Showing papers in "Current Proteomics in 2005"

Molecular Beacon Aptamers for Protein Monitoring in Real-Time and in Homogeneous Solutions

Abstract: Aptamers are nucleic acids selected for binding target molecules of interest with high affinity and selectivity. They have seen increasing application in protein detection due to many of their advantages over traditional protein probes such as antibodies. Aptamers' robust yet flexible functional structures and relatively small sizes have allowed us to develop several strategies for sensitive protein detection in real time and in homogeneous solutions while posing minimum effects on the biological activities of the proteins. Quantitative protein analyses were done using aptamers labeled with a fluorophore and a quencher based on fluorescence resonance energy transfer (FRET), or using aptamers labeled with only one fluorophore based on fluorescence anisotropy. Real world biological samples were tested for the presence of target proteins. We believe that aptamers hold great potential for high throughput protein analysis in areas such as disease diagnosis and functional proteomics.

Current Developments in the Analysis of Proteomic Data: Artificial Neural Network Data Mining Techniques for the Identification of Proteomic Biomarkers Related to Breast Cancer

Abstract: Artificial Neural Network (ANN) techniques are becoming increasing popular in many areas of the biological sciences for the analysis of complex data. Careful selection of key parameters when developing ANN models and algorithms is extremely important in order to create generalised models with real-world applicability. This study applies these approaches to the analysis of proteomic data generated using Surface Enhanced Laser Desorption/Ionisation mass spectrometry profiling of cell lines from patients with breast cancer. Examples of these approaches include constrained architecture, Correlated Activity Pruning (CAPing), appropriate training termination methods and other, more advanced methodologies such as parameterisat ion by weightings analysis and stepwise additive approaches. These approaches, when applied to breast cancer cell lines from actual patients, resulted in the identification of 8 protein/peptide molecular ions which were capable of classifying samples into their respective groups to an accuracy of 94.8 % with an area under the curve value of 0.993 when examined with a receiver operating characteristic curve. Several ions which appear to show a significant up or down-regulation with regards to treatment regimen have also been identified. These results indicate that when coupled with other powerful techniques, the development of these novel methodologies and algorithms using ANNs allows for the development of effective data mining tools in order to analyse complex, non-linear, noisy data. This paper will consider current methodologies for the analysis of proteomic data using Artificial Neural Network (ANN) based methodologies, their advantages, disadvan- tages and limitations, and then will describe an application of novel methodologies developed using actual patient data. ANN techniques have been widely applied to many areas of the physical sciences for the analysis of complex systems. As such, extensive knowledge exists on the application and limitations of these methods. Similarly, methodologies exist to overcome many of these limitations and enhance the pre- dictive capabilities and real-world applicability of developed models. This study applies these approaches to the analysis of proteomic data generated using Surface Enhanced Laser Desorption/Ionisation (SELDI) mass spectrometry (MS) profiling with the aim of identifying candidate biomarkers indicative of treatment regimen for chemosensitive (MCF-7 and T47-D) breast cancer cell lines, in order to develop ANN algorithms to correctly assign samples into their appropriate class of either control or drug treated. Examples of these approaches and important parameters which need to be considered when developing ANN models will be discussed, followed by methodologies employed in order to create generalised models with real- world applicability.

Drug Discovery Using Yeast as a Model System: A Functional Genomic and Proteomic View

Abstract: Drug discovery is a complex process that includes the identification of biological targets as well as the identification of leads that aim at altering or inhibiting the function of a particular target. The budding yeast Saccharomyces cerevisiae has long been recognized as a valuable model organism for studies of eukaryotic cells since many of the basic cellular processes between yeast and humans are highly conserved. In this review, we highlight emerging yeast-based functional genomic and proteomic technologies that are advancing the utility of yeast as a model organism in the drug-discovery process. These approaches include the utilization of yeast deletion strain collection, synthetic genetic array combined with chemical genomics, variations of the yeast two-hybrid system, yeast biosensor assay, and protein microarrays. Although still at an early stage, these technologies show promise as novel and useful methods for development of target-specific therapeutic approaches.

The Application of Proteomics in Neurology

Abstract: Rapidly progressing proteomics techniques have been widely adopted in most areas of biology and medicine. In neurology and neuroscience, many applications of proteomics have involved neurotoxicology and neurometabolism, as well as in the determination of specific proteomic aspects of individual brain areas and body fluids in neurodegeneration. Investigation of brain protein groups in neurodegeneration, such as enzymes, cytoskeleton proteins, chaperones, synaptosomal proteins and antioxidant proteins, is in progress as phenotype related proteomics. The concomitant detection of several hundred proteins on a gel provides sufficiently comprehensive data to determine a pathophysiological protein network and its peripheral representatives. The rapid spread of proteomics technology, which principally consists of twodimensional gel electrophoresis (2-DE) with in-gel protein digestion of protein spots and identification by massspectrometry, has provided an explosive amount of results. An additional advantage is that hitherto unknown proteins have been identified as brain proteins. The current proteomics methods, however, have shortcomings and disadvantages. We would emphasize the failure to separate hydrophobic proteins as a major problem. So far, we have been unable to analyze the vast majority of these proteins in gels on 2-DE. There are several other analytical problems which also need to be overcome, and once solved, will allow for a more comprehensive analysis of the individual disease process. Here, we have reviewed the recent progress in proteomics research on neurodegeneration, with reference to its technological utility and problems in clinical application.