Analysis method of peptide impurities in high-purity peptides based on data mining

Abstract

The present invention proposes a method for analyzing peptide impurities in high-purity polypeptides based on data mining. The operation method for analyzing peptide impurities in high-purity polypeptides is to analyze peptide impurities based on data-dependent acquisition strategies while analyzing Optimize the detection data set and identification data set, adopt the "peeling onion" strategy, and identify the high-abundance impurity peptides in turn, and the subsequent analysis will no longer focus on the identified peptides, saving the precious window period. Identify the unidentified second-highest-abundance peptide impurities, and finally identify the ultra-low-abundance peptide ions by multiple selective enrichment. In this way, the number of identifications of peptide impurities in high-purity peptides can be significantly improved.

Description

technical field [0001] The invention relates to an operation method for qualitative analysis of peptide impurities in high-purity polypeptides, in particular to a method for significantly improving the number of peptide impurities identified based on intelligent multiple tests based on data mining. Background technique [0002] High-purity peptides and proteins are the reference materials for establishing biological standard reference materials. Impurities include inorganic impurities, organic impurities and biological peptide impurities. The industry requires that 0.1% impurities must be analyzed qualitatively and quantitatively. Inorganic impurities and organic impurities can be accurately analyzed by traditional analysis methods. Biological peptide impurities have various structures and trace amounts, and are easily subject to strong interference from principal components, making it difficult to analyze accurately and qualitatively. [0003] There are many types of pepti...

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Problems solved by technology

[0005] Michalski, Cox et al. reported that there is a significant problem with the DDA strategy: the number of peptide sequences identified by LC-MS / MS is far less than the number of peptide sequences detected by MS (see [1], see [2])

There are two specific deficiencies: (1) This method needs to set an abundance threshold to determine whether the ion has sufficient intensity to do MS / MS, so all ions below this threshold are excluded, which is very important for the identification of trace peptides. Unfavorable; (2) Due to the serial operation of DDA, the timing of peptide identification is often not at the peak of the peptide chromatographic peak, that is, MS / MS is not performed when the peak intensity is the highest, so the qualitative and quantitative data obtained None of the information is optimal

However, this type of method brings new problems: the spectrum of MS / MS data is too complex, and the obtained MS / MS data is not easy to accurately determine its precursor ion, and it is necessary to develop new algorithms and programs to solve the spectrum (see [9] : Bern, M., Finney, G., Hoopmann, M.R., Merrihew, G., Toth, M.J. and MacCoss, M.J. Deconvolution of Mixture Spectra from Ion-Trap Data-Independent-Acquisition Tandem Mass Spectrometry. Analytical Chemistry, 2010, 82(3 ):833-841

[0007] Although analytical strategies such as DDA and DIA have made great progress, there is still a lack of effective solutions for the identification and accurate quantitative analysis of impurities of interest and low-abundance peptides in high-purity peptides / proteins (see [1])

Analyzing its reasons, there are two deficiencies: (1) DDA is dependent on data acquisition, only relying on the previous full scan data and the data of simple leading ions stored in the dynamic exclusion method, many effective and beneficial data information, such as peptide Information about relative elution order of segments, information about optimal identification conditions for rare ions in complex matrices, etc. are not utilized

(2) Not "flexible" enough. Regardless of DDA or DIA, once the mass spectrometer is adjusted (including mass axis calibration and sensitivity tuning, etc.), the mass spectrometer basically maintains the same set of compromised and optimized ion optics parameters throughout the experiment. (Except for adjusting the total time of ion injection by automatic gain control) Mass spectrometry should be performed on peptide series with diverse types, huge data, and large differences in expression levels, and the results obtained are very unfavorable for trace peptide series

Even if the analysis test is not repeated many times, it is still difficult to identify low-abundance peptides, and it is even difficult to be selected for identification.

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