针对无标签鸟枪法蛋白质定量的新方法
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摘要
目的通过所检测到的肽段丰度计算蛋白质丰度是蛋白质组学的一个重要部分。由于退化肽段的丰度可能由多个蛋白质提供,简单剔除退化肽段可消除这种不确定性并简化问题。但是由于退化肽段的信息没有被充分利用,蛋白质定量的准确性会受到影响,还可能显著降低可以定量的蛋白质规模。如何充分利用退化肽段的信息,提高蛋白定量的准确性和全面性,并且不会导致问题规模更为复杂,成为一个重要的问题。方法为了在不引入更多误差的情况下充分利用退化肽段来进行更准确的定量,本文提出一个基于误差最小化的方法(error-minimization-based quantification for protein,EMQ)。不同于以往的大多数算法,EMQ利用退化肽段中的信息,最大限度地将肽段层面信息还原到蛋白质层面,得到了更多的蛋白质定量结果并提高了结果的精度。结果多个实验数据上的表现证明本方法可以在较小的时间代价下获得更高的精度,并提高结果的规模。结论本文提出的基于误差最小化的方法可以快速准确地对大规模蛋白质组学问题进行定量。
Objective Calculating the abundance of proteins by the peptides is an important part of proteomics.The abundance of degenerate peptides may be due to the contributions from multiple proteins,and removing the degenerate peptides directly can eliminate the uncertainty and simplify the problem.However,because the information of the degenerate peptides is not fully utilized,the accuracy of the protein abundance may be affected and the amount of protein that can be quantified may significantly be reduced.How to make full use of the information of degenerate peptides to improve the accuracy and comprehensiveness of protein quantification,and not lead to more complex problems,is an important issue.Methods We provide a method based on error-minimization-based quantification for protein(EMQ).Unlike most of the previous algorithms,EMQ takes advantage of the information in the degenerate peptides to maximize the reduction of peptide level information to the protein level,obtaining more protein quantitative results and improving the accuracy.Results The results in multiple experimental data show that our method can get higher accuracy and more protein quantification results with a relatively short time.Conclusions The method based on error minimization proposed in this paper can quickly and accurately be applied in large-scale proteomics problems.
Objective Calculating the abundance of proteins by the peptides is an important part of proteomics.The abundance of degenerate peptides may be due to the contributions from multiple proteins,and removing the degenerate peptides directly can eliminate the uncertainty and simplify the problem.However,because the information of the degenerate peptides is not fully utilized,the accuracy of the protein abundance may be affected and the amount of protein that can be quantified may significantly be reduced.How to make full use of the information of degenerate peptides to improve the accuracy and comprehensiveness of protein quantification,and not lead to more complex problems,is an important issue.Methods We provide a method based on error-minimization-based quantification for protein(EMQ).Unlike most of the previous algorithms,EMQ takes advantage of the information in the degenerate peptides to maximize the reduction of peptide level information to the protein level,obtaining more protein quantitative results and improving the accuracy.Results The results in multiple experimental data show that our method can get higher accuracy and more protein quantification results with a relatively short time.Conclusions The method based on error minimization proposed in this paper can quickly and accurately be applied in large-scale proteomics problems.
引文
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[2]Pandey A,Mann M.Proteomics to study genes and genomes[J].Nature,2000,405(6788):837-846.
[3]Aebersold R,Mann M.Mass spectrometry-based proteomics[J].Nature,2003,422(6928):198-207.
[4]Hoopmann MR,Moritz RL.Current algorithmic solutions for peptide-based proteomics data generation and identification[J].Current Opinion in Biotechnology,2013,24(1):31-38.
[5]Gstaiger M,Aebersold R.Applying mass spectrometry-based proteomics to genetics,genomics and network biology[J].Nature Reviews Genetics,2009,10(9):617-627.
[6]Zhang Y,Wen Z,Washburn MP,et al.Improving label-free quantitative proteomics strategies by distributing shared peptides and stabilizing variance[J].Analytical Chemistry,2015,87(9):4749-4756.
[7]Jin S,Daly DS,Springer DL,et al.The effects of shared peptides on protein quantitation in label-free proteomics by LC/MS/MS[J].Journal of Proteome Research,2007,7(1):164-169.
[8]Dost B,Bandeira N,Li X,et al.Accurate mass spectrometry based protein quantification via shared peptides[J].Journal of Computational Biology,2012,19(4):337-348.
[9]He Z,Huang T,Liu X,et al.Protein inference:a protein quantification perspective[J].Computational Biology and Chemistry,2016,63:21-29.
[10]Silva JC,Gorenstein MV,Li GZ,et al.Absolute quantification of proteins by LCMSE a virtue of parallel MS acquisition[J].Molecular&Cellular Proteomics,2006,5(1):144-156.
[11]Gerster S,Kwon T,Ludwig C,et al.Statistical approach to protein quantification[J].Molecular&Cellular Proteomics,2014,13(2):666-677.
[12]Malmstr9m J,Beck M,Schmidt A,et al.Proteome-wide cellular protein concentrations of the human pathogen Leptospira interrogans[J].Nature,2009,460(7256):762-765.
[13]Ludwig C,Claassen M,Schmidt A,et al.Estimation of absolute protein quantities of unlabeled samples by selected reaction monitoring mass spectrometry[J].Molecular&Cellular Proteomics,2012,11(3):M111.013987.
[14]Beck M,Schmidt A,Malmstroem J,et al.The quantitative proteome of a human cell line[J].Molecular Systems Biology,2011,7(1):549.