zur Erlangung des akademischen Grades

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PredictionofConservedandConsensusRNAStructures

DISSERTATION

zurErlangungdesakademischenGrades

Doctorrerumnaturalium

Vorgelegtder

Fakult¨atf¨urNaturwissenschaftenundMathematik

derUniversit¨atWien

von

ChristinaWitwer

amInstitutf¨urTheoretischeChemieundMolekulare

Strukturbiologie

imM¨arz2003

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Contents

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Contents1 Introduction 1.1 General Context . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 RNA structure . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Objectives of this Work . . . . . . . . . . . . . . . . . . . . . . 1.4 Organization of this Thesis . . . . . . . . . . . . . . . . . . . . 2 Basics 4 4 5 7 8 10

2.1 Graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.2 Contact Structures . . . . . . . . . . . . . . . . . . . . . . . . 11 2.3 RNA Contact Structures . . . . . . . . . . . . . . . . . . . . . 13 2.3.1 2.3.2 2.3.3 2.3.4 2.3.5 2.3.6 Secondary Structure . . . . . . . . . . . . . . . . . . . 13 Bi-Secondary Structure . . . . . . . . . . . . . . . . . . 14 The Inconsistency Graph of a Diagram . . . . . . . . . 16 Color Partition of a Graph . . . . . . . . . . . . . . . . 17 Loop Decomposition of Secondary Structures . . . . . . 19 RNA Secondary Structure Representation . . . . . . . 21

2.4 Classi cations of Pseudoknots . . . . . . . . . . . . . . . . . . 23 2.4.1 2.4.2 H-type Pseudoknots . . . . . . . . . . . . . . . . . . . 23 Isambert-Siggia Decomposition of Secondary Structures 26

2.5 Matching Theory . . . . . . . . . . . . . . . . . . . . . . . . . 31 2.5.1 2.5.2 2.5.3 De nitions and Matching Concepts . . . . . . . . . . . 31 Maximum Cardinality Matching . . . . . . . . . . . . . 34 Maximum Weighted Matching . . . . . . . . . . . . . . 41

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3 Structure prediction - State of the Art

55

3.1 Comparative Sequence Analysis . . . . . . . . . . . . . . . . . 55 3.2 Thermodynamic Prediction of Secondary Structure . . . . . . 57 3.2.1 3.2.2 The Energy Model . . . . . . . . . . . . . . . . . . . . 57 The Algorithm . . . . . . . . . . . . . . . . . . . . . . 59

3.3 Thermodynamic Prediction of Secondary Structure Including Pseudoknots . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 3.3.1 3.3.2 Energy Models for Pseudoknots . . . . . . . . . . . . . 61 Algorithms . . . . . . . . . . . . . . . . . . . . . . . . 63

3.4 Combination of Phylogenetic and Thermodynamic Structure Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 3.4.1 3.4.2 Algorithms Based on a Set of Unaligned Sequences . . 66 Algorithms Based on a Multiple Sequence Alignment . 67

3.5 The Algorithm Alidot . . . . . . . . . . . . . . . . . . . . . . 70 4 Conserved Structural Elements of Picornaviruses 75

4.1 About Picornaviruses . . . . . . . . . . . . . . . . . . . . . . . 75 4.1.1 4.1.2 4.1.3 The Virion . . . . . . . . . . . . . . . . . . . . . . . . . 77 The Genome . . . . . . . . . . . . . . . . . . . . . . . 79

The Viral Life-Cycle . . . . . . .

. . . . . . . . . . . . 79

4.2 Methods and Tools . . . . . . . . . . . . . . . . . . . . . . . . 84 4.2.1 4.2.2 Schematic Drawing . . . . . . . . . . . . . . . . . . . . 84 Vienna Atlas of Viral RNA Structures . . . . . . . . . 87

4.3 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . 90 4.3.1 5’-Non-translated region . . . . . . . . . . . . . . . . . 92

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4.3.2 4.3.3

Coding Region . . . . . . . . . . . . . . . . . . . . . . 101 3’ Non-Translated Region . . . . . . . . . . . . . . . . 103 107

5 Predicting RNA Structures based on MWM

5.1 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 5.1.1 5.1.2 5.1.3 Base Pair Scoring . . . . . . . . . . . . . . . . . . . . . 108 Maximum Weighted Matching . . . . . . . . . . . . . . 110 Post-processing . . . . . . . . . . . . . . . . . . . . . . 111

5.2 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . 113 5.2.1 5.2.2 5.2.3 5.2.4 5.2.5 5.2.6 5.2.7 Signal Recognition Particle RNA . . . . . . . . . . . . 113 Ribonuclease P RNA . . . . . . . . . . . . . . . . . . . 120 Transfer-messenger RNA . . . . . . . . . . . . . . . . . 124 Prediction Based on ClustalW Alignment . . . . . . . . 127 The Role of the MWM Algorithm . . . . . . . . . . . . 128 Comparison to Other Methods . . . . . . . . . . . . . . 130 Alternative Thermodynamic Score . . . . . . . . . . . 132 133 136

6 Conclusion and Outlook A Appendix

A.1 List of Picornavirus Sequences . . . . . . . . . . . . . . . . . . 136 A.2 Conserved Structure Elements in Picornaviruses . . . . . . . . 143 A.3 Sequences Used for Hxmatch Predictions . . . . . . . . . . . . 161 A.4 Manual Pages . . . . . . . . . . . . . . . . . . . . . . . . . . . 163

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1

Introduction

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11.1

IntroductionGeneral Context

Proteins and nucleic acids, DNA and RNA, are the fundamental biopolymers in molecular genetics. In all prokaryotic and eukaryotic cells genetic information is stored in the form of DNA. In viruses the genetic material is either DNA or RNA. RNA plays an important role in the expression of genes. DNA is copied to messenger RNA (mRNA), which is subsequently decoded for protein synthesis. Translation is mediated by ribosomes, composed of ribosomal RNA (rRNA) and proteins, and transfer RNA (tRNA), which translates the codons of the mRNA into the amino acids of the protein. Proteins play a crucial role in most biological processes, the remarkable scope of their activity includes catalysis of chemical reactions, transport of small molecules and ions, control of growth and di erentiation of cells, and an import …… 此处隐藏：22289字，全部文档内容请下载后查看。喜欢就下载吧 ……