A Method for Extracting Characteristic Parameters of Peptide Mass Spectrum Peaks Based on Nonlinear Fitting Method

Abstract

The invention relates to a method for extracting characteristic parameters of peptide mass spectrum peaks. The existing method has the disadvantage that it is difficult to ensure the accuracy of the extracted mass spectrum peak characteristic parameters when there is a large deviation in the distribution of the various points that form the peak in the peptide mass spectrum. The present invention proposes a method for extracting characteristic parameters of peptide mass spectrum peaks based on a nonlinear fitting method, using multiple sample point data, guided by the minimum difference between the actual data and the fitting result, and adopting an iterative method to continuously update the estimated value of the characteristic parameters, Until the convergence condition is satisfied, the final characteristic parameter estimation is obtained. This method can effectively reduce the adverse effects of the sample point distribution deviation on the solution of the characteristic parameters of the Gaussian curve, improve the numerical accuracy of the characteristic parameters, and then help improve the accuracy of peptide identification.

Description

technical field [0001] The invention belongs to the technical field of biological mass spectrum data preprocessing and information extraction, and specifically relates to a method for extracting characteristic parameters of peptide mass spectrum peaks based on a nonlinear fitting method. Background technique [0002] Peptide identification based on tandem mass spectrometry is a widely used technique in the field of proteome research. The peptides to be identified are fragmented into fragment ions in the mass spectrometer to generate tandem mass spectrometry data, which are compared and analyzed with the theoretical tandem mass spectrum library or the identified peptide mass spectrum library, and finally the identification of unknown peptides is completed. [0003] Usually, mass spectrometry detection is performed on a certain ion, and the detected mass-to-charge ratio data is not a single numerical point, but several sample points, which are fitted to a Gaussian curve on the...

AI Technical Summary

Problems solved by technology

However, in actual detection, affected by factors such as experimental conditions, environment, and equipment noise, the sample points on the mass spectrogram are often not strictly distributed on the Gaussian curve, but there is a certain deviation

When the deviation value of each sampling point is large, the assumptions in the above method are difficult to be established, which will inevitably cause large errors in the values ​​of the characteristic parameters obtained, which will affect the accuracy of peptide identification.

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