Assessment and Prediction of Human Proteotypic Peptide Stability for Proteomics Quantification

Mass spectrometry coupled to liquid chromatography is
one of the
most powerful technologies for proteome quantification in biomedical
samples. In peptide-centric workflows, protein mixtures are enzymatically
digested to peptides prior their analysis. However, proteome-wide
quantification studies rarely identify all potential peptides for
any given protein, and targeted proteomics experiments focus on a
set of peptides for the proteins of interest. Consequently, proteomics
relies on the use of a limited subset of all possible peptides as
proxies for protein quantitation. In this work, we evaluated the stability
of the human proteotypic peptides during 21 days and trained a deep
learning model to predict peptide stability directly from tryptic
sequences, which together constitute a resource of broad interest
to prioritize and select peptides in proteome quantification experiments.