Identification method of protein by MS/MS based on mass-to-charge ratio error recognition ability

Abstract

The invention discloses a protein secondary mass spectrometry identification method based on the mass-to-charge ratio error recognition ability, comprising the following steps: virtual enzymolysis protein database sequence, establishing a peptide database for the enzymolysis peptide according to the mass number of the peptide section and Peptide database index, according to the mass number of the parent ion decharged in the experimental spectrum to be analyzed, find out the candidate peptides that meet the requirements in the established peptide database, perform deisotope peaks and select effective peaks, and generate candidate peptides that meet the requirements The experimental marker spectrum of the theoretical spectrum, the mass error information of different ions is counted, the mass-to-charge ratio error recognition ability of different ion types in different intervals is calculated, and each candidate peptide is scored based on the mass-to-charge ratio error recognition ability, and the selection score The highest peptide is used as the identification result of this experimental map, and the overall quality control of the identification results is carried out. The number of effective spectra and the number of peptides identified by this method are higher than the current algorithm, and the peak can be dynamically selected, and the operation speed is fast.

Description

technical field [0001] The invention relates to the field of protein secondary mass spectrometry identification, in particular to a protein secondary mass spectrometry identification method based on the ability to identify mass-to-charge ratio errors. Background technique [0002] The use of biological mass spectrometry technology makes large-scale automated protein identification a reality, and the combination of biological experiments and mass spectrometry technology can generate a large amount of experimental mass spectrometry data in a short period of time. Therefore, in proteomics research, secondary mass spectrometry data processing is an important A very important research content, its purpose is to infer the composition of sample proteins from data with noise or partial information missing. At present, there are two main methods for inferring the protein composition of samples: one is database search, and the other is DeNovo sequencing. Among them, database search is...

AI Technical Summary

Problems solved by technology

It provides an extremely powerful tool for the study of protein structure information and functional domains, but it does not involve the important characteristic information of the ability to identify mass-to-charge ratio errors.

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