The APEX Quantitative Proteomics Tool: Generating protein quantitation estimates from LC-MS/MS proteomics results

Braisted, John C.; Kuntumalla, Srilatha; Vogel, Christine; Marcotte, Edward M.; Rodrigues, Alan R.; Wang, Rong; Huang, Shih Ting; Ferlanti, Erik S.; Saeed, Alexander I.; Fleischmann, Robert; Peterson, Scott N.; Pieper, Rembert

doi:10.1186/1471-2105-9-529

Cited by 150 publications

(136 citation statements)

References 20 publications

(26 reference statements)

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“…The major analytical caveat to using this approach is that spectral count ratios can be biased by protein size, peptide length and other peptide physiochemical properties that affect MS detection. Therefore, appropriate statistical analyses should be considered [8,9].…”

Section: Spectral Countingmentioning

confidence: 99%

Quantitative strategies to fuel the merger of discovery and hypothesis-driven shotgun proteomics

Kline¹,

Finney²,

Wu³

2009

Briefings in Functional Genomics and Proteomics

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The ultimate goal of most shotgun proteomic pipelines is the discovery of novel biomarkers to direct the development of quantitative diagnostics for the detection and treatment of disease. Differential comparisons of biological samples identify candidate peptides that can serve as proxys of candidate proteins. While these discovery approaches are robust and fairly comprehensive, they have relatively low throughput. When merged with targeted mass spectrometry, this pipeline can fuel hypothesis-driven studies and the development of novel diagnostics and therapeutics.

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Section: Spectral Countingmentioning

confidence: 99%

Quantitative strategies to fuel the merger of discovery and hypothesis-driven shotgun proteomics

Kline¹,

Finney²,

Wu³

2009

Briefings in Functional Genomics and Proteomics

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“…The utility of spectral counting as a mode of quantification is greatly enhanced by computational modeling of, and correction for, these experimental biases (Mallick et al, 2007). In this work, we used a computational approach to spectral counting termed APEX (Lu et al, 2007;Vogel and Marcotte, 2008) colorimetric assays such as the Bradford, Lowry or BCA), we could say that there are 2500 copies of that protein in the average cell in that sample. However, APEX is blind to all the non-detected proteins, which are generally numerous and, as noted in Section 3.5.1, not necessarily of low cellular abundance.…”

Section: Quantification Of Fractional Cellular Protein and Transcript Amentioning

confidence: 99%

“…To perform this analysis, the set of MS 2 spectra at each of the 14 diel timepoints identified as belonging to peptides of Prochlorococcus MED4 proteins were analyzed using the APEX Quantitative Proteomics Tool (v. 1.0.0, Braisted et al, 2008).…”

Section: Quantification Of Fractional Cellular Protein and Transcript Amentioning

