Computational Simulation of Progranulin for Blood-Based Detection of Parkinson's Disease up to 7 Years Before symtom OnsetYears Before Symptom Onset

Parkinson’s disease (PD) is a neurological degenerative disease that affects signal pathways in the brain, leading to motor control issues, as well as various other conditions. Detecting PD is challenging because its symptoms develop relatively slowly and tend to be initially subtle. PD’s symptoms overlap with different neurological disorders, allowing it to be easily confused with disorders like Essential Tremor or Multiple System Atrophy (MSA). However, there may be a way to detect it years ahead of it worsening. Recently, the granulin protein has been linked to the onset of PD, meaning that it has the potential to be used as a biomarker. Our research aims to locate granulin precursor proteins cleaved into the granulin proteins.  “Antibody” is a general term used to classify proteins that bind to various structures, allowing the body to identify and/or destroy them. We hypothesize that antibodies binding to the protein's surface could be used to detect and diagnose PD. We will use HDOCK to find binding sites between the granulin precursor and a specific antibody. We will use Prodigy for binding energy. We will use P2Rank to predict the best binding sites on the granulin precursor. Based on the visual inspection and binding energy, we have selected antibody 1IGT as the most appropriate candidate for granulin protein detection. By finding the antibody that best binds to the granulin precursor, we can modify it to help us locate it.  This would allow doctors to diagnose PD much earlier and allow for faster and earlier treatment.