stress conditions. In this study, we have constructed an integrated mathematical model of ion regulation in Saccharomyces cerevisiae to understand this coordinated adaptation process. Using this model, we first predict that the interaction between phosphorylated Hog1p and Tok1p at the plasma membrane inhibits Tok1p activity and consequently reduces Na+ influx under NaCl stress. We further characterize the impacts of NaCl, sorbitol, KCl and alkaline pH stresses on the cellular physiology and the differences between the cellular responses to these stresses. We predict that the calcineurin pathway is essential for maintaining a non-toxic level of intracellular Na+ in the long-term adaptation to NaCl stress, but that its activation is not required for maintaining a low level of Na+ under other stresses investigated. We provide evidence that, in addition to extrusion of toxic ions, Ena1p plays an important role, in some cases alongside Nha1p, in re-establishing membrane potential after stress perturbation. To conclude, this model serves as a powerful tool for both understanding the complex system-level properties of the highly coordinated adaptation process and generating further hypotheses for experimental investigation.
Yeast fitness and protein evolution  [cached]
Reiner Veitia
Genome Biology , 2001, DOI: 10.1186/gb-2001-2-9-reports0030
Abstract: Hirsh and Fraser obtained reliable estimates of fitness for 548 homozygous single-gene deletants. The evolutionary distances di (number of substitutions per amino acid site) could be estimated for 119 mutants out of the 548 using two different methods. They found a statistically significant relationship between di and fi, showing that proteins with a lower fitness effect are more divergent. But, the di values represent evolutionary change not only in the yeast sequences but also in the corresponding nematode orthologs. To estimate evolutionary change only in the lineage leading to yeast, di measures were split into two components, one between yeast and the hypothetical most recent common ancestor (MRCA) of fungi and animals and another between the MRCA and C. elegans: the outgroup sequences were orthologs present in other completely sequenced genomes. Hirsh and Fraser analyzed 48 yeast proteins in this way and again a significant relationship between fi and the 'new' di values was obtained. Most interestingly, statistically significant results were obtained when they plotted fi (from yeast) against the di between the MRCA and the nematode. This suggests that non-essential proteins that have an impact on the fitness of yeast might also have a proportional effect on worm fitness. No differences were detected when the essential genes were subject to the same analysis and compared with the non-essential ones, which is consistent with the results of analyses in mouse. When the comparison was carried out against the most dispensable 60 proteins (smallest fi values), however, a highly significant difference was detected.This work provides the long awaited (at least preliminary) confirmation of a fundamental prediction about protein evolution. It is an example of how genomic data can be exploited from different perspectives to address important biological questions. In fact, the starting point of the present study was the results of a high-throughput parallel analysis of ye
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