We used 3DVizSNP to sort and prioritize variants according to their 3D structural information. Using data from The Cancer Genome Atlas Program (TCGA), we filtered a list of 27,526 variants to identify mutations that were most likely to be damaging. We then fed those 901 resulting variants into the 3DVizSNP tool. We identified a mutation in a Cytochrome P450 protein (the AlphaFold2 structure prediction is shown above), which helps protect against cancer. The mutation changes a neutral leucine amino acid (A) to a charged arginine (B), and results in a destabilized version of the protein.
We are conducting a survey to assess the compute-intensive resource needs of the cancer research community. Regardless of your area of cancer research, your insights will contribute to a better understanding of computing needs and challenges for research that involves large amounts of data and analysis.