Non-linear fitting mode-based peptide mass spectrum speak characteristic parameter extraction method

Abstract

The invention relates to a non-linear fitting mode-based peptide mass spectrum speak characteristic parameter extraction method. The prior art is difficult to guarantee a precision of the extracted mass spectrum speak characteristic parameter when distributing error of sample points formed in a peptide mass spectrum is large. The non-linear fitting mode-based peptide mass spectrum speak characteristic parameter extraction method is characterized in that data of multiple sample points is utilized, the difference minimum of actual data and a fitting result is used as a guide, and a characteristic parameter estimated value is continuously updated by an iteration method until convergence conditions are satisfies so that the final characteristic parameter estimated value is obtained. The method provided by the invention can effectively reduce the unfavorable influence produced by sample point distributing error on Gauss curve characteristic parameter solving, improve a characteristic parameter value precision and is conducive to peptide segment identification precision improvement.

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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