The Leucine-rich repeat kinase 2 (LRRK2) not only plays a vital role in familial forms of Parkinson"s disease (PD) but is also considered as a risk factor for developing idiopathic PD. So far, no causative treatment for PD exists, ameliorating the progressive loss of dopaminergic neurons within the substatia nigra. The multi-domain protein LRRK2 represents a promising drug target for PD treatment. Besides a kinase and a G-domain, LRRK2 contains multiple scaffolding domains, not only being involved in keeping inactive LRRK2 in an auto-inhibited state but are also involved in protein-protein interactions making LRRK2 to a signaling hub possibly integrating multiple upstream signals or enabling a context-specific activation of downstream pathways by controlling the spatial localization of LRRK2. Therefore, an in-depth understanding of the LRRK2 protein-protein interaction (PPI) networks not only allows illuminating a deeper understanding of the associated signaling pathways but potentially also reveals new drug targets. Here, we present new BioID proximity proteomes of LRRK2 covering both known as well as yet unknown interactors. By clustering based on co-evolution within the PPI network combined with functional enrichment, we identified a subcluster showing highest co-evolution to LRRK2 which is enriched in cytoskeletal components linked to the centrosome and microtubules as well as primary cilia. In addition, by this approach, we could demonstrate that the resulting BioID datasets are enriched in direct interactions. Furthermore, structural prediction of binary interactions via AlphaFold-multimer revealed distinct groups of interactors engaging through specific interfaces and LRRK2 conformations. Finally, we identified distinct changes in the LRRK2 proximity proteome induced by the type I kinase inhibitor MLi2 and by RAB29 co-expression. These functional state-specific interactomes link LRRK2 to defined cellular sub-compartments. Noteworthy, MLi2-induced LRRK2 complexes are characterized by a significantly higher co-evolution and are not predicted to interact with the catalytic domains, particularly the kinase domain.

In conclusion, our novel approach combining proximity proteomics with the analysis of co-evolution and structural modelling helped to stratify the primary BioID datasets and lead to the identification of molecular networks linking LRRK2 to centrosomal biology and primary cilia.