Nucleic acid Structure and Recognition - L.J. Maher

Meet Our Students

John-paul Bida

Building Transcription Factors from RNA

With training in mathematics and computer science from Johns Hopkins University, John-paul Bida entered Mayo Graduate School in 2006 as a perfect candidate to tackle a thesis project involving the holy grail of computational biology: predicting how macromolecules fold. His quantitative thinking and engineering aptitude made the transition fast and fascinating. John-paul's project involves a bold proposition: the unknown folded structures of RNA molecules can be accurately predicted by computer simulations. The challenge is to start with only the sequence of the RNA and then use a library of parameters deduced from known RNA structures to accurately and quickly predict the unknown RNA structure. Plenty of attempts have been made in the past, but even the smallest RNA structures have been predicted inefficiently or not at all. Accurate prediction of RNA structure is being tested by comparing predictions to actual structures. John-paul has introduced fresh insights into his approach, including vastly faster computational methods to explore the kinds of compact folded structures known to be preferred by natural RNAs. With an optimized RNA structure prediction approach comes the potential to design RNA molecules for engineered purposes. For example, the Maher lab is interested in designing RNA-based inhibitory decoys that bind to transcription factors. Such RNAs can be selected from random libraries and have shown the unusual ability to mimic duplex DNA in structure. Designing new RNA decoys for additional transcription factor targets is a potential application of Bida's approaches. If John-paul's strategy is successful, it might be extended to improve structure prediction for proteins.