Proteomics

Post‐translational modifications (PTMs) occur on almost all proteins analyzed to date. The function of a modified protein is often strongly affected by these modifications and therefore increased knowledge about the potential PTMs of a target protein may increase our understanding of the molecular processes in which it takes part. High‐throughput methods for the identification of PTMs are being developed, in particular within the fields of proteomics and mass spectrometry. However, these methods are still in their early stages, and it is indeed advantageous to cut down on the number of experimental steps by integrating computational approaches into the validation procedures. Many advanced methods for the prediction of PTMs exist and many are made publicly available. We describe our experiences with the development of prediction methods for phosphorylation and glycosylation sites and the development of PTM‐specific databases. In addition, we discuss novel ideas for PTM visualization (exemplified by kinase landscapes) and improvements for prediction specificity (by using ESS – evolutionary stable sites). As an example, we present a new method for kinase‐specific prediction of phosphorylation sites, NetPhosK, which extends our earlier and more general tool, NetPhos. The new server, NetPhosK, is made publicly available at the URL http://www.cbs.dtu.dk/services/NetPhosK/. The issues of underestimation, over‐prediction and strategies for improving prediction specificity are also discussed.

Two‐dimensional gel electrophoresis (2‐DE) with immobilized pH gradients (IPGs) combined with protein identification by mass spectrometry (MS) is currently the workhorse for proteomics. In spite of promising alternative or complementary technologies (e.g.multidimensional protein identification technology, stable isotope labelling, protein or antibody arrays) that have emerged recently, 2‐DE is currently the only technique that can be routinely applied for parallel quantitative expression profiling of large sets of complex protein mixtures such as whole cell lysates. 2‐DE enables the separaration of complex mixtures of proteins according to isoelectric point (pI), molecular mass (M_r), solubility, and relative abundance. Furthermore, it delivers a map of intact proteins, which reflects changes in protein expression level, isoforms or post‐translational modifications. This is in contrast to liquid chromatography‐tandem mass spectrometry based methods, which perform analysis on peptides, whereM_rand pIinformation is lost, and where stable isotope labelling is required for quantitative analysis. Today's 2‐DE technology with IPGs (Görget al.,Electrophoresis2000,21, 1037–1053), has overcome the former limitations of carrier ampholyte based 2‐DE (O'Farrell,J. Biol. Chem. 1975,250, 4007–4021) with respect to reproducibility, handling, resolution, and separation of very acidic and/or basic proteins. The development of IPGs between pH 2.5–12 has enabled the analysis of very alkaline proteins and the construction of the corresponding databases. Narrow‐overlapping IPGs provide increased resolution (δpI = 0.001) and, in combination with prefractionation methods, the detection of low abundance proteins. Depending on the gel size and pH gradient used, 2‐DE can resolve more than 5000 proteins simultaneously (˜2000 proteins routinely), and detect and quantify < 1 ng of proteinperspot. In this article we describe the current 2‐DE/MS workflow including the following topics: sample preparation, protein solubilization, and prefractionation; protein separation by 2‐DE with IPGs; protein detection and quantitation; computer assisted analysis of 2‐DE patterns; protein identification and characterization by MS; two‐dimensional protein databases.

Despite the complete determination of the genome sequence of several higher eukaryotes, their proteomes remain relatively poorly defined. Information about proteins identified by different experimental and computational methods is stored in different databases, meaning that no single resource offers full coverage of known and predicted proteins. IPI (the International Protein Index) has been developed to address these issues and offers complete nonredundant data sets representing the human, mouse and rat proteomes, built from the Swiss‐Prot, TrEMBL, Ensembl and RefSeq databases.