confidence: 99%

Molecular biogeochemistry of modern and ancient marine microbes

Waldbauer¹

2010

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Biological activity has shaped the surface of the earth in numerous ways, but life's most pervasive and persistent global impact has been the secular oxidation of the surface environment. Through primary production -the biochemical reduction of carbon dioxide to synthesize biomass -large amounts of oxidants such as molecular oxygen, sulfate and ferric iron have accumulated in the ocean, atmosphere and crust, fundamentally altering the chemical environment of the earth's surface. This thesis addresses aspects of the role of marine microorganisms in driving this process. In the first section of the thesis, biomarkers (hydrocarbon molecular fossils) are used to investigate the early history of microbial diversity and biogeochemistry. Molecular fossils from the Transvaal Supergroup, South Africa, document the presence in the oceans of a diverse microbiota, including eukaryotes, as well as oxygenic photosynthesis and aerobic biochemistry, by ca. 2.7Ga. Experimental study of the oxygen requirements of steroid biosynthesis suggests that sterane biomarkers in late Archean rocks are consistent with the persistence of microaerobic surface ocean environments long before the initial oxygenation of the atmosphere. In the second part, using Prochlorococcus (a marine cyanobacterium that is the most abundant primary producer on earth today) as a model system, we explored how microbes use the limited nutrient resources available in the marine environment to make the protein catalysts that enable primary production. Quantification of the Prochlorococcus proteome over the diel cell-division cycle reveals that protein abundances are distinct from transcript-level dynamics, and that small temporal shifts in enzyme levels can redirect metabolic fluxes. This thesis illustrates how molecular techniques can contribute to a systems-level understanding of biogeochemical processes, which will aid in reconstructing the past of, and predicting future change in, earth surface environments. ACKNOWLEDGMENTSI have incurred many debts of gratitude and appreciation over the course of this work. None are greater than those to my advisors, Penny Chisholm and Roger Summons, who allowed and enabled me to pursue such a wide range of scientific interests and offered their help and advice every step of the way. I have been very lucky to have had the chance to learn so much during my Ph.D., and none of that would have been possible without their guidance and support.I have also had the great fortune to know and work with all the members of the Chisholm and Summons labs, who have made this an enjoyable and rewarding place to be. I am especially grateful to Laura Sherman for her skill and dedication in analyzing biomarkers in the late Archean cores -it was truly getting blood from a stone. I was lucky to collaborate with Sébastien Rodrigue, whose expertise and collegiality made the diel experiment even more fruitful than I could have hoped. IntroductionThe coevolution of life and earth surface environments is central to the history and functionin...

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“…Most methods make use of protein abundance and/or the protein translation rate inside the cell using mRNA expression levels as the principal predictive feature (7,15,22). Tuller and colleagues (6) used linear regression to integrate mRNA expression, tAI, and the evolutionary rate (ER) of the transcripts.…”

mentioning

confidence: 99%

Predicting the Dynamics of Protein Abundance

Mehdi

Patrick

Bailey

et al. 2014

Molecular & Cellular Proteomics

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Protein synthesis is finely regulated across all organisms, from bacteria to humans, and its integrity underpins many important processes. Emerging evidence suggests that the dynamic range of protein abundance is greater than that observed at the transcript level. Technological breakthroughs now mean that sequencing-based measurement of mRNA levels is routine, but protocols for measuring protein abundance remain both complex and expensive. This paper introduces a Bayesian network that integrates transcriptomic and proteomic data to predict protein abundance and to model the effects of its determinants. We aim to use this model to follow a molecular response over time, from condition-specific data, in order to understand adaptation during processes such as the cell cycle. With microarray data now available for many conditions, the general utility of a protein abundance predictor is broad.Whereas most quantitative proteomics studies have focused on higher organisms, we developed a predictive model of protein abundance for both Saccharomyces cerevisiae and Schizosaccharomyces pombe to explore the latitude at the protein level. Our predictor primarily relies on mRNA level, mRNA-protein interaction, mRNA folding energy and half-life, and tRNA adaptation. The combination of key features, allowing for the low certainty and uneven coverage of experimental observations, gives comparatively minor but robust prediction accuracy. The model substantially improved the analysis of protein regulation during the cell cycle: predicted protein abundance identified twice as many cell-cycle-associated proteins as experimental mRNA levels. Predicted protein abundance was more dynamic than observed mRNA expression, agreeing with experimental protein abundance from a human cell line. We illustrate how the same model can be used to predict the folding energy of mRNA when protein abundance is available, lending credence to the emerging view that mRNA folding affects translation efficiency. The software and data used in this research are available at http://bioinf.scmb. uq.edu.au/proteinabundance/. Molecular & Cellular Proteomics 13:

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The APEX Quantitative Proteomics Tool: Generating protein quantitation estimates from LC-MS/MS proteomics results

Cited by 150 publications

References 20 publications

Quantitative strategies to fuel the merger of discovery and hypothesis-driven shotgun proteomics

Quantitative strategies to fuel the merger of discovery and hypothesis-driven shotgun proteomics

Molecular biogeochemistry of modern and ancient marine microbes

Predicting the Dynamics of Protein Abundance

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