Computational Simulations Predict Inhibitors Targeting Parkinson’s Disease

Parkinson’s disease is a degenerative neurological condition that impairs a person’s capacity to regulate their body’s movements. Neurons in the substantia nigra area of the brain gradually degenerate in this illness. Alpha-synuclein protein aggregates in these neurons to create Lewy bodies, which are toxic, fibril-like structures. Thus, blocking these fibrils may be a useful treatment approach for Parkinson’s disease. We hypothesize that chemical substances that can bind at the interface can inhibit this binding and stop fibrils from forming. As a result, we have screened 5239 chemical compounds against the alpha-synuclein protein using molecular docking simulations, which stops them from clumping and inhibits the production of fibrils. We chose the top three substances that strongly bind to the protein.  Since all of these ligands attach to the protein’s hydrophobic region, it is likely that hydrophobic medications—which can pass through the blood-brain barrier—will work better to treat this illness. By docking inhibitor-bound and free fibrils together, we confirmed our hypothesis and discovered that the inhibitor prevents the interaction between the fibril interface. Furthermore, to validate the molecular docking data, we have also computed the druggable on the surface of the fibril using the P2Rank web server, a machine learning-based method. The work promises better therapeutic alternatives in the future and opens up new pathways for innovative Parkinson’s disease treatment.

Keywords: Parkinson’s Disease, Blood-Brain Barrier, Alpha-Synuclein.