Oxygen is necessary for aerobic metabolism but can cause the harmful oxidation of lipids and other macromolecules. Oxidation of cholesterol and phospholipids containing polyunsaturated fatty acyl chains can lead to lipid peroxidation, membrane damage, and cell death. Lipid hydroperoxides are key intermediates in the process of lipid peroxidation. The lipid hydroperoxidase glutathione peroxidase 4 (GPX4) converts lipid hydroperoxides to lipid alcohols, and this process prevents the iron (Fe²⁺)‐dependent formation of toxic lipid reactive oxygen species (ROS). Inhibition of GPX4 function leads to lipid peroxidation and can result in the induction of ferroptosis, an iron‐dependent, non‐apoptotic form of cell death. This review describes the formation of reactive lipid species, the function of GPX4 in preventing oxidative lipid damage, and the link between GPX4 dysfunction, lipid oxidation, and the induction of ferroptosis.

The advent of high‐throughput proteomics has enabled the identification of ever increasing numbers of proteins. Correspondingly, the number of publications centered on these protein identifications has increased dramatically. With the first results of the HUPO Plasma Proteome Project being analyzed and many other large‐scale proteomics projects about to disseminate their data, this trend is not likely to flatten out any time soon. However, the publication mechanism of these identified proteins has lagged behind in technical terms. Often very long lists of identifications are either published directly with the article, resulting in both a voluminous and rather tedious read, or are included on the publisher's website as supplementary information. In either case, these lists are typically only provided as portable document format documents with a custom‐made layout, making it practically impossible for computer programs to interpret them, let alone efficiently query them. Here we propose the proteomics identifications (PRIDE) database (http://www.ebi.ac.uk/pride) as a means to finally turn publicly available data into publicly accessible data. PRIDE offers a web‐based query interface, a user‐friendly data upload facility, and a documented application programming interface for direct computational access. The complete PRIDE database, source code, data, and support tools are freely available for web access or download and local installation.

The ability to sequence whole genomes has taught us that our knowledge with respect to gene function is rather limited with typically 30–40% of open reading frames having no known function. Thus, within the life sciences there is a need for determination of the biological function of these so‐called orphan genes, some of which may be molecular targets for therapeutic intervention. The search for specific mRNA, proteins, or metabolites that can serve as diagnostic markers has also increased, as has the fact that these biomarkers may be useful in following and predicting disease progression or response to therapy. Functional analyses have become increasingly popular. They include investigations at the level of gene expression (transcriptomics), protein translation (proteomics) and more recently the metabolite network (metabolomics). This article provides an overview of metabolomics and discusses its complementary role with transcriptomics and proteomics, and within system biology. It highlights how metabolome analyses are conducted and how the highly complex data that are generated are analysed. Non‐invasive footprinting analysis is also discussed as this has many applications toin vitrocell systems. Finally, for studying biotic or abiotic stresses on animals, plants or microbes, we believe that metabolomics could very easily be applied to large populations, because this approach tends to be of higher throughput and generally lower cost than transcriptomics and proteomics, whilst also providing indications of which area of metabolism may be affected by external perturbation.

A novel database search algorithm is presented for the qualitative identification of proteins over a wide dynamic range, both in simple and complex biological samples. The algorithm has been designed for the analysis of data originating from data independent acquisitions, whereby multiple precursor ions are fragmented simultaneously. Measurements used by the algorithm include retention time, ion intensities, charge state, and accurate masses on both precursor and product ions from LC‐MS data. The search algorithm uses an iterative process whereby each iteration incrementally increases the selectivity, specificity, and sensitivity of the overall strategy. Increased specificity is obtained by utilizing a subset database search approach, whereby for each subsequent stage of the search, only those peptides from securely identified proteins are queried. Tentative peptide and protein identifications are ranked and scored by their relative correlation to a number of models of known and empirically derived physicochemical attributes of proteins and peptides. In addition, the algorithm utilizes decoy database techniques for automatically determining the false positive identification rates. The search algorithm has been tested by comparing the search results from a four‐protein mixture, the same four‐protein mixture spiked into a complex biological background, and a variety of other “system” type protein digest mixtures. The method was validated independently by data dependent methods, while concurrently relying on replication and selectivity. Comparisons were also performed with other commercially and publicly available peptide fragmentation search algorithms. The presented results demonstrate the ability to correctly identify peptides and proteins from data independent acquisition strategies with high sensitivity and specificity. They also illustrate a more comprehensive analysis of the samples studied; providing approximately 20% more protein identifications, compared to a more conventional data directed approach using the same identification criteria, with a concurrent increase in both sequence coverage and the number of modified peptides.

