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1.  A comprehensive protein–protein interactome for yeast PAS kinase 1 reveals direct inhibition of respiration through the phosphorylation of Cbf1 
Molecular Biology of the Cell  2014;25(14):2199-2215.
PAS kinase is a conserved sensory protein kinase required for glucose homeostasis. The interactome for yeast PAS kinase 1 (Psk1) is identified, revealing 93 binding partners. Evidence is provided for in vivo phosphorylation of Cbf1 and subsequent inhibition of respiration, supporting a role for Psk1 in partitioning glucose for cell growth.
Per-Arnt-Sim (PAS) kinase is a sensory protein kinase required for glucose homeostasis in yeast, mice, and humans, yet little is known about the molecular mechanisms of its function. Using both yeast two-hybrid and copurification approaches, we identified the protein–protein interactome for yeast PAS kinase 1 (Psk1), revealing 93 novel putative protein binding partners. Several of the Psk1 binding partners expand the role of PAS kinase in glucose homeostasis, including new pathways involved in mitochondrial metabolism. In addition, the interactome suggests novel roles for PAS kinase in cell growth (gene/protein expression, replication/cell division, and protein modification and degradation), vacuole function, and stress tolerance. In vitro kinase studies using a subset of 25 of these binding partners identified Mot3, Zds1, Utr1, and Cbf1 as substrates. Further evidence is provided for the in vivo phosphorylation of Cbf1 at T211/T212 and for the subsequent inhibition of respiration. This respiratory role of PAS kinase is consistent with the reported hypermetabolism of PAS kinase–deficient mice, identifying a possible molecular mechanism and solidifying the evolutionary importance of PAS kinase in the regulation of glucose homeostasis.
doi:10.1091/mbc.E13-10-0631
PMCID: PMC4091833  PMID: 24850888
2.  Proteomics, lipidomics, metabolomics: a mass spectrometry tutorial from a computer scientist's point of view 
BMC Bioinformatics  2014;15(Suppl 7):S9.
Background
For decades, mass spectrometry data has been analyzed to investigate a wide array of research interests, including disease diagnostics, biological and chemical theory, genomics, and drug development. Progress towards solving any of these disparate problems depends upon overcoming the common challenge of interpreting the large data sets generated. Despite interim successes, many data interpretation problems in mass spectrometry are still challenging. Further, though these challenges are inherently interdisciplinary in nature, the significant domain-specific knowledge gap between disciplines makes interdisciplinary contributions difficult.
Results
This paper provides an introduction to the burgeoning field of computational mass spectrometry. We illustrate key concepts, vocabulary, and open problems in MS-omics, as well as provide invaluable resources such as open data sets and key search terms and references.
Conclusions
This paper will facilitate contributions from mathematicians, computer scientists, and statisticians to MS-omics that will fundamentally improve results over existing approaches and inform novel algorithmic solutions to open problems.
doi:10.1186/1471-2105-15-S7-S9
PMCID: PMC4110734  PMID: 25078324
3.  Rubabel: wrapping open Babel with Ruby 
Background
The number and diversity of wrappers for chemoinformatic toolkits suggests the diverse needs of the chemoinformatic community. While existing chemoinformatics libraries provide a broad range of utilities, many chemoinformaticians find compiled language libraries intimidating, time-consuming, arcane, and verbose. Although high-level language wrappers have been implemented, more can be done to leverage the intuitiveness of object-orientation, the paradigms of high-level languages, and the extensibility of languages such as Ruby. We introduce Rubabel, an intuitive, object-oriented suite of functionality that substantially increases the accessibily of the tools in the Open Babel chemoinformatics library.
Results
Rubabel requires fewer lines of code than any other actively developed wrapper, providing better object organization and navigation, and more intuitive object behavior than extant solutions. Moreover, Rubabel provides a convenient interface to the many extensions currently available in Ruby, greatly streamlining otherwise onerous tasks such as creating web applications that serve up Rubabel functionality.
Conclusions
Rubabel is powerful, intuitive, concise, freely available, cross-platform, and easy to install. We expect it to be a platform of choice for new users, Ruby users, and some users of current solutions.
doi:10.1186/1758-2946-5-35
PMCID: PMC3733738  PMID: 23883475
Chemoinformatics; Open Babel; Ruby
4.  Sequence and Structural Characterization of Great Salt Lake Bacteriophage CW02, a Member of the T7-Like Supergroup 
Journal of Virology  2012;86(15):7907-7917.
