Secondary protein mass spectrum identification method based on mass-to-charge ratio error recognition capability

Abstract

The invention discloses a secondary protein mass spectrum identification method based on mass-to-charge ratio error recognition capability. The method comprises the following steps: virtualizing an enzymolysis protein databank sequence, establishing a peptide fragment databank and a peptide fragment databank index for each peptide fragment after being subjected to enzymolysis according to the mass number of the peptide fragment, finding out each candidate peptide fragment which meets the requirement in the established peptide fragment databank according to the mass number after charges of mother ions are removed in an experiment spectrum to be analyzed, performing isotopic peak removal and selecting an effective peak for processing, generating an experiment mark spectrum which meets a candidate peptide fragment theoretic spectrum, counting mass error information of different ions, calculating the mass-to-charge ratio error recognition capabilities of the different ions in different areas, scoring the candidate peptide fragments based on the mass-to-charge ratio error recognition capabilities, selecting the peptide fragment with the highest score as the identification result of the experiment spectrum, and performing the overall mass control on the identification result. The numbers of effective spectrums and peptide fragments identified by using the method are both greater than that of a conventional algorithm, the peak can be dynamically selected, and the operation speed is high.

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...

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