北京生物医学工程

一种基于加权非负最小二乘的蛋白质定量方法

A protein quantification method based on weighted non-negative least squares

作者：方言郑浩然

单位：中国科学技术大学计算机科学与技术学院(合肥 230027) 通信作者：郑浩然。E-mail: hrzheng@ustc.edu.cn

分类号：R318.04

出版年·卷·期（页码）：2022·41·1（62-67）

摘要：

目的提出一种综合利用肽段信息的蛋白质定量方法WeQuant (weighted peptide-based protein quantification)，以提高蛋白质定量的通量与准确度，特别是低丰度蛋白质。方法基于肽段和蛋白质的关系，按照肽段的丰度与来源对所有肽段进行加权，并利用肽段和蛋白质的等量关系建立加权非负最小二乘模型，从而得到蛋白质的相对丰度。结果与传统蛋白质定量方法相比，WeQuant在实验数据集上显著增加了有效定量的蛋白质数量，并在不同丰度范围均达到了更高的定量准确度。此外，WeQuant能够有效定量未被其他方法报告的低丰度蛋白质。结论本文提出的基于加权非负最小二乘的模型能够克服对高丰度肽段和唯一肽段的依赖，实现对不同丰度范围的蛋白质进行准确定量。

Objective A protein quantification method named WeQuant (weighted peptide-based protein quantification), which comprehensively utilizes peptide information, is proposed to improve the throughput and accuracy of protein quantification, especially for low-abundance proteins. Methods Based on the relationship between peptides and proteins, all peptides are weighted according to their abundances and sources, and a weighted non-negative least squares model is established using the equal relationship between peptides and proteins to obtain the relative abundance of proteins. Results Compared with traditional protein quantification methods, WeQuant significantly increases the number of effective quantitative proteins in the experimental data set, and achieves higher quantitative accuracy in different abundance ranges. In addition, WeQuant effectively quantified low-abundance proteins not reported by other methods. Conclusions The model based on weighted non-negative least squares proposed in this paper can overcome the dependence on high-abundance peptides and unique peptides, and achieve accurate quantification of proteins with different abundance ranges.

参考文献：

[1]?Bian Y, Zheng R, Bayer FP, et al. Robust, reproducible and quantitative analysis of thousands of proteomes by micro-flow LC–MS/MS[J]. Nature Communications, 2020, 11(1): 1-12.

[2]?Miller RM, Millikin RJ, Hoffmann CV, et al. Improved protein inference from multiple protease bottom-up mass spectrometry data[J]. Journal of Proteome Research, 2019, 18(9): 3429-3438.

[3]?Gillet LC, Leitner A, Aebersold R. Mass spectrometry applied to bottom-up proteomics: entering the high-throughput era for hypothesis testing[J]. Annual Review of Analytical Chemistry, 2016, 9: 449-472.

[4]?Matzke MM, Brown JN, Gritsenko MA, et al. A comparative analysis of computational approaches to relative protein quantification using peptide peak intensities in label-free LC-MS proteomics experiments[J]. Proteomics, 2013, 13(3-4): 493-503.

[5]?Keshishian H, Addona T, Burgess M, et al. Quantitative, multiplexed assays for low abundance proteins in plasma by targeted mass spectrometry and stable isotope dilution[J]. Molecular & Cellular Proteomics, 2007, 6(12):2212-2229.

[6]?Tiambeng TN, Roberts DS, Brown KA, et al. Nanoproteomics enables proteoform-resolved analysis of low-abundance proteins in human serum[J]. Nature Communications, 2020, 11(1): 1-12.

[7]?Berna M, Ackermann B. Increased throughput for low-abundance protein biomarker verification by liquid chromatography/tandem mass spectrometry[J]. Analytical Chemistry, 2009, 81(10): 3950-3956.

[8]?Polpitiya AD, Qian WJ, Jaitly N, et al. DAnTE: A statistical tool for quantitative analysis of -omics data[J]. Bioinformatics, 2008, 24(13): 1556-1558.

[9]?Grossmann J, Roschitzki B, Panse C, et al. Implementation and evaluation of relative and absolute quantification in shotgun proteomics with label-free methods[J]. Journal of Proteomics, 2010, 73(9): 1740-1746.

[10]?Blein-Nicolas M, Xu H, de Vienne D, et al. Including shared peptides for estimating protein abundances: A significant improvement for quantitative proteomics[J]. Proteomics, 2012, 12(18): 2797-2801.

[11]?江丹阳, 郑浩然. 针对无标签鸟枪法蛋白质定量的新方法[J]. 北京生物医学工程, 2018, 37(4): 351-355, 391.

Jiang DY, Zheng HR. A new approach to protein quantification in label-free shotgun proteomics[J]. Beijing Biomedical Engineering, 2018, 37(4): 351-355, 391.

[12]?Jacob L, Combes F, Burger T. PEPA test: fast and powerful differential analysis from relative quantitative proteomics data using shared peptides[J]. Biostatistics, 2019, 20(4): 632-647.

[13]?He B, Shi J, Wang X, et al. Label-free absolute protein quantification with data-independent acquisition[J]. Journal of Proteomics, 2019, 200: 51-59.

[14]?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.

[15]?Giansanti P, Tsiatsiani L, Low TY, et al. Six alternative proteases for mass spectrometry-based proteomics beyond trypsin[J]. Nature Protocols, 2016, 11(5): 993-1006.

[16]?Gerster S, Kwon T, Ludwig C, et al. Statistical Approach to Protein Quantification[J]. Molecular & Cellular Proteomics, 2014, 13(2): 666-677.

[17]?Navarro P, Kuharev J, Gillet LC, et al. A multicenter study benchmarks software tools for label-free proteome quantification[J]. Nature Biotechnology, 2016, 34(11): 1130-1136.

[18]?Collins BC, Hunter CL, Liu Y, et al. Multi-laboratory assessment of reproducibility, qualitative and quantitative performance of SWATH-mass spectrometry[J]. Nature Communications, 2017, 8(1): 1-12.

[19]?Ludwig C, Gillet L, Rosenberger G, et al. Data-independent acquisition-based SWATH-MS for quantitative proteomics: a tutorial[J]. Molecular Systems Biology, 2018, 14(8): e8126.

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