Modeling Exopeptidase Activity from LC-MS Data

Recent studies demonstrate that the peptides in the serum of cancer patients that are generated (ex vivo) as a result of tumor protease activity can be used for the detection and classification of cancer. In this paper, we propose the first formal approach to modeling exopeptidase activity from liquid chromatography-mass spectrometry (LC-MS) samples. We design a statistical model of peptidome degradation and a Metropolis-Hastings algorithm for Bayesian inference of model parameters. The model is successfully validated on a real LC-MS dataset. Our findings support the hypotheses about disease-specific exopeptidase activity, which can lead to new diagnostic approach in clinical proteomics.