Study: Web tool predicts molecular activity from mutations

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A study published online Jan. 5 in Human Mutation predicts the molecular causes of genetic diseases and has led to the creation of a Web tool available to academic researchers to study inherited genetic diseases.

The study focused on amino acid substitutions (AAS), which are genetically driven changes in proteins that can give rise to disease, and utilized a series of complex mathematical algorithms to predict activity stemming from the mutations.

"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 Sean Mooney, PhD, director of bioinformatics at Buck Institute in Novato, Calif., who led the research team. "Traditionally people have used a very time consuming process based on evolutionary information about protein structure to predict molecular activity," Mooney said.

As a first step, researchers used available databases of known sites of protein function and built mathematical algorithms to predict new sites of protein function, said Mooney.

"We looked for statistical differences between the percentage of mutations that fell into the same functional site from both non-disease and disease-associated AAS and looked to see if there was a statistically significant enrichment or depletion of protein activity based on the type of AAS. That data was used to hypothesize the molecular mechanism of genetic disease," said Mooney.

All the 40,000 AAS analysis was done by scientists at the Buck Institute and Cardiff University in Cardiff, Wales.

Mooney identified three different areas of research that could be furthered by use of the tool: to develop hypotheses about what those mutations are causing on a molecular level, to correlate molecular activity to the clinical severity or subtype of a disease, to identify mutations that drive the progression of the malignancy and also to gain insight into what is causing the mutations.

The Web tool, designed to enhance the functional profiling of novel AAS, has been made available at