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Proteome label-free quantification method combining tandem mass spectrometry with machine learning algorithm

A secondary mass spectrometry and machine learning technology, applied in the quantitative field of proteomics, to achieve the effect of reliable identification results

Active Publication Date: 2014-06-25
DALIAN INST OF CHEM PHYSICS CHINESE ACAD OF SCI
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The present invention provides a new method for absolute and relative quantification at the proteome level, which uses the intensity of the secondary mass spectrum as the quantitative basis and introduces a machine learning algorithm to correct the response difference of peptides with different properties on the liquid chromatography-mass spectrometry system

Method used

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  • Proteome label-free quantification method combining tandem mass spectrometry with machine learning algorithm
  • Proteome label-free quantification method combining tandem mass spectrometry with machine learning algorithm
  • Proteome label-free quantification method combining tandem mass spectrometry with machine learning algorithm

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Experimental program
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Embodiment 1

[0020] 1. Using yeast and mouse brain extract protein trypsin digest as the data source of the training data set, run 5 times on the one-dimensional nano-RPLC-MS / MS system. The mass spectrometer is LTQ XL and Orbitrap Velos of Thermo Company. The UPS2 standard protein mixture purchased from Sigma was used as the quantitative data set test method. The UPS2 standard protein mixture is a mixture of 48 standard proteins derived from humans, and its concentration spans 6 orders of magnitude, with 8 proteins with different properties on each order of magnitude. The trypsin hydrolysate of UPS2 was run 5 times on the same system. The absolute amount of UPS2 on the column ranges from 5 amol to 500 fmol, spanning 6 orders of magnitude.

[0021] The original data RAW file is converted into mgf format with msconvert.exe component in TPP (version4.6), and then the mgf file is searched by Mascot (version2.3.02) database search engine. The databases used to search the three samples are: (1) ...

Embodiment 2

[0028] 1. The relative quantitative test data set comes from Clinical Proteomic Technology Assessment for Cancer (CPTAC), from http: / / www.proteomecommons.org / Download from the website (hash:NGX3cBUAZXSWvc+6XFNIdVhpLPJTO87lzAxUQmwwR2KHUwWDrdFwV1dso3bvxf7H eXZ4C / juqwEUIz4boC9H3HcLrxEAAAAAAAAmDw==86). The name of the data set isStudy6OrbitrapOtudy6. The data set contains 5 samples AE, each sample contains the same amount of yeast extract protein (60ng / μL), and successively contains 0.24, 0.74, 2.2, 6.7, 20fmol / μL UPS1 standard protein mixture, so that every two The fold change of UPS1 protein in adjacent samples was 3 times while the yeast protein content remained unchanged. UPS1 is similar to UPS2, except that the 48 standard proteins are equimolar mixtures. Each sample was injected 3 times on the Orbitrap XL mass spectrometer. Because the UPS1 protein content in samples A and B is extremely low, the yeast protein in them is used as the data source of the training data set; t...

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Abstract

The invention relates to a proteome label-free quantification method combining tandem mass spectrometry with machine learning algorithm, which is used for absolute and relative quantitative analysis of a proteome level. The method firstly analyzes an enzymolytic peptide fragment mixture of a proteome actual sample for establishing a training data set and an enzymolytic peptide fragment mixture of a proteome sample to be analyzed by using a liquid chromatography-tandem mass spectrometry system. The total amount of the samples can be obtained by cell counting or protein concentration determination, and the absolute amount of each protein can be calculated according to the percents and sample total amount calculated in the last step. The absolute amounts of the same protein in different samples are compared to obtain relative quantitative information of the protein in different samples. The method has good accuracy in both absolute quantification and relative quantification.

Description

Technical field [0001] The invention belongs to a proteomics quantitative method based on mass spectrometry technology, and specifically relates to a label-free absolute and relative quantitative proteomics method that combines the intensity of secondary mass spectrometry and a machine learning algorithm. Background technique [0002] Proteomics technology based on mass spectrometry has gradually changed from qualitative to quantitative. Quantitative proteomics plays an important role in the discovery of disease biomarkers. Absolute quantification on the omics scale allows us to dynamically monitor the changes in time and space of the protein in the sample. At present, the amount of single or several proteins can be obtained by adding known amounts of isotope-labeled peptides or proteins, but the absolute quantification of proteomics scale can still only be achieved by label-free experimental strategies combined with new calculation methods. [0003] The calculation method of abs...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01N30/86
Inventor 张丽华吴琪梁振曲焱焱蒋好张玉奎
Owner DALIAN INST OF CHEM PHYSICS CHINESE ACAD OF SCI
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