RNA Secondary Structure Prediction

Transcription

1 RNA Secondary Structure Prediction

2 Outline 1) Introduction: RNA structure basics 2) Dynamic programming for RNA secondary structure prediction

3 The Central Dogma of Molecular Biology DNA CCTGAGCCAACTATTGATGAA transcription שעתוק RNA CCUGAGCCAACUAUUGAUGAA translation תרגום Protein PEPTIDE

4 The Central Dogma of Molecular Biology DNA transcription RNA translation Non Coding RNA - RNA molecule that is not translated into a protein - Have been found to have roles in a great variety of processes Protein

5 The Recent Example The Nobel Prize in Physiology or Medicine 2006 "for their discovery of RNA interference - gene silencing by double-stranded RNA" Andrew Z. Fire Craig C. Mello DNA RNA Protein

6 The RNA world Ribonucleic acid (RNA) is a nucleic acid polymer consisting of nucleotide monomers. It is transcribed (synthesized) from DNA. RNA serves as the template for translation of genes into proteins.

7 RNA s Structure Primary Structure Secondary Structure Tertiary Structure

8 RNA Basics RNA bases A,C,G,U Canonical Base Pairs A-U G-C G-U wobble pairing Bases can only pair with one other base. 23 Hydrogen Bonds more stable

9 RNA Secondary Structure Pseudoknot Single-Stranded Stem Interior Loop Bulge Loop Hairpin loop Junction (Multiloop)

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11 Trouble with Pseudoknots Images David Mount Pseudoknots cause a breakdown in the Dynamic Programming Algorithm. In order to form a pseudoknot, checks must be made to ensure base is not already paired this breaks down the recurrence relations

12 Sequence Alignment as a method to determine structure Bases pair in order to form backbones and determine the secondary structure Aligning bases based on their ability to pair with each other gives an algorithmic approach to determining the optimal structure Problem: number of potential structures grows exponentially with the number, n, of bases

13 RNA structure prediction by basepair maximization Input: a string over A,C,G,U A pairs with U, C pairs with G Output: a subset of possible base-pairs of maximal size such that no two base-pairs intersect.

14 Base Pair Maximization Dynamic Programming Algorithm (Nussinov-Jacobson 1978) Alignment Method Align RNA strand to itself Score increases for feasible base pairs S(i,j) is the number of base pairs in the folding of the subsequence of the RNA strand from index i to index j which results in the highest number of base pairs Each score S(i,j) independent of overall structure

15 Dividing the problem space S(i+1, j-1) + 1 S(i,k) S(k+1,j) i k k+1 j

16 Base Pair Maximization Dynamic Programming Algorithm S(i,j) is the number of base pairs in the folding of the subsequence of the RNA strand from index i to index j which results in the highest number of base pairs

17 Base Pair Maximization Dynamic Programming Algorithm Alignment Method Align RNA strand to itself Score increases for feasible base pairs Each score independent of overall structure Bifurcation adds extra dimension Initialize Fill first two diagonal Bases in squares cannot sweeping pair, similar Dynamic to arrays diagonally unmatched to Programming pair, similar 0 to possible paths alignment

18 Base Pair Maximization Dynamic Programming Algorithm S(i + 1, j 1) Initialize Fill in squares first two sweeping diagonal arrays diagonally to 0

19 Base Pair Maximization Dynamic Programming Algorithm S(i + 1, j) Fill in squares sweeping diagonally

20 Base Pair Maximization Dynamic Programming Algorithm S(i, j 1) Dynamic Programming possible paths

21 Base Pair Maximization Dynamic Programming Algorithm Bifurcation adds extra dimension Initialize Fill first two diagonal Bases in squares cannot sweeping pair, similar Dynamic arrays Bifurcation pair, similar diagonally to Programming 0 add values to matched possible for all k paths alignment i=0, Reminder: j=8, k = 1 : Bifurcation For all kmax in this case S(i,k) S(0,1) + + S(k S(2, + 1, 8) j)

22 Base Pair Maximization Dynamic Programming Algorithm Time Complexity: O(n 3 ) Space Complexity: O(n 2 ) Fill in squares sweeping diagonally

23 Base Pair Maximization - Drawbacks Base pair maximization will not necessarily lead to the most stable structure May create structure with many interior loops or hairpins which are energetically unfavorable Comparable to aligning sequences with scattered matches not biologically reasonable

24 Energy Minimization Thermodynamic Stability Mathews&Turner measured the energy behavior of structural components using experimental techniques Theory : Most Stable is the Most likey Zuker and Stiegler s Algorithm (mfold and Vienna) Computes RNA structure by finding the conformation with the minimum energy based on Mathews&Turner s formulations.

25 References How Do RNA Folding Algorithms Work?. S.R. Eddy. Nature Biotechnology, 22: , Chapter 10 in Biological sequence analysis. Probablistic models of proteins and nucleic acids. R. Durbin, S. Eddy, A. Krogh and G. Mitchison. RNA secondary structure and runtime prediction. Serafim Buzaglo s class notes. ture4.pdf L2: De novo prediction of RNA structure. Vineet Bafna and Pavale Pevzner s class slides.