GRAEZing: A classifier to increase glycoproteomic coverage
Inventors: Richard Lee, John Froelich, Eric Dodds
Invention Types: Information Technology/Software, Research Tool
Keywords: SoftwareFor More Information Contact: Chou, Jennifer
This invention is based on the identification of glycopeptide-rich acquisition enhancement zones (GRAEZs). The GRAEZing tool, developed in the lab of Dr. Richard S. Lee, MD, is a powerful way of increasing accuracy and sensitivity of MS/MS-based glycoproteomic analyses. This tool, used in conjunction with available MS instruments, allows discrimination between peptides and glycopeptides in complex mixtures of biological origin based on accurate mass measurements of precursor peaks.
The GRAEZ classifier may be used in conjunction with targeted MS to overcome dynamic range challenges and increase glycoproteomic coverage. It may also be used as a QC metric for various benchtop preparations. For example, GRAEZ may rapidly compare the effectiveness of different glycopeptide sample preparations, or evaluate the prevalence of glycosylated species in different samples. Further, GRAEZ classification of existing proteomic data sets may be used to evaluate the prevalence of glycosylated peptides in existing data, regardless of shether these species were selected for MS/MS fragmentation. This tool will improve accuracy and sensitivity of analysis of the glycoproteome in biological samples.
The GRAEZ classifier operates in association with multiple glycopeptide identification software and increases accuracy, sensitivity and specificity of the software. The tool may also be incorporated into any high-accuracy MS acquisition engine to accurately identify glycopeptides in real time.
• A simple and broad tool to increase accuracy and sensitivity of MS/MS-based glycoproteomic analyses.
• Incorporation into emerging glycopeptide identification software, to increase sensitivity and specificity of assignments.
• Incorporation into MS acquisition engine â€“ to identify likely glycopeptides in real time.
• Accurate analysis of glycoprotein site occupancy and glycan heterogeneity is a challenging task.
• Peptides greatly confound the analysis of glycopeptides.
• Compared to using just straightforward MS analysis, this technology allows discrimination between peptides and glycopeptides in complex mixtures of biological origin.
Licensing opportunity available
Key Publications: Froehlich JW, Dodds ED, Wilhelm M, Serang O, Steen JA, Lee RS. A classifier
based on accurate mass measurements to aid large-scale, unbiased glycoproteomics.
Mol Cell Proteomics. 2013 Feb 25. [Epub ahead of print] PubMed PMID: 23438733.
IPStatus: Pat. Pend.