Recent advances in the speed and sensitivity of mass spectrometers and analytical methods, the exponential acceleration of computer processing speeds, and the availability of genomic databases from an array of species and protein information databases have led to a deluge of proteomic data. Unfortunately, this enhancement in data acquisition has not been accompanied by a concomitant increase in the availability of tools allowing users to rapidly assimilate, explore, and analyze this data and adapt to a variety of experimental workflows with minimal user intervention. Often the manual aggregation and analysis of proteomic data in current proteomics software distract investigators from the biological meaning of their data, leading to the all-too-frequent deposition of proteomic data into the scientific literature with little or no biological or clinical interpretation.
We seek to fill the gap by providing a flexible platform for high-throughput autonomous proteomic analysis with the following critical components: liquid chromatography/mass spectrometry (LC/MS) acquisition control module, tandem mass spectra (MS/MS) database search, peptide spectral validation, peptide quantitation, quantitative data exploration tool within a relational database, cached public protein information databases and protein network exploration tool. The LC/MS control tool integrates lab information management system (LIMS) to provide automated multidimensional sample analysis, as well as captures meta-data during analysis and associates them with sample preparation protocols and experiment results in a relational database. Instrument acquired raw data are streamlined through a customized proteomic pipeline for database searching followed by peptide validation. The logistic spectral score we developed for high-throughput statistical validation of peptide sequence assignment to MSMS spectra outperforms standard tools already available in the proteomics field such as Sequest and X!Tandem to obtain the highest yield of confident peptide assignments. The logistic spectral score outperforms SEQUEST XCorr (242% more peptides identified on average) and the X!Tandem E-Value (87% more peptides identified on average) at a 1% false discovery rate estimated by decoy database approach. Peptide identifications, along with data-dependent calculation results are directed into a relational database for organization of expansive proteomic data sets, collation of proteomic data with available protein information resources, and visual comparison of multiple quantitative proteomic experiments. This platform provides flexible adaptation to diverse workflows for the unique requirements of the individual proteomics lab, enabling proteomic scientists to modify the presentation of the proteomic data, implement extra data-dependent analysis tasks, process additional input formats and control new types of instruments.
The utility of this system is illustrated through analysis of insulin signaling pathway important to liver cancers. We explored changes in phosphorylation profile quantitatively in hIRS1-transfected NIH3T3 cells in response to insulin stimulation. In a SILAC-labeled NIH3T3-hIRS1/NIH3T3-hIRS1 Y1180F timecourse, we discovered a total of 2201 phosphorylation sites at 1% false discovery rate, among which 1862 (84.6%) were on Serine, 299 (13.6%) were on Threonine and 40 (1.8%) were on Tyrosine. Using a label-free/SILAC hybrid quantitation approach, different phosphorylation patterns were identified in wild type and mutated cell lines.
|Advisor:||Salomon, Arthur R.|
|School Location:||United States -- Rhode Island|
|Source:||DAI-B 71/11, Dissertation Abstracts International|
|Subjects:||Analytical chemistry, Bioinformatics|
|Keywords:||Insulin signaling, Phosphoproteomics, Phosphorylation|
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