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A Label-free Quantitative Method for Proteome Combining Secondary Mass Spectrometry and Machine Learning Algorithms

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: 2016-01-27
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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  • A Label-free Quantitative Method for Proteome Combining Secondary Mass Spectrometry and Machine Learning Algorithms
  • A Label-free Quantitative Method for Proteome Combining Secondary Mass Spectrometry and Machine Learning Algorithms
  • A Label-free Quantitative Method for Proteome Combining Secondary Mass Spectrometry and Machine Learning Algorithms

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Experimental program
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Embodiment 1

[0020] 1. The trypsin digests of proteins extracted from yeast and mouse brain were used as the data source of the training data set, and they were run 5 times on the one-dimensional nano-RPLC-MS / MS system respectively. Mass spectrometers are LTQXL and OrbitrapVelos from Thermo Company. The UPS2 standard protein mixture purchased from Sigma was used as the quantitative data set to test the effect of the method. The UPS2 standard protein mixture is composed of 48 human-derived standard proteins, the concentration of which spans 6 orders of magnitude, and there are 8 proteins with different properties in each order of magnitude. The tryptic digest of UPS2 was run 5 times on the same system. The absolute amount of UPS2 on the column spans 6 orders of magnitude from 5 amol to 500 fmol.

[0021]The original data RAW file was converted into mgf format with the msconvert.exe component in TPP (version4.6), and then the mgf file was searched using the Mascot (version2.3.02) database ...

Embodiment 2

[0028] 1. The relatively quantitative test data set comes from the Clinical Proteomic Technology Assessment for Cancer (CPTAC), from http: / / www.proteomecommons.org / Downloaded from the website (hash:NGX3cBUAZXSWvc+6XFNIdVhpLPJTO87lzAxUQmwwR2KHUwWDrdFwV1dso3bvxf7HeXZ4C / juqwEUIz4boC9H3HcLrxEAAAAAAAmDw==), the name of the dataset is Study6OrbitrapO86. This data set contains 5 samples A-E, each sample contains an equal amount of yeast extract protein (60ng / μL), and contains 0.24, 0.74, 2.2, 6.7, 20fmol / μL of UPS1 standard protein mixture, so that every two The fold change of UPS1 protein in adjacent samples was 3-fold while the content of yeast protein was unchanged. UPS1 is similar to UPS2 except that the 48 standard proteins are mixed in equimolar proportions. Each sample was injected in triplicate on the OrbitrapXL mass spectrometer. Since the UPS1 protein content in samples A and B is extremely low, the yeast protein in them was used as the data source of the training data ...

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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 the quantitative method of proteomics based on mass spectrometry technology, and in particular relates to a label-free absolute and relative quantitative method of proteomics combined with the intensity of secondary mass spectrum and machine learning algorithm. Background technique [0002] Mass spectrometry-based proteomics technology has gradually shifted from qualitative to quantitative. Quantitative proteomics plays an important role in the discovery of disease biomarkers. Absolute quantification at the omics scale allows us to dynamically monitor how proteins in a sample change in time and space. At present, the quantity of single or several proteins can be obtained by adding known amounts of isotope-labeled peptides or proteins, but absolute quantification at the proteomic scale can only be achieved by label-free experimental strategies combined with novel computational methods. [0003] The calculation method of absolu...

Claims

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Application Information

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Patent Type & Authority Patents(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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