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Looking for Secondary Structure


  • Title: Assistant Professor, University of Rochester
  • Education: PhD, University of Rochester, 2002; MD, University of Rochester, 2003
  • Recommended by: Alan Guttmacher, Doug Turner

The most widely known secondary structure of RNA is the cloverleaf-shaped configuration of transfer RNA. The three loops of the cloverleaf have specific roles during translation at the ribosome. There are, however, other RNA arrangements that occur, and their functions are not always as clear. The University of Rochester's David Mathews is building algorithms to predict secondary structures of RNA and applying that knowledge to finding novel, noncoding RNA in the genome and uncovering what those RNAs do. In addition, he says that knowing more about secondary RNA structure might help researchers design better and more effective siRNAs. "We're best known for our work in predicting secondary structure," Mathews says of his lab.

Mathews' group wants to be able to predict, from any given sequence, if there will be an RNA structure and, if so, what base pairs will form. As a bioinformatics and computational biology group, the team has developed an algorithm to detect the RNA structure common to two homologous sequences. "We'd like to be able to, given a set of homologous sequences, be able to determine the secondary structure of those sequences with 100 percent accuracy. That would improve our ability to determine three-dimensional structures and it would improve our ability to find noncoding RNAs," Mathews says.

Currently, the accuracy with which Mathews and others working in the area of predicting secondary structure ranges. With a single sequence, he says, the predictions are about 70 percent accurate; with multiple sequences,- that figure can reach 90 percent. He says people working on this problem use different approaches, which has resulted in variation in accuracy. "But I'd say none is perfect," he says.

Studying RNA structure leads to questions plaguing genomics, Mathews says —in particular to the attention paid to noncoding RNAs. "They may, for example, explain why higher organisms are more complicated than lower ones even though we don't have dramatically more protein-coding genes. We may have many more noncoding RNAs that are functioning at the level of RNA, so there's a lot of interest to be able to find these genome sequences," he says. Mathews' group has also developed an algorithm that searches genomes for noncoding RNAs.

Another area that RNA secondary structure knowledge will help is in designing effective siRNAs, Mathews says. Providing complementary short duplex RNAs may silence a message, but it isn't always effective. "We've looked at that as a problem of equilibrium binding and so approaching it that way we have an algorithm that can design effective siRNAs for a given mRNA target," he says.

Looking ahead

Mathews says the goals for the next five years are to find all the noncoding RNAs and to move toward better prediction of three-dimensional RNA structures. "If we are able to find more in noncoding RNAs, then we have more information to use to determine three-dimensional structures — and if we're better at predicting three-dimensional structures, we're going to be able to more effectively find noncoding RNAs with better sensitivity and specificity," he says.

Publications of note

In 2006, Mathews and his team published a paper in BMC Bioinformatics. "That's where we apply our algorithm for finding common secondary structures and use that to find regions in genome alignments that are likely to contain structured RNA," Mathews says.

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