Zylstra, A. J.; Rovetta, M.; Vedelaar, S.; Bleischwitz, C.; Fülleborn, J. A.; van Oppen, Y. B.; Markus, H. P.; Korbeld, K. T.; Milias-Argeitis, A.; Buczak, K.; Schmidt, A.; Heinemann, M.

The cell division cycle is characterised by oscillatory dynamics in regulatory mechanisms and biosynthesis, coordinated with genome replication and segregation. To understand these dynamics, quantitative cell cycle-dependent protein concentration data is essential. Unfortunately, accurate resolution of cell cycle-dependent protein dynamics is challenging because single-cell proteomics is currently infeasible and bulk proteomics requires inherently imperfect cell synchronisation. Here, we developed a computational method to deconvolve cell cycle-dependent protein concentration dynamics and applied it to new budding yeast bulk proteome data. Key to this method was a yeast population model, parameterised with experimental cell cycle progression and volume growth data, for quantifying the desynchronisation in sampled populations. We performed deconvolution on 3373 proteins, using cross-validation to determine regularisation parameters, and identified 563 proteins with cell cycle-dependent dynamics. Many of these dynamics were consistent with known yeast biology and dynamic proteins were enriched for several metabolic process, extending previous observations and supporting the emerging picture of metabolic activity as varying substantially over cell cycle phases. We consider the generated cell cycle-resolved budding yeast proteome data a key resource.