Protein identification is frequently accomplished by chemically digesting (fragmenting) proteins then using LC/MS technology to separate and analyze the resulting peptide fragments. The PNNL-developed breakthrough technology uses artificial neural networks to predict how long it takes individual peptides to emerge, or elute, from the liquid chromatograph. This predictive power greatly increases confidence in the LC/MS identification of the peptides and original proteins. Tests conducted in the William R. Wiley Environmental Molecular Sciences Laboratory, a DOE scientific user facility located at PNNL, have shown the predicted retention times match actual retention times to within approximately 3 percent.
"The elution prediction model adds another significant metric to peptide identification which can increase accuracy or alternatively, reduce the need for high-mass measurement accuracy in mass spectrometry proteomics," said Richard Smith, a laboratory fellow at PNNL. "We are excited about the prospect of further developing and demonstrating the method on the standardized commercial LC/MS platform to be supplied by Agilent."
PNNL plans to use funding provided by the DOE's Office of Science, Life Sciences Division to demonstrate the peptide retention time capability on Agilent's instruments. Under the agreement, Agilent has the option to negotiate an exclusive license for the patent-pending, Battelle-owned background intellectual property and any inventions that may arise from it.
For more information, visit: www.agilent.com