Current Proteomics - Volume 9, Issue 3, 2012

Mass spectrometry based proteomics allow us to analyze complex mixtures of proteins from various biological samples in a high-throughput manner, in order to identify important proteomic patterns and hopefully novel disease biomarkers. However, as most omics data, mass spectrometry proteomics data are complex, noisy and incomplete. Additionally, the data are usually represented by relatively few samples and a very large number of predictor variables, i.e., m/z peaks. These characteristics pose a significant challenge for most computational analysis methods and in recent literature various alternatives have been proposed. A typical mass spectrometry proteomics data analysis workflow consists of two major steps: preprocessing and higher level analysis. In the recent years, a wide range of algorithms have been proposed for both, varying from classical approaches to second generation algorithms. Many of the proposed algorithms have been reported to produce encouraging results. However, no common strategy has emerged as a method of choice and for each dataset different algorithms produce different results, making the evaluation of the algorithms practically impossible. This work provides a critical review of the recent approaches for both preprocessing and higher level analysis of proteomics data. The strengths and limitations of each method are also presented and emphasis is given on describing the most common and serious mistakes recorded in published differential proteomics studies. Moreover, the review provides guidance for choosing and correctly applying the appropriate algorithms according to our experience and hints for the design of novel algorithms, which will more effectively handle the specific characteristics and constrains of differential proteomics data.

Long-range contacts in protein structures were demonstrated to be predictive of different physiological properties of hemoglobin and albumin proteins. Complex networks based approach was demonstrated to highlight basic principles of protein folding and activity. The presence of a natural scaling region ending at an approximate threshold of 120-150 residues shared by proteins of different size and quaternary structure was highlighted. This threshold is reminiscent of the typical size for a macromolecule to have a binding site sensible to environmental regulation.

Protein phosphorylation is a key post-translational modification that controls intracellular signalling in virtually all cell types. In the nervous system, it contributes to the regulation of neuronal signalling, and processes underlying synaptic plasticity and cognitive functions. Despite its importance for brain functions, knowledge about the brain phosphoproteome has remained incomplete. A pre-requisite for gaining such knowledge and better understanding the molecular and biochemical bases of brain functions is to carry out quantitative analyses of protein phosphorylation and its dynamics. Such analyses require high-throughput methodologies, which in recent years, have greatly benefited from advances in proteomics and genomics, and have been combined with computational modelling. Current phosphoproteomic workflows have reached a level of maturity that permits their combination with molecular approaches, and their application to the study of higher-order brain functions and cognitive processes. Neuroproteomics has thus emerged as an important novel sub-field of neurosciences. This review focuses on the proteomic methodologies currently used to study phosphorylation in the brain, and recent examples of their application.

Within the nucleus, the nucleolus is a dynamic compartment which is critical to maintain cellular homeostasis under normal, stress and disease conditions. During the last years, proteomics research provided new information on the complexity of nucleolar proteomes. These studies also established that many chaperones, co-chaperones and other factors involved in proteostasis associate with nucleoli in the absence of stress or disease. Moreover, quantitative proteomics demonstrated that physiological and environmental changes alter the nucleolar profile of chaperones and co-chaperones. At present, the emphasis has shifted towards sophisticated in-depth analyses of the nucleolar proteome. As such, turnover and posttranslational modifications are now quantified for individual proteins that associate with nucleoli. This large body of work generated new insights into the sumoylation, phosphorylation and acetylation of the nucleolar proteome. At the same time, we have gained a better understanding of the nucleolar organization, as novel subcompartments were identified within the nucleolus that are induced by physiological and other forms of stress. Notably, some of these subcompartments are also enriched for chaperones. To review these results, we will focus on recent studies that analyzed the nucleolar proteome, and particular emphasis will be given to nucleolar chaperones. Despite remarkable progress in the field, crucial questions regarding the physiological relevance of nucleolar chaperones remain to be answered in the years ahead. We conclude our update by discussing these future directions in the context of the latest developments in the nucleolar and chaperone fields.