Description
- Abstract:
- Recent advances in the speed and sensitivity of mass spectrometers and analytical methods, the exponential acceleration of computer powers, and the availability of genomic databases from an array of species have led to a deluge of proteomic data. Unfortunately, this enhancement has not been accompanied by a concomitant increase in the availability of tools allowing users to efficiently analyze these data. Often the manual aggregation and analysis of proteomic data in current software distract investigators from the biological meaning of their data, leading to the all-too-frequent deposition of data into scientific literature with little biological interpretation. We seek to fill the gap by providing a high-throughput autonomous proteomic analysis pipeline with the following critical components: liquid chromatography/mass spectrometry (LC/MS) acquisition control, peptide validation, quantitative data exploration, and protein network analysis. The automated LC/MS control tool provides reproducible and sensitive multi-dimensional sample analysis. Instrument acquired data are streamlined to a customized proteomic pipeline for database searching and post-acquisition calculation. The logistic spectral score we developed for high-throughput statistical validation of database assignment outperforms SEQUEST XCorr (3.4-fold more peptides) and X!Tandem E-Value (1.9-fold more peptides) at a 1% false discovery rate estimated by decoy database. All calculation results are directed into a relational database for organization of proteomic results, collation of experimental data with available protein information resources, and visual comparison of multiple proteomic experiments. This platform provides flexible adaptation to diverse workflows for individual proteomics labs and enables proteomic scientists to modify the presentation of the proteomic data, implement extra data-dependent analysis tasks and process additional input formats. The utility of this system is illustrated through analysis of insulin signaling pathway important to liver cancers. We explored changes in phosphorylation quantitatively in hIRS1-transfected NIH3T3 cells in response to insulin stimulation using a label-free/SILAC hybrid quantitation approach. In the NIH3T3-hIRS1/NIH3T3-hIRS1 Y1180F timecourse, we discovered 2201 phosphorylation sites at 1% false discovery rate, among which 84.6% were on Serine, 13.6% were on Threonine and 1.8% were on Tyrosine.
- Notes:
- Thesis (Ph.D.) -- Brown University (2010)
Access Conditions
- Rights
- In Copyright
- Restrictions on Use
- Collection is open for research.
Citation
Yu, Kebing,
"A Phosphoproteomic Study of Insulin Signaling Pathway Using A Novel High-Throughput Pipeline"
(2009).
Chemistry Theses and Dissertations.
Brown Digital Repository. Brown University Library.
https://doi.org/10.7301/Z0BP012C
