CF Therapies Only Weakly Aid ‘Core’ Region of CFTR Protein, Study Says
Using a machine learning approach based on genetic data, a trio of scientists at Scripps Research identified a region of the CFTR protein that is vital for its proper function, but is barely affected by existing cystic fibrosis (CF) treatments.
Researchers described their findings in the study “Triangulating variation in the population to define mechanisms for precision management of genetic disease,” published in the journal Structure.
The human genome contains thousands of genes that provide instructions for making proteins. Each individual’s code is unique, and consequently there are many variations in the proteins produced. Sometimes these variations result in problems — for example, mutations in the CFTR gene can cause CF — but other variations have more subtle effects on protein structure and workings.
Here, the researchers designed a novel machine learning approach called variation-capture mapping or VarC, which uses advanced statistical modeling to make predictions about how variations in the genetic code are likely to translate into differences in protein structure and, consequently, protein function.
“VarC provides a new approach that is distinct from algorithms that predict only structures, because VarC, as a genome-based tool, captures structural relationships in the context of native function of the [protein] fold framed by physical, chemical, and/or cell biologic properties found in the evolving human population,” the researchers wrote.
The algorithm is designed in “tiers” of increasing complexity, first determining how individual genetic variations are likely to influence the protein, and then building out to capture how the variations affect each other, and how they would affect interactions with other molecules like medications.
“When you want to treat patients, you really have to appreciate that different therapeutics might target different variants in completely different ways, and that’s why our approach that looks at many different variants all at once is so powerful,” said Chao Wang, a staff scientist at Scripps and study co-author, said in a press release.
“Our approach not only reveals how these variants contribute to each patient’s biology, but also connects them in a way that each variant can inform how to manage the others,” Wang added.
Notably, the algorithm only requires a few genetic sequences to make these predictions.
“The fact that we can get so much information from a few gene sequences is really unprecedented,” said William Balch, PhD, a professor at Scripps and the study’s senior author.
To demonstrate the utility of VarC, the researchers used the algorithm to analyze dozens of common CF-causing variations in the CFTR gene. They also evaluated how several approved CFTR modulators affect the proteins.
CFTR modulators are therapies that can act to boost CFTR protein function in people with certain disease-causing mutations. The team’s analysis included the triple-combination therapy Trikafta, as well as analyses of modulators individually and in dual combinations.
Among the notable findings, results indicated that a specific region toward the center of the CFTR protein is important for the protein’s function. This “core” region was defined by a particular structure called a YKDAD motif, and results showed that CF-causing mutations destabilized the region. However, the tested CFTR modulators did little to restore stability in this important region, models showed.
“We have discovered a key energetic core of the CFTR fold that is critical for CFTR function but only weakly impacted by current therapeutics,” the researchers wrote.
They proposed that identifying small molecules able to stabilize this region could substantially improve the ability of mutated CFTR to work more effectively, benefiting people with CF.
“In most drug discovery, you throw thousands of compounds at a protein and see which ones change it, often without fully understanding the mechanism,” Balch said. “To fix a thing, you must first understand the problem.”
The researchers also think that their VarC algorithm could have applications far beyond CF.
“We really think we can do this for any protein out there. It’s a fast track toward drug discovery for rare diseases that have been very hard and slow to study in the past,” Balch said.
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