Current Proteomics - Volume 5, Issue 4, 2008

Nuclear receptors (NRs) constitute one of the largest families of transcriptional regulators responding to certain physiological ligands, with 48 NRs identified in the human genome. Approximately one half of the members among this superfamily are orphan because they lack physiological ligands. Recent molecular and proteomic studies have uncovered extensive post-translational modifications (PTMs) on NRs, which can regulate the biological activities of many NRs. PTMs that exert functional consequences on NRs include phosphorylation, acetylation, ubquitination, SUMOylation and methylation. They affect receptor protein stability, ligand sensitivity, recruitment of co-regulators and sub-cellular distribution. Studies exploiting the biological effects induced by PTMs have begun to unravel novel functional roles for certain NRs. This is particularly significant for orphan NRs because their biological activities have been largely puzzling in the past. This review summarizes PTMs confirmed for two orphan NRs, Testis Receptor 2 (TR2) and 4 (TR4), and discusses the biological relevance of these PTMs.

Predicting protein structure and function from amino acid sequences is a central aim of bioinformatics. Most bioinformatics analyses use sequence alignment as the basis by which to measure similarity. However, there is increasing evidence that many protein families are resistant to this straightforward method of comparison. Increasingly, a combination of machine-learning techniques and abstract representations of protein sequences is being used to classify proteins based upon the similarity of their physico-chemical properties rather than scoring sequence alignments. This is particularly effective in protein families that show greater structural conservation but appear to lack conserved sequences. Here we describe the inherent limitations of the alignment-dependent approaches to protein classification and present ‘alignment- free’ representations as a viable and realistic alternative to solve complex problems within bioinformatics.

This article provides a focused perspective on the application of MALDI/SELDI mass spectrometry (MS)- based technologies to biomarker discovery and validation in various biological materials, which might eventually serve as the basis for new clinical tests to improve diagnosis, guide molecularly-targeted therapy, and monitor the activity and therapeutic response to various cancers. However, the contribution of MALDI/SELDI MS to these goals has been scant due partially to current unavailability of robust analytical methods and instruments to overcome the finite complexity of the proteome and peptidome. Moreover, with a coherent pipeline, the successful throughput from marker discovery to robust validation methods has been dismal. While the development of a faster and more sensitive MS methods will have a major impact on the ability to identify thousands of proteins in body fluids, improvement in sample preparation methods such as high abundance protein depletion and chromatography, and better sample processing methods have also played key roles. Further optimization of MS-based methods is expected to further improve their analytical performance. Because of the guarded skepticism voiced by several investigators about the ultimate value of the entire MS-based proteomics approach for biomarker discovery in complicated samples such as blood, exhaustive experimental methods that use multiple reaction monitoring targeted proteomic approaches, which include immunoassays coupled with MS spectrometry techniques need to be developed and standardized, initially for less complex body fluids, tissues, or even cells or tissue explants in animal models and later on translated to blood, in order to avoid the pitfalls of bias and/or overfitting that plagued earlier studies, and it is still hoped that eventually proteomic profiling in complex biological samples will become a fulfilled promise rather than just an unrealized hope for the scientific and clinical community.

Proteomics and bioinformatics approach were applied for the analyzing of wheat leaf proteins' composition and function. Wheat proteins were precipitated by ammonium sulfate and analyzed by two-dimensional gel electrophoresis and mass spectrometry. A total of 200 wheat proteins were selected to identify based on reproducibility and relative quantity, and 123 proteins were identified with an identification success rate of 61.5 ;. The classifications of these proteins by BGSSJ (bioinformatic software) were mainly classified by their molecular, biological and cellular function. Proteins grouped under the molecular function were involved in catalytic, binding and antioxidant activity. The catalytic activity of identified wheat proteins included oxidoreductase, transferase, hydrolase, lyase and isomerase. Only 10.6 &percnt of the wheat protein identifications lacked ascertainable functions. These results provided the information to investigate the composition and function of proteins found in wheat leaf, and enhanced the feasibility of future research on wheat.

Amino acid sequence database is one of the essential components in the current proteomics with mass spectrometry. Protein identification routine as well as posttranslational modification analysis is based on correlation between the mass spectrometry data of peptides obtained from proteome and the entry sequences in the database. While different sequence databases are available from public resources for the correlation search, these primary sequence data can be processed into more useful forms. In alteration of the unmodified precursor sequences according to their biological processes and variations, the altered sequence database allows to output the search results on the matured polypeptides. Modification of the composition in sequence database would be practically beneficial to the wide range from the detail analysis focusing on a single protein to the biomarker discovery studies for the clinical utility.