Systems biology of the motif-mediated cell cycle interactome

Abstract

The eukaryotic cell cycle is a tightly controlled series of events leading to cell division. It relies on a complex network of regulatory proteins, serving as checkpoints to oversee DNA replication and ensure orderly cell division. Disruption of this system can lead to uncontrolled cell growth, as seen in diseases like cancer. Cell cycle regulation occurs at multiple levels yet largely depends on protein and phosphorylation abundance dynamics. Protein abundance is defined by the balance between transcription and protein degradation at different cycle stages. Whereas, kinases and phosphatases manage protein phosphorylation, further modulating cell cycle progression. The primary objective of this project is to study the spatiotemporal dynamics of the cell cycle machinery. To achieve this goal, cell cycle analysis methods have been developed and applied to create a high-confidence dataset of proteins and phosphorylation sites oscillating in a cell cycle-dependent manner. These data were integrated with available information on protein, phosphorylation site, and mRNA abundance, and enriched with complementary annotations of protein half-life, localisation, cancer dependency, complexes, and interaction interfaces. This comprehensive approach aims to link cell cycle-dependent abundance dynamics to functional changes, illuminating the biological role of oscillating proteins and phosphorylation events. An online database, Cell Cycle Database (CCdb), has been created to disseminate this data, providing an in-depth resource for the cell cycle community. Next, I focussed on characterising the short linear motifs (SLiMs)-based interactome of the cell cycle. SLiMs are short peptides that mediate transient and low affinity protein-protein interactions. SLiMs are particularly crucial for cell cycle progression, and the functions of most cell cycle regulators are coordinated directly or indirectly through SLiMs. A motif specificity comparison method has been developed and applied to create two novel tools: CompariPSSM, which quantifies the similarity between motifs binding determinants, and SLiMScan, which searches with motifs binding determinants to discover uncharacterised SLiMs. These tools were applied to discover cell cycle state-specific motif-mediated interactions, cell cycle motifs, and the mechanisms that control the conditionality of these motifs on a proteome-wide scale.