Data analysis to provide a revised data set for use in peptide sequencing determination

Abstract

In one aspect of the present invention, the less “useful” spectral data is disregarded from the spectral data resulting from the fragmentation by ETD and candidate charge states for the “useful” data assigned. Knowledge of the first order ion product charge state reduces the subset of comparison data hence aiding in the eventual identification of the precursor ion, and thus aiding in peptide sequence database searching capabilities. Such capabilities include, but are not limited to, computational requirements for database search and data storage, CPU time, the volume taken up on the hard disk to store results, visualization and dissemination of data, and overall improvement in the confidence in the precursor identification. Thus determination of the peptide sequence can be resolved in less time, costing less money, and requiring less computer power.

Description

FIELD OF THE INVENTION[0001]The invention relates to a method for analyzing product ion data to produce a revised data set that can be used in peptide sequencing determination. More specifically, the invention relates to determining the charge states of product ions generated from precursor ions by a non-ergodic technique.BACKGROUND OF THE INVENTION[0002]Mass spectrometry has become the method of choice for fast and efficient identification of proteins in biological samples. Tandem mass spectrometry of peptides in a complex protein mixture can be used to identify and quantify the proteins present in the original mixture. Tandem mass spectrometers achieve this by selecting single m / z values and subjecting the precursor ions to fragmentation, providing product ions that can be used to sequence and identify peptides. The information created by the product ions of a peptide can be used to search peptide and nucleotide sequence databases to identify the amino acid sequence represented by...

AI Technical Summary

Benefits of technology

[0008]In one aspect of the present invention, the less “useful” spectral data is disregarded from the spectral data resulting from the fragmentation by a non-ergodic reaction such as ETD, and candidate charges for the “useful” data are assigned. To facilitate this, first order ion products and second order ion products are identified. Knowledge of the product ion charge reduces the subset of comparison data hence aiding in the eventual identification of the precursor ion, and thus aiding in peptide sequence database searching capabilities. Such capabilities include, but are not limited to, computational requirements for search requirements and data storage such as the CPU time taken in searching, the volume taken up on the hard disk to store large quantities of search results for redundant charge states, visualization and dissemination of data, and overall improvement in the confidence in the precursor identification. Thus allowing the determination of the peptide to be determined in less time, costing less money, and requiring less computer power.

Problems solved by technology

Though non-ergodic reactions such as ETD or ECD fragmentation appear to offer the best solutions for peptide determination, these techniques create their own problems.

ECD can not be performed with trap-type mass analyzers since the electrons created by the reaction do not typically retain their thermal energy long enough to be trapped, thus ECD is typically performed on a FT-ICT mass spectrometer.

These instruments are expensive.

ETD fragmentation particularly of large peptides and proteins, which can be performed by an ion trap, often leads to spectra too complicated for direct interpretation.

The limited m / z resolution of currently available mass analyzers makes interpretation of these highly charged product m / z spectral data difficult.

In addition, the charge state determination is more complicated and important than for CID where normally charge states up to only +4 are observed.

This is consuming in terms of time and space, in terms, for example, of computer storage space, the number of searches performed, computer execution time, and the valuable time of the scientist in reviewing the data.

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