Improved prediction of peptide detectability for targeted proteomics using a rank-based algorithm and organism-specific data.

Unlabelled: The in silico prediction of the best-observable "proteotypic" peptides in mass spectrometry-based workflows is a challenging problem. Being able to accurately predict such peptides would enable the informed selection of proteotypic peptides for targeted quantification of previously observed and non-observed proteins for any organism, with a significant impact for clinical proteomics and systems biology studies. Current prediction algorithms rely on physicochemical parameters in combination with positive and negative training sets to identify those peptide properties that most profoundly affect their general detectability.

A mighty small heart: the cardiac proteome of adult Drosophila melanogaster.

Drosophila melanogaster is emerging as a powerful model system for the study of cardiac disease. Establishing peptide and protein maps of the Drosophila heart is central to implementation of protein network studies that will allow us to assess the hallmarks of Drosophila heart pathogenesis and gauge the degree of conservation with human disease mechanisms on a systems level. Using a gel-LC-MS/MS approach, we identified 1228 protein clusters from 145 dissected adult fly hearts.

Generating and navigating proteome maps using mass spectrometry.

Proteomes, the ensembles of all proteins expressed by cells or tissues, are typically analysed by mass spectrometry. Recent technical and computational advances have greatly increased the fraction of a proteome that can be identified and quantified in a single study. Current mass spectrometry-based proteomic strategies have the potential to reproducibly, accurately, quantitatively and comprehensively measure any protein or whole proteomes from cells and tissues at different states.

Protein and gene model inference based on statistical modeling in k-partite graphs.

One of the major goals of proteomics is the comprehensive and accurate description of a proteome. Shotgun proteomics, the method of choice for the analysis of complex protein mixtures, requires that experimentally observed peptides are mapped back to the proteins they were derived from. This process is also known as protein inference.

An integrated proteomics and transcriptomics reference data set provides new insights into the Bradyrhizobium japonicum bacteroid metabolism in soybean root nodules.

Bradyrhizobium japonicum, a gram-negative soil bacterium that establishes an N(2)-fixing symbiosis with its legume host soybean (Glycine max), has been used as a symbiosis model system. Using a sensitive geLC-MS/MS proteomics approach, we report the identification of 2315 B. japonicum strain USDA110 proteins (27.

Identification and functional characterization of N-terminally acetylated proteins in Drosophila melanogaster.

Protein modifications play a major role for most biological processes in living organisms. Amino-terminal acetylation of proteins is a common modification found throughout the tree of life: the N-terminus of a nascent polypeptide chain becomes co-translationally acetylated, often after the removal of the initiating methionine residue. While the enzymes and protein complexes involved in these processes have been extensively studied, only little is known about the biological function of such N-terminal modification events.

Deterministic protein inference for shotgun proteomics data provides new insights into Arabidopsis pollen development and function.

Pollen, the male gametophyte of flowering plants, represents an ideal biological system to study developmental processes, such as cell polarity, tip growth, and morphogenesis. Upon hydration, the metabolically quiescent pollen rapidly switches to an active state, exhibiting extremely fast growth. This rapid switch requires relevant proteins to be stored in the mature pollen, where they have to retain functionality in a desiccated environment.

Visualizing regulatory interactions in metabolic networks.

Background: Direct visualization of data sets in the context of biochemical network drawings is one of the most appealing approaches in the field of data evaluation within systems biology. One important type of information that is very helpful in interpreting and understanding metabolic networks has been overlooked so far. Here we focus on the representation of this type of information given by the strength of regulatory interactions between metabolite pools and reaction steps.