Understanding RNA Types and Their Secondary Structures in Bioinformatics

1. RNA BioinformaticsGenes and Secondary Structure Anne Haake Rhys Price Jones & Tex Thompson

2. Types of RNA • Coding • mRNA (messenger) • Non-Coding • rRNA (ribosomal) • tRNA (transfer) • snRNA (small nuclear;splicing) • RNAseP (ribozyme) • siRNA (small inhibitory) • Others…

3. RNA genes? Recall: • Protein-coding genes; we have relatively good methods • Ab initio • Homology-based • RNA genes • Poor sequence homology • Secondary structure useful

4. Pictures from the Web

5. Functional Roles: RNA Secondary Structure • mRNA • Regulation of transcription termination • Regulation of translation initiation • rRNA • ribosomal structure • tRNA • adaptor in translation • RNA interference • Regulation of gene expression • Anti-viral activity

6. mRNAs have functional secondary structures • e.g. Transcription Termination Signal

7. rRNA

8. tRNA

9. Ribozymes: Enzymes made of RNA • RNA molecules in Tetrahymena were shown to splice out introns without the aid of proteins • Ribozymes have been discovered in higher organisms, and may play a role in processing mRNA

10. Ribonuclease P • Enzyme found in many organisms, cleaves the 5’ end of tRNA molecules • Heterodimer consisting of a protein molecule and an RNA molecule • Without RNA molecule, Ribonuclease P loses all activity • Without protein, Ribonuclease P shows only reduced activity

11. Pictures from the Web • http://www.mbio.ncsu.edu/RNaseP/rna/threed/threed.html • http://proteinexplorer.org (molecule 1d6t)

12. Discovery of Ribonuclease P http://www.mun.ca/biochem/courses/3107/Lectures/Topics/Splicing.html

13. Can identify RNA genes that belong to a known family • Infer secondary structure by comparing sequences (multiple alignments) • e.g. Look for covariance; positions that covary to maintain Watson-Crick base-pairing;implies role in secondary structure • Rfam: a collection of multiple alignments and covariance models for ncRNAs • Rfam

14. Prediction of RNA Secondary Structure • Find the configuration that maximizes the number of base pairs • Scoring all possibilities would be computationally expensive • Use dynamic programming • Thermodynamics approach • Mfold: uses an energy minimization method of Zuker • http://www.bioinfo.rpi.edu/applications/mfold/ • http://bioweb.pasteur.fr/seqanal/interfaces/mfold-simple.html

15. RNA Interference • Breakthrough of the year in 2002 • Discovered in C. elegans • dsRNA involved in sequence-specific gene silencing • Post-transcriptional gene silencing • 21-25 nucleotide dsRNAs (siRNAs) facilitate the degradation of homologous RNAs

16. RNAi • Useful for gene targeting to study function • Other techniques for gene targeting • “knock-out” by homologous recombination • Antisense • siRNA-direct “knock-down” has potential to allow systematic study of each gene in a pathway • siRNA might allow silencing of pathogenic genes or pathogens (e.g. viruses)

17. Mechanism • siRNAs: 21-23 nt dsRNA with 2-3 nt 3’ overhangs • Produced from cleavage of long dsRNAs by “Dicer” enzyme • Form a siRNA-protein complex “RISC” • Cleaves homologous mRNA target • Also can start with a hairpin precursor rather than dsRNA

18. siRNA demo • RNA Interference links and refs

19. Introduction to Proteomics Techniques & Computational Issues

20. Experimental Techniques • As with transcriptome analysis, proteome analysis is limited by the techniques currently available • But, proteome analysis even more difficult and less precise due to the nature of proteins

21. Two-dimensional Gel Electrophoresis • 2D gels • First dimension: isoelectric focusing • Separates proteins on basis of charge • Second dimension: SDS-PAGE • Able to resolve thousands of proteins in a single gel • Proteins are usually radioactively labeled

22. Challenges of 2D gel Analysis • Reproducibility • Software is available to assist in aligning the spots between gels and integrating the intensities of the spots • Identification of the proteins of interest • Some underrepresented e.g. membrane proteins • Some below levels of detection • Which protein is represented by each spot? • Mass spectrometry has greatly enhanced ability to identify individual proteins

23. 2-Dimensional Gel Electrophoresis • http://us.expasy.org/ • For other examples and tools

24. Mass Spectrometry • Able to uniquely identify the proteins associated with individual spots in 2D gels • Spots are excised from gels • Proteins are digested into peptide fragments using proteases such as trypsin • Trypsin cleaves peptide bonds next to the amino acids lysine and arginine. • Peptides are ionized for Mass Spec analysis • For a quick explanation of Mass Spec see: http://www.jeol.com/ms/docs/whatisms.html

25. Mass Spectrometry • Generates a peptide mass fingerprint • Computational challenge: the fingerprint must be matched up with the theoretical mass spectrum of the proteins derived from genomics databases • Analysis software ProteinProspector

26. Protein Microarrays • High-throughput techniques similar to gene chips • Probes (e.g. antibodies) attached to chips • Fluorescently-labeled proteins washed over chips • Fluorescence intensity indicative of relative levels • Variations include protein-compound (drug) interactions, protein-DNA etc.

27. Protein Microarrays • Major problems with analysis of proteins in this way • Protein-protein binding not determined by strict rules as it is in nucleic acids (base-pairing) • One protein may bind several others on the chip • Protein interactions very sensitive to chemistry • Application of protein arrays often used as a follow-up to gene chip studies

28. http://speedy.embl-heidelberg.de/gtsp/flowchart2.html