Timepoint Selection Strategy for In Vivo Proteome Dynamics from Heavy Water Metabolic Labeling and LC–MS

Protein
homeostasis, proteostasis, is essential for healthy cell
functioning and is dysregulated in many diseases. Metabolic labeling
with heavy water followed by liquid chromatography coupled online
to mass spectrometry (LC–MS) is a powerful high-throughput
technique to study proteome dynamics in vivo. Longer labeling duration
and dense timepoint sampling (TPS) of tissues provide accurate proteome
dynamics estimations. However, the experiments are expensive, and
they require animal housing and care, as well as labeling with stable
isotopes. Often, the animals are sacrificed at selected timepoints
to collect tissues. Therefore, it is necessary to optimize TPS for
a given number of sampling points and labeling duration and target
a specific tissue of study. Currently, such techniques are missing
in proteomics. Here, we report on a formula-based stochastic simulation
strategy for TPS for in vivo studies with heavy water metabolic labeling
and LC–MS. We model the rate constant (lognormal), measurement
error (Laplace), peptide length (gamma), relative abundance of the
monoisotopic peak (beta regression), and the number of exchangeable
hydrogens (gamma regression). The parameters of the distributions
are determined using the corresponding empirical probability density
functions from a large-scale dataset of murine heart proteome. The
models are used in the simulations of the rate constant to minimize
the root-mean-square error (rmse). The rmse for different TPSs shows
structured patterns. They are analyzed to elucidate common features
in the patterns.