PubMed Health⌕ Search

Biomedical subjects

Daniel Figeys

Publications and source records attributed to Daniel Figeys.

25 records · Page 2Linked to original sources
PubMed2002

Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry.

The recent abundance of genome sequence data has brought an urgent need for systematic proteomics to decipher the encoded protein networks that dictate cellular function. To date, generation of large-scale protein-protein interaction maps has relied on the yeast two-hybrid system, which detects binary interactions through activation of reporter gene expression. With the advent of ultrasensitive mass spectrometric protein identification methods, it is feasible to identify directly protein complexes on a proteome-wide scale. Here we report, using the budding yeast Saccharomyces cerevisiae as a test case, an example of this approach, which we term high-throughput mass spectrometric protein complex identification (HMS-PCI). Beginning with 10% of predicted yeast proteins as baits, we detected 3,617 associated proteins covering 25% of the yeast proteome. Numerous protein complexes were identified, including many new interactions in various signalling pathways and in the DNA damage response. Comparison of the HMS-PCI data set with interactions reported in the literature revealed an average threefold higher success rate in detection of known complexes compared with large-scale two-hybrid studies. Given the high degree of connectivity observed in this study, even partial HMS-PCI coverage of complex proteomes, including that of humans, should allow comprehensive identification of cellular networks.

Amino Acid Sequence↗
PubMed2002

Adapting arrays and lab-on-a-chip technology for proteomics.

The impact of proteomics as a discovery engine in life science and in drug discovery has increased tremendously over the last seven years. At the same time, proteomics has expanded from the initial trust as a two-dimensional gel based approach to cover more functional and structural properties of proteins. The development of lab-on-a-chip and protein arrays for proteomics will have to evolve with the changes in proteomics to stay relevant. Here, we review the changes in the field of proteomics and their impact on the development in protein arrays and lab-on-a-chip.

Antibodies↗
PubMed2002

Functional proteomics: mapping protein-protein interactions and pathways.

The function of a protein is defined by its interactions with other proteins and molecules. Mapping of protein interactions can highlight new functionalities for a known protein or can even define the function of novel proteins. With the draft sequence of the human genome now available, it is possible to perform high-throughput mapping of protein-protein interactions in humans, which is termed as functional proteomics. The developments in functional proteomics are particularly timely since pharmaceutical companies are searching for technologies that will strengthen their genomic efforts and prioritize their drug discovery pipeline. In this article we review recent developments in functional proteomics.

Drug Design↗
PubMedDate not supplied

Definition and characterization of a "trypsinosome" from specific peptide characteristics by nano-HPLC-MS/MS and in silico analysis of complex protein mixtures.

Although HPLC-ESI-MS/MS is rapidly becoming an indispensable tool for the analysis of peptides in complex mixtures, the sequence coverage it affords is often quite poor. Low protein expression resulting in peptide signal intensities that fall below the limit of detection of the MS system in combination with differences in peptide ionization efficiency plays a significant role in this. A second important factor stems from differences in physicochemical properties of each peptide and how these properties relate to chromatographic retention and ultimate detection. To identify and understand those properties, we compared data from experimentally identified peptides with data from peptides predicted by in silico digest of all corresponding proteins in the experimental set. Three different complex protein mixtures extracted were used to define a training set to evaluate the amino acid retention coefficients based on linear regression analysis. The retention coefficients were also compared with other previous hydrophobic and retention scale. From this, we have constructed an empirical model that can be readily used to predict peptides that are likely to be observed on our HPLC-ESI-MS/MS system based on their physicochemical properties. Finally, we demonstrated that in silico prediction of peptides and their retention coefficients can be used to generate an inclusion list for a targeted mass spectrometric identification of low abundance proteins in complex protein samples. This approach is based on experimentally derived data to calibrate the method and therefore may theoretically be applied to any HPLC-MS/MS system on which data are being generated.

Animals↗
PubMedDate not supplied

Proteomic analysis of ubiquitinated proteins from human MCF-7 breast cancer cells by immunoaffinity purification and mass spectrometry.

Post-translational modification of proteins via the covalent attachment of Ubiquitin (Ub) plays an important role in the regulation of protein stability and function in eukaryotic cells. In the present study, we describe a novel method for identifying ubiquitinated proteins from a complex biological sample, such as a whole cell lysate, using a combination of immunoaffinity purification and liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. We have demonstrated the applicability of this approach by identifying 70 ubiquitinated proteins from the human MCF-7 breast cancer cell line after treatment with the proteasome inhibitor MG132. This method will aid the study of protein ubiquitination and may be used as a tool for the discovery of novel biomarkers that are associated with disease progression.

Biomarkers, Tumor↗
PubMedDate not supplied

Strategy to design improved proteomic experiments based on statistical analyses of the chemical properties of identified peptides.

Proteomics is an emerging field that uses many types of proteomic platforms however has few standardized procedures. Deciding which platform to use to perform large-scale proteomic studies is either based on personal preference or on so-called "figures of merit" such as dynamic range, resolution, and the limit of detection; these factors are often insufficient to predict the outcome of the experiment as the detection of peptides correlates to the chemical properties of each peptide. There is a need for a novel figure of merit that describes the overall performance of a platform based on measured output, which in proteomics is often a list of identified peptides. We report the development of such a figure of merit based on a predictive genetic algorithm. This algorithm takes into account the properties of the observed peptides such as length, hydrophobicity, and pI. Several large-scale studies that differed in sample type or platform were used to demonstrate the usefulness of the algorithm for improved experimental design. The figures that were obtained were clustered to find platforms that were biased in similar ways. Even though some platforms are different, they lead to the identification of similar peptide types and are thus redundant. The algorithm can thus be used as an exploratory tool to suggest a minimal number of complementary experiments in order to maximize experimental efficiency.

Algorithms↗