A Peptide Detectability Prediction Method Aided by Enzyme Cutting Probability

Abstract

The invention discloses an enzyme digestion probability assisted peptide fragment detectability prediction method which comprises the steps: 1) screening outer high-reliability proteins among all theidentified proteins; 2) constructing an enzyme digestion point enzyme digestion probability prediction model training set; 3) training the enzyme digestion point enzyme digestion probability prediction model; 4) performing theoretical enzyme digestion on all the high-reliability proteins so as to obtain the theoretical enzyme digestion peptide fragments; 5) predicting the enzyme digestion probability of all the theoretical enzyme digestion peptide fragments; 6) constructing a peptide fragment detectability training set; 7) training the peptide fragment detectability model; and 8) predicting the peptide fragment detectability of all the theoretical enzyme digestion peptide fragments of other proteins. The protein enzymatic hydrolysis process is considered in the peptide segment detectability prediction process according to the characteristics of the shotgun proteomics process so as to considerably improve the accuracy of peptide fragment detectability prediction.

Description

technical field [0001] The invention relates to a method for predicting the detectability of peptides in proteomics, in particular to a method for predicting the detectability of peptides in shotgun proteomics. Background technique [0002] Targeted proteomics experiments can selectively detect and quantify peptides and proteins of interest, such as MRM assay strategies. This method can be used to quickly validate candidate biomarkers. The first step in developing an MRM assay is selecting representative peptides for a candidate protein. Methods for selecting representative peptides can be mainly divided into two categories: methods based on experimental data and methods based on calculations. However, methods based on experimental data have some limitations. First, not all proteins have existing experimental data. Secondly, whether the peptide can be detected is affected by many factors. It can be identified in the previous experiment, but it may not be detected in the ...

AI Technical Summary

Problems solved by technology

However, protein abundance is generally unknown without prior mass spectrometry experiments

[0004] Although many methods for peptide detectability have been proposed, the accuracy of current peptide detectability methods is still not satisfactory

Images