Shotgun proteomics techniques infer the presence and quantity of proteins using peptide proxies, which are produced by cleavage of all isolated protein by a protease. Most protein quantitation strategies assume that multiple peptides derived from a protein will behave quantitatively similar across treatment groups, but this assumption may be false for biological or technical reasons. Here, I describe a strategy called peptide correlation analysis (PeCorA) that detects quantitative disagreements between peptides mapped to the same protein. Simple linear models are used to assess whether the slope of a peptide’s change across treatment groups differs from the slope of all other peptides assigned to the same protein. Reanalysis of proteomic data from primary mouse microglia with PeCorA revealed that about 15% of proteins contain one discordant peptide. Inspection of the discordant peptides shows utility of PeCorA for direct and indirect detection of regulated PTMs, and also for discovery of poorly quantified peptides that should be excluded. PeCorA can be applied to an arbitrary list of quantified peptides, and is freely available as a script written in R.
The data from microglia used in this study is a re-analysis of data from a prior publication https://doi.org/10.1016/j.jprot.2020.103753
Peptide-level quantities were analyzed for differences in quantities across condition groups compared to the other peptides in the same protein with peptide correlation analysis.
Please see the original publication for more details https://doi.org/10.1016/j.jprot.2020.103753
Five replicates of primary mouse microglia were treated with either ethanol, LPS, or control, and samples were analyzed with standard bottom up proteomics with DDA mass spectrometry.