PhosphoBlast, a computational tool for comparing phosphoprotein signatures among large datasets.

Abstract

Identification of specific protein phosphorylation sites provides predicative signatures of cellular activity and specific disease states such as cancer, diabetes, Alzheimer disease, and rheumatoid arthritis. Recent progress in phosphopeptide isolation technology and tandem mass spectrometry has provided the means to identify thousands of phosphorylation sites from a single biological sample. These advances now make it possible to profile global changes in the phosphoproteome at an unprecedented level. However, although this technology is generating a wealth of information, there is currently no efficient means to identify phosphoprotein signatures shared among large phosphoprotein databases. Identification of common phosphoprotein signatures found in biologically relevant systems and their conservation throughout evolution would provide valuable insight into mechanisms of signal transduction and cell function. Here we describe the development of a computational program (PhosphoBlast) that can rapidly match thousands of phosphopeptides that share phosphorylation sites within and across species. PhosphoBlast analysis of several large phosphoprotein datasets from the literature revealed common phosphorylation signatures shared across diverse experimental platforms and species. Moreover PhosphoBlast is a powerful analysis tool to identify specific phosphosite mutations. Comparison of the mouse and human phosphoproteomes revealed more than 130 specific phosphoamino acid mutations, some of which are predicted to alter protein function. Further analysis revealed that known phosphorylated amino acids are more evolutionally conserved than the Ser/Thr/Tyr amino acids not known to be phosphorylated. Together our results demonstrate that PhosphoBlast is a versatile mining tool capable of identifying related phosphorylation signatures and phosphoamino acid mutations among complex proteomics datasets in a highly efficient and accurate manner. PhosphoBlast will aid in the informatics analysis of the phosphoproteome and the identification of phosphoprotein biomarkers of disease.

• Authors: Yingchun Wang, Richard L Klemke
• Journal: Molecular & cellular proteomics : MCP (Mol Cell Proteomics) Vol. 7 Issue 1 Pg. 145-62 (Jan 2008) ISSN: 1535-9476 [Print] United States
• PMID: 17934212 (Publication Type: Comparative Study, Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't)

Chemical References

• Amino Acids
• Phosphopeptides
• Phosphoproteins

Topics

• Amino Acid Sequence
• Amino Acids
• Animals
• Computational Biology (methods)
• Databases, Protein
• Humans
• Mice
• Molecular Sequence Data
• Mutation
• Phosphopeptides (chemistry)
• Phosphoproteins (chemistry)
• Phosphorylation
• Software