Evaluation of empirical rule of linearly correlated peptide selection (ERLPS) for proteotypic peptide-based quantitative proteomics.

Precise protein quantification is essential in comparative proteomics. Currently, quantification bias is inevitable when using proteotypic peptide-based quantitative proteomics strategy for the differences in peptides measurability. To improve quantification accuracy, we proposed an "empirical rule for linearly correlated peptide selection (ERLPS)" in quantitative proteomics in our previous work. However, a systematic evaluation on general application of ERLPS in quantitative proteomics under diverse experimental conditions needs to be conducted. In this study, the practice workflow of ERLPS was explicitly illustrated; different experimental variables, such as, different MS systems, sample complexities, sample preparations, elution gradients, matrix effects, loading amounts, and other factors were comprehensively investigated to evaluate the applicability, reproducibility, and transferability of ERPLS. The results demonstrated that ERLPS was highly reproducible and transferable within appropriate loading amounts and linearly correlated response peptides should be selected for each specific experiment. ERLPS was used to proteome samples from yeast to mouse and human, and in quantitative methods from label-free to O18/O16-labeled and SILAC analysis, and enabled accurate measurements for all proteotypic peptide-based quantitative proteomics over a large dynamic range.