LFAQ: Toward Unbiased Label-Free Absolute Protein Quantification by Predicting Peptide Quantitative Factors
Year: 2018
Type: article
Abstract: Mass spectrometry (MS) has become a predominant choice for large-scale absolute protein quantification, but its quantification accuracy still has substantial room for improvement. A crucial issue is the bias between the peptide MS intensity and the actual peptide abundance, i.e., the fact that peptides with equal abundance may have different MS intensities. This bias is mainly caused by the diverse physicochemical properties of peptides. Here, we propose an algorithm for label-free absolute protein quantification, LFAQ, which can correct the biased MS intensities by using the predicted peptide quantitative factors for all identified peptides. When validated on data sets produced by different MS instruments and data acquisition modes, LFAQ presented accuracy and precision superior to those of existing methods. In particular, it reduced the quantification error by an average of 46% for low-abundance proteins. The advantages of LFAQ were further confirmed using the data from published papers.
Source: Analytical Chemistry
Language: English
Subfield: Spectroscopy
Field: Chemistry
Domain: Physical Sciences
Open Access status: green
Funders: National Natural Science Foundation of China, Chinese Academy of Sciences, Ministry of Science and Technology of the People's Republic of China, National Key Research and Development Program of China, National Center for Mathematics and Interdisciplinary Sciences, Chinese Academy of Sciences
Awards: G1053236471, G1193943778, G1711591602, G2381821213, G2953583009, G3280040305, G3413908168, G3529746609, G4316344736, G4332384139, G5197074785, G5292479265, G5485389118, 基于高分辨率质谱的蛋白质组定量可靠性评估策略及绝对定量算法的研究, G6705486475, G7321106489, G7540475375, G7578692726, G7582427561, G7820668467, G8710076561, G8908567792
OpenAlex ID: W2953367041