LFAQ: Toward Unbiased Label-Free Absolute Protein Quantification by Predicting Peptide Quantitative Factors

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.