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Prediction of Secondary Structure of RNA Swetha Nandyala Slide 2 Overview Introduction How RNA Secondary Structure is obtained ? Various types of loops possible in RNA Secondary Structure Motivation Resource Implements the Algorithms to be Understood Algorithm Nussinovs RNA Folding Example Slide 3 RNA Secondary Structure Primary Structure of RNA : A sequence of the bases A,G,C and U. Due to hydrogen bonds, the bases of the RNA may form base pairs a) Watson-Click base pairs: o GC : formed by a triple hydrogen bond o A=U : formed by a double hydrogen bond b) Wooble base pairs: G-U : formed by single hydrogen bond Secondary Structure of an RNA : The W-C and Wooble base pairs occurring in an RNA Fold Slide 4 Structural Features Of RNA Slide 5 MOTIVATION Why is Prediction of RNA Secondary Structure important? Tertiary Structure Prediction Identify highly conserved Motifs Design and Testing of Pharmaceutical products. Slide 6 Algorithm Input an alignment of RNA sequence Output single common structure for the sequence The Model The evolutionary model (Nussinovs) The SCFG Slide 7 Resource: http://ludwig- sun2.unil.ch/~bsondere/nussinov/form.html#nussinovhttp://ludwig- sun2.unil.ch/~bsondere/nussinov/form.html#nussinov Is this the only Secondary Structure possible? No How to distinguish the correct structure? Need for both a function and an algorithm arises at this point, Hence Dynamic Programming Key Idea of Nussinovs algorithm Recursive and only 4 possible ways Questions: Slide 8 Nussinovs Dynamic Programming Unpaired base iUnpaired base j paired i,j Bifurcation Slide 9 Energy Matrix E(i,j)=maximum energy for sub chain starting at i and ending at j and s(r i,r j )=energy of pair r i, r j (r j =base at position j) i is unpaired E(i,j)=E(i+1,j) j is unpaired E(i,j)=E(i,j-1) i,j is paired E(i,j)=E(i+1,j-1)+s(r i,r j ) Bifurcation E(i,j)=E(i,k)+E(k+1,j) Slide 10 RNA Secondary Structure Algorithm Given: RNA Sequence x 1,x 2,x 3 .,x L Initialization: E(i,i-1)=0 for i=2 to L E(i,i)=0 for i=1 to L Recursion: for n=2 to L //iteration over length E(i,j)=max{ E(i+1,j), E(i,j-1), E(i+1,j-1)+s(ri,rj) max i Trace back Algorithm Initialization: Push(1,L) onto stack Recursion: Repeat until stack is empty pop(i,j) if i>=j continue; else if E(i+1,j)=E(i,j) push(i+1,j) else if E(i+1,j)=E(i,j) push(i,j-1) else if E(i+1,j)=E(i,j) push(i,j) record i,j base pair push(i+1,j-1) else for k=i+1 to j-1 : if E(i,k)+E(k+1,j)=E(i,j); push(k+1,j) push(i,k) break Slide 15 GGGAAAUCC G000000123 G000000123 G000000122 A00000111 A000111 A00000 U0000 C000 C00 A A A U G C G Slide 16 Problems and Improvements Advantages: Accurate Output can be obtained if able to provide certain basic details Cost:O(n3) Success rate was 70% Main Drawbacks: Hairpin loops could be of any length. Developed in 1978.Therefore, it is not state of art today, but is a good starting point Improvements: Minimization Gibbs Free energy SCFGs Slide 17 References: http://ludwig-sun2.unil.ch/~bsondere/nussinov/ http://www.daimi.au.dk/~schauser/genome_analysis_F03 /lectures_F03/RNA-struct-prediction.pdfhttp://www.daimi.au.dk/~schauser/genome_analysis_F03 /lectures_F03/RNA-struct-prediction.pdf http://www.scs- pw.gmu.edu/jamison/CSI730F01/Lec10.pdfhttp://www.scs- pw.gmu.edu/jamison/CSI730F01/Lec10.pdf Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids- R.Durbin, S.Eddy, A.Korgh, G.Mitchison. University of Cambridge Press,1998 Slide 18 THANK YOU