BayesENproteomics: Bayesian Elastic Nets for Quantification of Peptidoforms in Complex Samples

Multivariate regression modelling
provides a statistically powerful
means of quantifying the effects of a given treatment while compensating
for sources of variation and noise, such as variability between human
donors and the behavior of different peptides during mass spectrometry.
However, methods to quantify endogenous post-translational modifications
(PTMs) are typically reliant on summary statistical methods that fail
to consider sources of variability such as changes in the levels of
the parent protein. Here, we compare three multivariate regression
methods, including a novel Bayesian elastic net algorithm (BayesENproteomics)
that enables assessment of relative protein abundances while also
quantifying identified PTMs for each protein. We tested the ability
of these methods to accurately quantify expression of proteins in
a mixed-species benchmark experiment and to quantify synthetic PTMs
induced by stable isotope labelling. Finally, we extended our regression
pipeline to calculate fold changes at the pathway level, providing
a complement to commonly used enrichment analysis. Our results show
that BayesENproteomics can quantify changes to protein levels across
a broad dynamic range while also accurately quantifying PTM and pathway-level
fold changes.