Currently, no treatments exist to target the genetic mutations that cause cystic fibrosis. Scientists have discovered molecules that target CFTRs' defects, such as incorrect folding and fast recycling, and there are a few molecules that help correct how CFTR folds or slow down the CAL recycling truck. These molecules help keep copies of CFTR functioning in the cell membrane to maintain some balance between salt and water levels.
Donald and his graduate student Kyle Roberts thought that computer algorithms based on the structure of CAL and similar proteins could quickly generate several dozen more molecules for slowing recycling by CAL and increase the pool of potential cystic fibrosis treatments.
"Research shows that you only need a fraction of normal CFTR activity to alleviate cystic fibrosis symptoms, so keeping CFTR in the membrane by using our inhibitors could have a significant therapeutic effect," said Roberts, first author of the new study.
Donald and Roberts' algorithms searched several thousand potential inhibitors and ranked them based on how strongly it predicted each would bind with CAL. In collaboration with researchers at Dartmouth and in Germany, the scientists synthesized 11 of the highest-ranked sequences and used fluorescent light to measure each molecule's attachment to CAL.
The results show that many of the algorithm-generated molecules attach more strongly to CAL than the connection between CAL and CFTR in nature. The best computer-generated molecules also bind more efficiently to CAL than any previously reported inhibitor.
In a culture of human cells with the cystic fibrosis mutation, the best algorithm-generated inhibitor increased CFTR activity by 12 percent. Donald said the new molecule could be used in combination with another molecule, which corrects how CFTR proteins fold and raises CFTR's activity by 15 percent. The two molecules should work together and could increase CFTR's activity by
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