Statistical Tools for RNA Folding Prediction and Antisense and Ribozyme Design for High Throughput Functional Genomics

Health Research Incorporated/New York State Department Of Health, Menands NY

Investigators

Abstract

0200970 Ding The long-term goal of this project is to develop statistical methods and software for improved prediction of RNA secondary structure, and for improved computational design of antisense oligonucleotides (oligos) and trans-cleaving ribozymes as the vehicles for high-throughput functional genomics and drug target validation in the post-genomic era. The objectives of the project include: 1) development of statistical algorithms and methodology for the rational design of antisense oligos and ribozymes; 2) experimental testing and improvement of antisense design method with experimental feedback data; 3) development of a statistical RNA folding software Sfold for RNA folding prediction and design of antisense oligos and ribozymes; and establishment and maintenance of a Sfold Web server for RNA folding and antisense oligo and ribozyme design. The Web server will be freely accessible by the scientific community for non-commercial applications. Of the estimated 30,000-40,000 genes in the human genome, definitive functions have been assigned to only a few percent. Through gene down-regulation, antisense oligos and ribozymes are important tools for the assignment of gene functions and for the validation of human therapeutic targets. There is compelling experimental evidence that the accessibility of the messenger or viral RNA by antisense oligos or ribozymes is most constrained by the secondary structure of the target. Recently, statistical approaches to the prediction of RNA secondary structure and antisense targets have shown promise and advantages over established methods. It is thus important to explore fully the advantages of statistical approaches to RNA folding prediction and antisense oligo and ribozyme design. It is expected that the methods and software developed from this project will be widely used for functional genomics applications. This grant is made under the Joint DMS/NIGMS Initiative to Support Research Grants in the Area of Mathematical Biology. This is a joint competition sponsored by the Division of Mathematical Sciences (DMS) at the National Science Foundation and the National Institute of General Medical Sciences (NIGMS) at the National Institutes of Health.