Statistical Approaches to RNA Secondary Structure Prediction and Applications

Title: Statistical Approaches to RNA Secondary Structure Prediction and Applications
Speaker: Ye Ding
Wadsworth Center, New York State Department of Health

Abstract

Abstract: RNAs are versatile regulators of gene expression. RNA secondary structures are known to be important for regulatory functions by various types of RNAs. An RNA molecule, particularly a long-chain mRNA, may have a population of structures in the cell. Furthermore, multiple structures have been demonstrated to play important functional roles. Thus a representation of the ensemble of probable structures is of interest. We developed a statistical algorithm to sample rigorously and exactly from the Boltzmann ensemble of secondary structures, and introduced the notion of centroid structures as a new class of structure predictors. These approaches can overcome inherent limitations in conventional algorithms and are the bases for our Sfold RNA folding program (http://sfold.wadsworth.org).

MicroRNAs are small non-coding RNAs that repress protein synthesis by binding to target mRNAs in multicellular eukaryotes. Target identification of microRNA targets is essential to fully understand this new dimension of the complex gene regulatory networks. By employing a two-step model for modeling microRNA:target hybridization, we found that target secondary structure has a major impact on target recognition by microRNAs. Based on analyses of large microRNA targeting data using the model parameters and other sequence and conservation features, we have recently developed a novel computational framework that offers major improvement over established algorithms for prediction of microRNA targets. Computational tools are available through Sfold web server.

http://www.cs.ubc.ca/labs/beta/Courses/BioinfoReadingGroup/winter2011/07Feb2012.html

not really protein structure but mRNA structure ...