Systematic parallel analysis of the phosphorylation status of networks of interacting proteins involved in the regulatory circuitry of cells and tissues is certain to drive research in the post‐genomics era for many years to come. Reversible protein phosphorylation plays a critical regulatory role in a multitude of cellular processes, including alterations in signal transduction pathways related to oncogene and tumor suppressor gene products in cancer. While fluorescence detection methods are likely to offer the best solution to global protein quantitation in proteomics, to date, there has been no satisfactory method for the specific and reversible fluorescent detection of gel‐separated phosphoproteins from complex samples. The newly developed Pro‐Q Diamond phosphoprotein dye technology is suitable for the fluorescent detection of phosphoserine‐, phosphothreonine‐, and phosphotyrosine‐containing proteins directly in sodium dodecyl sulfate (SDS)‐polyacrylamide gels and two‐dimensional (2‐D) gels. Additionally, the technology is appropriate for the determination of protein kinase and phosphatase substrate preference. Other macromolecules, such as DNA, RNA, and sulfated glycans, fail to be detected with Pro‐Q Diamond dye. The staining procedure is rapid, simple to perform, readily reversible and fully compatible with modern microchemical analysis procedures, such as matrix‐assisted laser desorption/ionization‐time of flight (MALDI‐TOF) mass spectrometry. Pro‐Q Diamond dye technology can detect as little as 1–2 ng of β‐casein, a pentaphosphorylated protein, and 8 ng of pepsin, a monophosphorylated protein. Fluorescence signal intensity correlates with the number of phosphorylated residues on the protein. Through combination of Pro‐Q Diamond phosphoprotein stain with SYPRO® Ruby protein gel stain, Multiplexed Proteomics technology permits quantitative, dichromatic fluorescence detection of proteins in 2‐D gels. This evolving discovery platform allows the parallel determination of protein expression level changes and altered post‐translational modification patterns within a single 2‐D gel experiment. The linear responses of the fluorescence dyes utilized, allow rigorous quantitation of changes over an unprecedented 500–1000‐fold concentration range.

Two‐dimensional difference gel electrophoresis (2‐D DIGE) coupled with mass spectrometry (MS) was used to investigate tumor‐specific changes in the proteome of human colorectal cancers and adjacent normal mucosa. For each of six patients with different stages of colon cancer, Cy5‐labeled proteins isolated from tumor tissue were combined with Cy3‐labeled proteins isolated from neighboring normal mucosa and separated on the same 2‐D gel along with a Cy2‐labeled mixture of all 12 normal/tumor samples as an internal standard. Over 1500 protein spot‐features were analyzed in each paired normal/tumor comparison, and using DIGE technology with the mixed‐sample internal standard, statistically significant quantitative comparisons of each protein abundance change could be made across multiple samples simultaneously without interference due to gel‐to‐gel variation. Matrix‐assisted laser desorption/ionization‐time of flight (MALDI‐TOF) and tandem (TOF/TOF) MS provided sensitive and accurate mass spectral data for database interrogation, resulting in the identification of 52 unique proteins (including redundancies due to proteolysis and post‐translationally modified isoforms) that were changing in abundance across the cohort. Without the benefit of the Cy2‐labeled 12 sample mixture internal standard, 42 of these proteins would have been overlooked due to the large degree of variation inherent between normal and tumor samples.

Unimod is a database of protein modifications for use in mass spectrometry applications, especially protein identification and de novo sequencing. It contains accurate and verifiable values, derived from elemental compositions, for the mass differences introduced by both natural and artificial modifications.