Complete glycopeptide identifying method and system

Abstract

The invention provides a complete glycopeptide identifying method. The method includes the steps the a carbohydrate chain structure database is traversed for any actual measurement tandem mass spectrum to be identified, for each carbohydrate chain structure, the mass of glycopeptide Y ions possibly obtained in fragment tests is concluded according to the mass of parent ions in the current series connection spectrogram, then the number of spectrum peaks matched with the current second-level spectrum is calculated, and the number of the matched spectrum peaks is used as a coarse marking result of matching between the glycopeptide Y ions and the current second-level spectrum; carbohydrate chain structures with the top K coarse marking scores serve as candidate carbohydrate chain structures; for the current series connection spectrogram, all the candidate carbohydrate chain structures are traversed, spectrum-spectrum matching between actual measurement spectrums and theoretical spectrums of peptide fragments of each candidate carbohydrate chain structure is marked, spectrum-spectrum matching between actual measurement spectrums and theoretical spectrums of the carbohydrate chain structures is marked, and then the glycopeptide structure identifying result is obtained. Reliability of complete large-scale glycopeptide identification can be improved, and calculation complexity is low.

Description

Technical field [0001] The present invention relates to the technical field of bioinformatics. Specifically, the present invention relates to the technical field of glycoproteomics and mass spectrometry. Background technique [0002] Mass spectrometry is the main method to identify site-specific protein glycosylation modifications on a large scale. In mass spectrometry, the intact glycopeptide is usually identified by tandem mass spectrometry of the intact glycopeptide, and then the glycosylation modification on the protein is inferred. At present, for the identification of large-scale complete glycopeptide spectra, there are two identification strategies, namely ①The identification of sugar chains based on complete glycopeptide tandem spectra represented by GRIP, ArMone 2.0 and other systems, and then matching peptides based on peptide quality The method, as well as the method of identifying peptides based on intact glycopeptide spectrograms represented by Byonic and other syst...

AI Technical Summary

Problems solved by technology

[0004] In the above two methods, only the complete glycopeptide spectrum is used to identify the sugar chain part or peptide part of the glycopeptide, while the other part is directly inferred by mass, so the reliability is poor

For example, after using the complete glycopeptide spectrum to identify the sugar chain part of the glycopeptide, the mass of the peptide is estimated to be 999.5633, and there are many masses that can be matched within this error range, such as the two peptides of LTEAKPVDK and DVPKAETLK The quality is exactly the same, and both can be matched to 999.5633. If only the quality is used to match, the two cannot be distinguished at all.

Similarly, the method of inferring the composition of sugar chains only based on the quality of sugar chains also has the above problems

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