Halophage CW02 infects a Salinivibrio costicola-like bacterium, SA50, isolated from the Great Salt Lake. Following isolation, cultivation, and purification, CW02 was characterized by DNA sequencing, mass spectrometry, and electron microscopy. A conserved module of structural genes places CW02 in the T7 supergroup, members of which are found in diverse aquatic environments, including marine and freshwater ecosystems. CW02 has morphological similarities to viruses of the Podoviridae family. The structure of CW02, solved by cryogenic electron microscopy and three-dimensional reconstruction, enabled the fitting of a portion of the bacteriophage HK97 capsid protein into CW02 capsid density, thereby providing additional evidence that capsid proteins of tailed double-stranded DNA phages have a conserved fold. The CW02 capsid consists of bacteriophage lambda gpD-like densities that likely contribute to particle stability. Turret-like densities were found on icosahedral vertices and may represent a unique adaptation similar to what has been seen in other extremophilic viruses that infect archaea, such as Sulfolobus turreted icosahedral virus and halophage SH1.
doi:10.1128/JVI.00407-12
PMCID: PMC3421657  PMID: 22593163
5.  AICAR inhibits ceramide biosynthesis in skeletal muscle 
Background
The worldwide prevalence of obesity has lead to increased efforts to find therapies to treat obesity-related pathologies. Ceramide is a well-established mediator of several health problems that arise from adipose tissue expansion. The purpose of this study was to determine whether AICAR, an AMPK-activating drug, selectively reduces skeletal muscle ceramide synthesis.
Methods
Murine myotubes and rats were challenged with palmitate and high-fat diet, respectively, to induce ceramide accrual, in the absence or presence of AICAR. Transcript levels of the rate-limiting enzyme in ceramide biosynthesis, serine palmitoyltransferase 2 (SPT2) were measured, in addition to lipid analysis. Student’s t-test and ANOVA were used to assess the association between outcomes and groups.
Results
Palmitate alone induced an increase in serine palmitoyltransferase 2 (SPT2) expression and an elevation of ceramide levels in myotubes. Co-incubation with palmitate and AICAR prevented both effects. However, ceramide and SPT2 increased with the addition of compound C, an AMPK inhibitor. In rats fed a high-fat diet (HFD), soleus SPT2 expression increased compared with normal chow-fed littermates. Moreover, rats on HFD that received daily AICAR injections had lower SPT2 levels and reduced muscle ceramide content compared with those on HFD only.
Conclusions
These results suggest that AICAR reduces ceramide synthesis by targeting SPT2 transcription, likely via AMPK activation as AMPK inhibition prevented the AICAR-induced improvements. Given the role of skeletal muscle ceramide in insulin resistance, it is tempting to speculate that interventions that activate AMPK may lead to long-term ceramide reduction and improved metabolic function.
doi:10.1186/1758-5996-4-45
PMCID: PMC3514253  PMID: 23134616
AICAR; Ceramide; AMPK; Obesity; Metabolic syndrome
6.  Integrating shotgun proteomics and mRNA expression data to improve protein identification 
Bioinformatics  2009;25(11):1397-1403.
Motivation: Tandem mass spectrometry (MS/MS) offers fast and reliable characterization of complex protein mixtures, but suffers from low sensitivity in protein identification. In a typical shotgun proteomics experiment, it is assumed that all proteins are equally likely to be present. However, there is often other information available, e.g. the probability of a protein's presence is likely to correlate with its mRNA concentration.
Results: We develop a Bayesian score that estimates the posterior probability of a protein's presence in the sample given its identification in an MS/MS experiment and its mRNA concentration measured under similar experimental conditions. Our method, MSpresso, substantially increases the number of proteins identified in an MS/MS experiment at the same error rate, e.g. in yeast, MSpresso increases the number of proteins identified by ∼40%. We apply MSpresso to data from different MS/MS instruments, experimental conditions and organisms (Escherichia coli, human), and predict 19–63% more proteins across the different datasets. MSpresso demonstrates that incorporating prior knowledge of protein presence into shotgun proteomics experiments can substantially improve protein identification scores.
Availability and Implementation: Software is available upon request from the authors. Mass spectrometry datasets and supplementary information are available from http://www.marcottelab.org/MSpresso/.
Contact: marcotte@icmb.utexas.edu; miranker@cs.utexas.edu
Supplementary Information: Supplementary data website: http://www.marcottelab.org/MSpresso/.
doi:10.1093/bioinformatics/btp168
PMCID: PMC2682515  PMID: 19318424
7.  mspire: mass spectrometry proteomics in Ruby 
Bioinformatics  2008;24(23):2796-2797.
Summary: Mass spectrometry-based proteomics stands to gain from additional analysis of its data, but its large, complex datasets make demands on speed and memory usage requiring special consideration from scripting languages. The software library ‘mspire’—developed in the Ruby programming language—offers quick and memory-efficient readers for standard xml proteomics formats, converters for intermediate file types in typical proteomics spectral-identification work flows (including the Bioworks .srf format), and modules for the calculation of peptide false identification rates.
Availability: Freely available at http://mspire.rubyforge.org. Additional data models, usage information, and methods available at http://bioinformatics.icmb.utexas.edu/mspire
Contact: marcotte@icmb.utexas.edu
doi:10.1093/bioinformatics/btn513
PMCID: PMC2639276  PMID: 18930952
9.  Press Publicity 
British Medical Journal  1959;2(5159):1102.
PMCID: PMC1990860
10.  Press Publicity 
British Medical Journal  1954;1(4855):216.
PMCID: PMC2093195
11.  Press Publicity 
British Medical Journal  1954;1(4852):43.
PMCID: PMC2093100

Results 1-11 (11)