Internet Tool Predicts Relationship Between Mutation and Disease

With this data, they compiled datasets of human disease-associated AAS in the contexts of inherited monogenic disease, complex disease, functional polymorphisms with no known disease association, and somatic mutations in cancer, and compared them with respect to predicted functional sites in proteins. A detailed description of the research was published in the January 5, 2010, online edition of the journal Human Mutation.

"We now have a quantitative model of function using bioinformatic methods that can predict things like the stability of the protein and how its stability is disrupted when a mutation occurs," said senior author Dr. Sean Mooney, associate professor bioinformatics at the Buck Institute for Age Research. "Traditionally people have used a very time consuming process based on evolutionary information about protein structure to predict molecular activity. I think we are the first group to really quantitatively describe the universe of molecular functions that cause human genetic disease."

"We are happy to collaborate with scientists, to share data and help them better identify hypotheses about the specific mutations they might be interested in," said Dr. Mooney.

To facilitate such collaboration the investigators have made public on the Internet a tool designed to enhance the functional profiling of novel AAS. The tool can be accessed at a link given below.

Related Links:
Buck Institute for Age Research
AAS profiling tool