Study Rationale:
Mutations in the GBA gene are a risk factor for Parkinson’s disease (PD) and cause deficits in the protein GCase. GCase reaches its final destination in the cell, the lysosome, via its transport protein LIMP-2. Mutations found in PD patients result in lysosomal dysfunction of GCase, partly due to instable protein as well as insufficient transport of GCase to the lysosome. Identification of the exact interaction site between GCase and LIMP-2 might represent a novel target for the development of therapeutic approaches, in order to stabilize GCase and boost its lysosomal transport as well as activity.
Hypothesis:
We would like to identify the exact interaction site between GCase and LIMP-2 and use this information to develop compounds that mimic this interaction in order to stabilize GCase and enhance its activity.
Study Design:
We will use the soluble parts of both proteins and optimize conditions so that both form a protein complex. We will then use an electron microscope and at very high magnifications of about 50k, we will take pictures of this complex. Next, we will utilize smart computer algorithms to calculate a 3D model of this protein complex. From this 3D model, we can then deduce the interface of both proteins and use it for the development of GCase stabilizing compounds.
Impact on Diagnosis/Treatment of Parkinson’s Disease:
With the exact localization of the interaction site we will be able to characterize mutations in GCase and predict those affected by a transport deficit. Additionally, we hope to develop better compounds that interact with GCase based on the protein-protein interface. We hope that those compounds may one day serve as treatment options for GBA-induced parkinsonism by boosting GCase activity.
Next Steps for Development:
After solving the structure of the LIMP-2/GCase complex, we will design peptides derived from the binding interface. These peptides will first be used in cell culture to assess a stabilizing effect towards GCase variants. Following, they could be tested in disease models and potentially make their way to the clinics.