Battelle Memorial Institute

Fred Hutchinson Cancer Center

The Translational Genomics Research Institute

Luxembourg Institute of Health

Konstantinos Petritis

Richard Dale Smith

Lars J Kangas

Patrick Pirrotte

Yeoun Jin Kim

Amanda G Paulovich

Guy Berchem

Anna Elisabeth Schritz

Tony Tegeler

A method of predicting whether a peptide present in a biological sample will be detected by analysis with a mass spectrometer. The method uses at least one mass spectrometer to perform repeated analysis of a sample containing peptides from proteins with known amino acids. The method then generates a data set of peptides identified as contained within the sample by the repeated analysis. The method then calculates the probability that a specific peptide in the data set was detected in the repeated analysis. The method then creates a plurality of vectors, where each vector has a plurality of dimensions, and each dimension represents a property of one or more of the amino acids present in each peptide and adjacent peptides in the data set. Using these vectors, the method then generates an algorithm from the plurality of vectors and the calculated probabilities that specific peptides in the data set were detected in the repeated analysis. The algorithm is thus capable of calculating the probability that a hypothetical peptide represented as a vector will be detected by a mass spectrometry based proteomic platform, given that the peptide is present in a sample introduced into a mass spectrometer.

Method for predicting peptide detection in mass spectrometry

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Richland, WA, United States of America

Konstantinos Petritis