Tandem mass spectrogram identification method

Abstract

The invention provides a tandem mass spectrogram identification method which is characterized by comprising the following steps: 1) conducting restrictive searching in a global sequence library on each spectrogram in a spectrogram dataset to be identified, so as to obtain a peptide fragment I matched with the spectrogram; 2) establishing a local sequence library according to the peptide fragments I obtained in step 1), and conducting open search in the local sequence library on the spectrogram in the spectrogram dataset to be identified, so as to obtain modified peptide fragments II matched with part of the spectrograms, and obtain the modification mass and error burst; 3) setting a restrictive searching interval for the spectrogram in the spectrogram dataset to be identified according to the matched modification mass and error burst in the step 2) and the mass of the current spectrogram to be identified, searching in the global sequence library, and obtaining a final matching result. The tandem mass spectrogram identification method has the advantages that the identification rate and accuracy are improved; the searching speed is higher.

Description

Technical field [0001] The present invention relates to the technical field of bioinformatics. Specifically, the present invention relates to a method for identifying tandem mass spectrometry. Background technique [0002] Tandem mass spectrometry identification technology is a key technology in proteomics research, and it is also the main method for large-scale protein sequence and modification identification. Sequence library search is a conventional tandem mass spectrum identification method. In the usual sequence library search, each spectrum is delineated with a mass interval centered on the mass m of the spectrum, and then the spectrum is combined with all peptides (peptides) in the corresponding mass interval in the sequence library. A segment can also be called a peptide sequence) for matching, and a peptide-spectrum matching score is obtained. The peptide with the best score is used as the identification result of this spectrum. Since the sequence library contains all ...

AI Technical Summary

Problems solved by technology

However, due to the relatively large mass interval used, the number of peptides to be matched is very large. Assuming that the number of peptides falling within the interval [m-0.00002m, m+0.00002m] is n, then it falls into the interval [m- The peptides within 200Da, m+200Da] may exceed 400n, which leads to a huge amount of calculation in the open sequence library search in the prior art, and the search speed is greatly reduced

In addition, due to the large search space of the open sequence library search, the number of peptides matched and scored with each spectrum has increased dramatically

Under the competition of many wrong peptides, it is very likely that the correct peptide will not get the highest peptide-spectrum matching score

Therefore, the improvement of spectral identification accuracy by open sequence library search is very limited

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