BSP :: Current Proteomics Home Page

Contents

Drug Discovery Using Yeast as a Model System: A Functional Genomic and Proteomic View Pp. 1-13
Daniel Auerbach, Anthony Arnoldo, Boris Bogdan, Michael Fetchko and Igor Stagljar
[Abstract] [Full text article]

Current Developments in the Analysis of Proteomic Data: Artificial Neural Network Data Mining Techniques for the Identification of Proteomic Biomarkers Related to Breast Cancer Pp. 15-29
Lee J. Lancashire, Shahid Mian, Ian O. Ellis, Robert C. Rees and Graham R. Ball
[Abstract] [Full text article]

Molecular Beacon Aptamers for Protein Monitoring in Real-Time and in Homogeneous Solutions Pp. 31-40
Zehui Cao, Steven W. Suljak and Weihong Tan
[Abstract] [Full text article]

The Application of Proteomics in Neurology Pp. 41-53
TeruyukiTsuji, Aiko Shiozaki and Shun Shimohama
[Abstract] [Full text article]

Measurements of the Forces in Protein Interactions with Atomic Force Microscopy Pp. 55-81
Shiming Lin, Ji-Liang Chen, Long-Sun Huang and Huan-We Lin
[Abstract] [Full text article]

Meeting Report:The Fourth Annual PepTalk Meeting: The Human Proteome Pp. 83-86
Sean R. Gallagher
[Abstract] [Full text article]

Abstracts

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Drug Discovery Using Yeast as a Model System: A Functional Genomic and Proteomic View
Daniel Auerbach, Anthony Arnoldo, Boris Bogdan, Michael Fetchko and Igor Stagljar
[Full text article]

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.

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Current Developments in the Analysis of Proteomic Data: Artificial Neural Network Data Mining Techniques for the Identification of Proteomic Biomarkers Related to Breast Cancer
Lee J. Lancashire, Shahid Mian, Ian O. Ellis, Robert C. Rees and Graham R. Ball
[Full text article]

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 parameterisation 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.

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Molecular Beacon Aptamers for Protein Monitoring in Real-Time and in Homogeneous Solutions
Zehui Cao, Steven W. Suljak and Weihong Tan
[Full text article]

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.

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The Application of Proteomics in Neurology
Teruyuki Tsuji, Aiko Shiozaki and Shun Shimohama
[Full text article]

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 two-dimensional gel electrophoresis (2-DE) with in-gel protein digestion of protein spots and identification by mass-spectrometry, 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.

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Measurements of the Forces in Protein Interactions with Atomic Force Microscopy
Shiming Lin, Ji-Liang Chen, Long-Sun Huang and Huan-We Lin
[Full text article]

Protein interactions with ligands or other proteins are controlled by a complex array of intermolecular forces. Although the interaction energies and intermolecular forces which contribute to the stabilization of the protein complex can be inferred indirectly from thermodynamic and kinetic approaches or be calculated with molecular simulation, recent progress in atomic force microscopy (AFM) has made it possible to quantify directly the ranges and magnitudes of the interaction forces between protein and other molecules. AFM has proved its value not only for resolving the topographical structure of protein samples, but also for probing the forces that control protein interactions or mechanical properties of proteins under physiological conditions. The objective of this review is to describe the uses of AFM in the determination of the forces that control biological interactions, focusing especially on protein-ligand and protein-protein interaction modes. We first consider measurements of the specific and the nonspecific forces that jointly control protein interactions. The review then indicates the theoretical background of AFM force curves and presents the great variety of force measurement modes that can be performed with this technique. In addition, some of the most recent studies in determining the unbinding forces and mechanical properties of proteins with AFM are reviewed and the available theoretical aspects necessary for the comprehension of the experiments have been provided.

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Meeting Report:The Fourth Annual PepTalk Meeting: The Human Proteome
Sean R. Gallagher
[Full text article]

The Fourth Annual PepTalk meeting organized by the Cambridge Healthtech Institute on proteomics was held in San Diego, California from January 10 to 13, 2005. The entire meeting had an interesting name: Proteomics in a Six-Pack, and consisted of six individual meetings that ran for two days each, in two groups of concurrent sessions: (1) Fourth Annual Protein Arrays: Complex Challenges – Creative Solutions (January 10-11); (2) Eighth Annual Protein Expression (January 10-11); (3) Inaugural Protein Folding Disorders (January 10-11); (4) Second Annual Protein Process Development: Optimizing Protein Expression Through Scale-Up (January 12-13); (5) Fifth Annual The Human Proteome: Plasma Proteomics (January 12-13); and (6) Inaugural Protein Therapeutics: Minimizing Problems – Maximizing Production, Progress, and Potential (January 12-13). This was indeed what the organizers called the “protein information week”. This report summarizes The Human Proteome meeting, which ended with a joint closing plenary session with the Protein Process Development and the Protein Therapeutics meetings.