The Von Hippel-Lindau (VHL) gene is a tumor suppressor gene, which plays an important role in the hypoxia response. Mutations within the VHL gene are known to cause VHL disease, which is characterized by the formation of cysts and tumors in multiple organs of the body, including the central nervous system, retina, and kidneys. The most common type of tumor to develop as a result of this disease is clear cell renal carcinoma (ccRCC).A major challenge in clinical practice is determining the probability that a tumor will form from a given mutation in the VHL gene. To overcome this limited performance, we characterized the effects of mutations within VHL using in silico biophysical tools describing changes in protein stability, dynamics and affinity to binding partners to provide insights into the structure-phenotype relationship. Preliminary results show that our model can identify ccRCC-causing missense mutations with an accuracy up to 0.79, and MCC up to 0.51 on 10-fold cross validation. Our predictive model achieved comparable performance across two separate independent test sets, out performing previous methods. This work offers a promising initial performance for an ultimately clinically-applicable tool.