Current Proteomics - Volume 2, Issue 3, 2005

Proteomic analysis does inherently involve the handling and interpretation of a huge amount of data. Originally, proteome analysis aimed at collecting comprehensive information on all proteins that are present in a specified sample. Proteome inventories developed to be highly sophisticated databases collecting all different kinds of data formats, including two-dimensional gel images, mass spectra, protein sequences, and post-translational modifications. These databases lay the foundations for identifying proteins in proteomic studies aiming to find differentially expressed proteins, thus promoting proteomics from constructing "descriptive" databases to the design of "functional" experiments. With the quest for finding differentially expressed proteins, data have to be compared between two or more experimental groups. Therefore, a new field of bioinformatic tools had to be developed for the proteomic high-throughput technologies, or these tools had to be adapted from other applications, such as genomic and transcriptomic analysis based on nucleotide microarrays. In this review, the strategies for comparing protein concentration and functional activity by different statistical means, as well as the methodology of comparing whole sets of genes or proteins have been evaluated. Comments on the statistical algorithms incorporated in 2D gel analysis software, and discussions on alternatives for data comparison have been incorporated. The use of supervised and unsupervised data analysis and its application in proteomic experiments, including the use of hierarchical clustering for identification of functional pathways in proteome analysis have also been reviewed.

Analysis of peptides and proteins has become an important component of doping control laboratories. Several peptide hormones such as insulin, insulin-like growth factor-I, growth hormone, erythropoietin, and hemoglobin-based oxygen carriers are considered to possess an enormous potential to artificially increase athletic performance and belong to the list of prohibited compounds and methods established by regulatory authorities such as the World Anti-Doping Agency (WADA). In order to reveal abuse of those drugs in professional as well as amateur sports, doping control laboratories have been developing various strategies to identify target analytes in blood or urine specimens employing different biochemical techniques such as immunoaffinity purification, isoelectric focusing, gel electrophoresis, double-blotting as well as concomitant top-down and bottom-up mass spectrometry based proteomic approaches. These enable the qualitative determination of derivatives of naturally occurring peptides and proteins such as insulin and hemoglobin as well as possibilities to distinguish between endogenously produced and presumably identical recombinant proteins such as growth hormone and erythropoietin. Most applied strategies are common proteomics procedures, but they have been modified to meet the specific requirements and limitations of doping control, i.e. type of specimens, available amounts, required specificity and sensitivity, unambiguousness of results, and speed of analysis.

In the post-genomic era, the study of G-protein coupled receptor (GPCR)-mediated signal transduction has taken a complicated turn, fueled by the discovery that individual GPCRs are organized within a supramolecular signaling complex termed the signalplex. It has now become clear that a vast amount of cellular information is transmitted via the activity of these multiprotein signaling complexes. In turn, the detailed characterization of several signalplexes has led to a critical re-evaluation of the mechanisms underlying the activation and selectivity of GPCR-mediated signaling within cells. This review examines the role of protein-protein interactions in D2 dopamine receptor (D2R) signaling within the brain. Based on studies utilizing yeast two-hybrid, proteomic, and cell biochemical approaches, the known direct and indirect interactions between D2 receptors and an array of cellular proteins which functionally can be subdivided into scaffolding, cytoskeletal, signaling, receptor, and ion channel molecules, have been summarized. Interactions between signalplex components are found to establish and maintain key aspects of receptor function including the trafficking and assembly of dopamine receptors within various cellular compartments. Understanding the molecular complexity of the D2R signalplex provides a new platform for defining the cellular mechanisms of dopamine signaling in the brain as well as the development of novel drugs for antipsychotic and antiparkinsonian therapy.

The yeast two-hybrid assay has proved a powerful tool in identifying and characterising binary protein-protein interactions. Not only can it be used to map interacting protein domains, it can also be used to screen cDNA libraries with a desired bait to identify novel binding partners. A number of factors including ease of use, cost effectiveness and suitability for high throughput analysis have made yeast-two hybrid one of the assays of choice for defining protein-protein interaction networks or interactomes for a range of organisms. The focus of this review is on the definition of viral interactomes using the yeast two-hybrid assay and the relevance of such studies to our understanding of viral pathogenesis.

Deciphering of the human genome and other model genomes presents the challenge of unraveling the gene products that are expressed by these genomes, and identifying the functional role of the expressed proteins. Rapid determination of the three-dimensional (3D) structures of the gene products is vital in this process and is the focus of various Structural Genomics initiatives around the world. However, determination of the structures of thousands of new proteins by experimental methods, such as X-ray crystallography and NMR remains a formidable task. Here we review a novel approach using an Augmented Homology Modeling technology termed "ProMax" to provide high-quality 3D protein structure from protein sequence data, rigorously assessed by comparison of the Root Mean Square Deviation (RMSD) of Cα carbons in modeled versus subsequently determined X-ray structures, normalized residue energies, and Ramachandran analyses. The modeling procedure starts with determination of the appropriate template 3D structures and proceeds from initial structure generation through multiple iterative energy-based structure refinements assessed against an elaborate panel of 3D structure quality assessment tests. The method is general, applicable to a broad cross-section of protein families, and reasonably accurate even for protein families with low sequence homology to available template structures. The Augmented Homology Modeling approach was tested on various target sequences proposed for the Critical Assessment of techniques for protein Structure Prediction (CASP) competitions: CASP3, CASP4 and CASP5. Also, more than 40 models derived with the "ProMax" approach were compared with X-ray and NMR structures released in the Protein Data Bank after the models were built. The comparison showed good agreement between our models and experimental structures within the core Cα atom RMSD < 2.0 Å, and with a stereochemical quality of the models approaching that of experimental structures. While this method is not a replacement for experimental methods such as X-ray crystallography, it is highly useful to derive 3D structures of protein homologues within or across genomes as a first approximation, with the accuracy and quality sufficient to use these models in rational experimental projects involving protein engineering, mutagenesis design, virtual screening and docking simulations.