An algorithm has been developed for improved prediction for RNA secondary structure. Novel computational tools have been developed for the rational design of antisense oligonucleotides, trans-cleaving ribozymes and siRNAs. This algorithm enables a better characterization of potential biological targets on mRNA and viral RNA.
An antisense oligonucleotide can down-regulate gene expression by binding to target mRNA through complimentary base pairing and inhibit its translation. Antisense binding to the RNA target is a necessary step before target cleavage by trans-cleaving ribozymes. For antisense oligonucleotides and ribozymes, it is well understood that the target accessibility is primarily determined by the secondary structure of the target. Recently, gene slicing by siRNAs has been well demonstrated and there is experimental evidence that the potency of siRNAs in mammalian applications is also determined by target accessibility. In the post-genomic era,these RNA-targeting techniques have emerged as increasingly important for high throughput functional genomics and drug target validation.
- Target accessibility prediction and rational design of antisense oligonucleotides
- Target accessibility prediction and rational design of trans-cleaving ribozymes
- Target accessibility prediction and RNA duplex thermodynamics for rational siRNA design
- Energetic characteristics of hybridization between a structured target and a microRNA
- Complete computational screening of the entire target mRNA or viral RNA
- Consideration of alternative mRNA or viral RNA structures through a statistically representative sample of probable secondary structures
- Assignment of probabilities as a measure of confidence for the prediction of target sites
- Demonstrated substantial improvement in prediction over other algorithms
Potential for high throughput applications to drug target validation and functional genomic
Areas of Potential Utility:
- RNAi knockdown of transcript in insects leading to control of harmful insect populations in crops
Research and Drug Development
- Creating effective siRNA compounds that are delivered into human cells resulting in the silencing of genes and viruses responsible for human diseases; gene modulation by microRNAs, microRNA mimics, sponges and decoys.
State of Development:
- The code for the algorithm has been extensively tested and long mRNA sequences (> 10,000 nt) have been analyzed
- Target predictions and rational design methods have been validated by comprehensive data analysis and experimental testing in both in vitro and in vivo systems
Sfold is avaible at no charge to the scientific community for non-commercial applications. The technology is available under license for commercial applications.
Dr. Ye Ding is a statistician by training. Since the late 1990s, he has been working on novel algorithms for RNA secondary structure predictions and applications to the rational design of RNA-targeting nucleic acids and the identification of targets for regulatory RNAs.
He is the developer of the Sfold software for RNA folding and applications that have been available since April 2003.
The Sfold web server has been accessed by scientists around the world to fold over 120,000 nucleotide sequences. It has also been used for teaching bioinfomatics and biochemistry. Sfold and Dr. Ding's work have been featured on a Nucleic Acids Research cover, Science NetWatch, Nature Research Highlights, four Faculty of 1000 Biology Evaluations, a feature by Genome Technology and a four-page keynote interview report by Research Media.
Chan CY, Carmack CS, Long DD, Maliyekkel A, Shao Y, Roninson IB, Ding Y. A structural interpretation of the effect of GC-content on efficiency of RNA interference. BMC Bioinformatics. 2009 Jan 30;10 Suppl 1:S33.
Long D, Chan CY, Ding Y. Analysis of microRNA-target interactions by a target structure based hybridization model. Pac Symp Biocomput. 2008:64-74.
Chan CY, Ding Y. Boltzmann ensemble features of RNA secondary structures: a comparative analysis of biological RNA sequences and random shuffles. J Math Biol. 2008 Jan;56(1-2):93-105. Epub 2007 Oct 2.
Shao Y, Chan CY, Maliyekkel A, Lawrence CE, Roninson IB, Ding Y. Effect of target secondary structure on RNAi efficiency. RNA. 2007 Oct;13(10):1631-40. Epub 2007 Aug 7.
Long D, Lee R, Williams P, Chan CY, Ambros V, Ding Y. Potent effect of target structure on microRNA function. Nat Struct Mol Biol. 2007 Apr;14(4):287-94. Epub 2007 Apr 1.
Shao Y, Wu Y, Chan CY, McDonough K, Ding Y. Rational design and rapid screening of antisense oligonucleotides for prokaryotic gene modulation. Nucleic Acids Res. 2006;34(19):5660-9. Epub 2006 Oct 11.
Ding Y, Chan CY, Lawrence CE. Clustering of RNA secondary structures with application to messenger RNAs. J Mol Biol. 2006 Jun 9;359(3):554-71. Epub 2006 Feb 2.
Ding, Y. Statistical and Bayesian approaches to RNA secondary structure prediction. RNA. 2006 Mar;12(3):323-31.
Chan CY, Lawrence CE, Ding Y. Structure clustering features on the Sfold Web server. Bioinformatics. 2005 Oct 15;21(20):3926-8. Epub 2005 Aug 18.
Ding Y, Chan CY, Lawrence CE. RNA secondary structure prediction by centroids in a Boltzmann weighted ensemble. RNA. 2005 Aug;11(8):1157-66.
Ding Y, Chan CY, Lawrence CE. Sfold web server for statistical folding and rational design of nucleic acids. Nucleic Acids Res. 2004 Jul 1;32 (Web Server issue):W135-41.
Ding Y, Lawrence CE. A statistical sampling algorithm for RNA secondary structure prediction. Nucleic Acids Res. 2003 Dec 15;31(24):7280-301.
Ding, Y., Lawrence, C.E. (2002) Statistical algorithms for prediction of secondary structure of nucleic acids and for prediction of effective targets and rational design of antisense oligonucleotides andribozymes for human therapeutics and functional genomics and drug target validation.
Ding, Y. Lawrence, (2002) Rational statistical design of antisense oligonucleotides for high throughput functional genomics and drug target validation. Statistics Sinca 12, 273-296.
Ding Y, Lawrence CE. Statistical prediction of single-stranded regions in RNA secondary structure and application to predicting effective antisense target sites and beyond. Nucleic Acids Res. 2001 Mar 1;29(5):1034-46.
Ding, Y., Lawrence, C.D. (1999) A Bayesian statistical algorithm for RNA secondary structure prediction. Computers & Chemistry 23, 387-400,1999.
Diane L. Borghoff, B.S., M.S.
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