北京生物醫(yī)學工程

一種基于加權非負最小二乘的蛋白質定量方法

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

作者：方言鄭浩然

單位：中國科學技術大學計算機科學與技術學院(合肥 230027) 通信作者：鄭浩然。E-mail: [email protected]

分類號：R318.04

出版年·卷·期（頁碼）：2022·41·1（62-67）

摘要：

目的提出一種綜合利用肽段信息的蛋白質定量方法WeQuant (weighted peptide-based protein quantification)，以提高蛋白質定量的通量與準確度，特別是低豐度蛋白質。方法基于肽段和蛋白質的關系，按照肽段的豐度與來源對所有肽段進行加權，并利用肽段和蛋白質的等量關系建立加權非負最小二乘模型，從而得到蛋白質的相對豐度。結果與傳統(tǒng)蛋白質定量方法相比，WeQuant在實驗數(shù)據(jù)集上顯著增加了有效定量的蛋白質數(shù)量，并在不同豐度范圍均達到了更高的定量準確度。此外，WeQuant能夠有效定量未被其他方法報告的低豐度蛋白質。結論本文提出的基于加權非負最小二乘的模型能夠克服對高豐度肽段和唯一肽段的依賴，實現(xiàn)對不同豐度范圍的蛋白質進行準確定量。

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.

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