Drilling for rare disease therapeutics

To cure rare genetic diseases, from cystic fibrosis to Niemann-Pick, scientists at Scripps Research have turned to a computational approach usually used to pinpoint the best spot for an oil well. By using the method to analyze the spatial relationships between different variants of a protein — instead of the relationships between test wells across a landscape — the researchers can obtain valuable information on how disease affects a protein’s underlying shape and how drugs can restore that shape to normal.

The new method, detailed today in the journal Structure, requires only a handful of gene sequences, collected from people with disease. Then, it determines how the structure of each corresponding variant protein is associated with its function, and how this functional structure can affect pathology and be repaired by therapeutics. To show its utility, the Scripps Research team used the method to show why existing drugs for cystic fibrosis fall short of curing the disease.

“This is an important step forward for treating rare diseases,” says senior author William Balch, PhD, professor of Molecular Medicine at Scripps Research. “The fact that we can get so much information from a few gene sequences is really unprecedented.”

Studies on inherited diseases often rely on techniques that determine the precise three-dimensional shape of a protein affected by disease. But genetic diseases can be caused by dozens — or even hundreds or thousands — of different changes to the same gene, called variants. Some of these variants destabilize or change the protein shape in ways that make isolating the protein for further investigation much more difficult than usual.

Balch, with Scripps Research senior staff scientist Chao Wang and staff scientist Frédéric Anglés, instead wanted to use natural variation to their advantage. For most genes in the human genome, numerous variants exist in the human population; some of these variants cause disease and others have little impact on biology and go unnoticed. So the group developed a method called variation-capture (VarC) mapping to analyze this natural array of gene sequences and determine the mechanism by which they each changed a protein’s structure to cause disease.

Balch’s group integrated a handful of machine learning and statistical tools into VarC, including the methods that oil companies use to draw inferences about the location of an oil reservoir using only a small number of test wells. With only a few gene sequences this let the researchers determine the most likely structural mechanisms driving function for each variant leading to disease, as well as model how drugs impacted those structural functions.

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