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RNA Secondary Structure
Prediction
C SC 550 - Spring 2012
Muhammad J. Alam
Sumin Byeon
RNA

Ribonucleic acid

Single-stranded molecule

Consists of nucleotides

Each nucleotide contains
a base (A, C, G, U)
RNA Structures

Primary structure:
Linear sequence of
nucleotide bases

Secondary structure:
Hydrogen bonds
between bases forming
base pairs
RNA Structures

Hairpin loop

Stacked pair

Internal loop

Bulge

Multi loop
Problem Definition
  Input: primary structure of an RNA

  Goal: to predict the secondary structure



Given a primary structure of an RNA, find a secondary
structure that maximizes the number of base pairs
Practical Applications


Function classification

Evolutionary studies

Pseudogene detection
Different Approaches

Physical methods (Kim et al)
  X-ray diffraction, Nuclear Magnetic Resonance (NMR)


Chemical/enzymatic methods (Ehresmann et al)

Mutational analysis (Tang and Draper)
Prediction with
Sequence Only
Structure prediction based on multiple RNA
sequences which are structurally similar
(Sankoff, Gary and Stormo)


Structure prediction based on a single RNA
sequence

   Nussinov Folding Algorithm, Zuker Algorithm
Assumptions

Three base pairs
(A-U, C-G, G-U)

One base forms at most one base pair

Pseudoknots do not occur
Pseudoknots

                                       c
               a               u
   g
       a   c       g   u   g       u
Pseudoknots

                                       c
               a               u
   g
       a   c       g   u   g       u
Nussinov Folding
Algorithm
                                  ...
  1     2                                     n


Case 1: (1) and (n) form a pair

Case 2: There is (k) that is not crossed by any pair
where 1 < k < n
Nussinov Folding
Algorithm

                                  ...
  1     2                               n

Case 1: (1) and (n) form a pair

  V(1, n) = V(2, n-1) + δ(S[1], S[n])
Nussinov Folding
 Algorithm

                                               ...
    1          2                                                  n

 Case 1: (1) and (n) form a pair

   V(1, n) = V(2, n-1) + δ(S[1], S[n])

           ⇢
               1, if(x, y) 2 (a, u), (u, a), (c, g), (g, c), (g, u), (u, g)
(x, y) =
               0, otherwise
Nussinov Folding
Algorithm

                                  ...
  1     2                 k                   n

Case 1: (1) and (n) form a pair

Case 2: There is (k) that is not crossed by any pair
where 1 < k < n

  V(1, n) = V(1, k) + V(k+1, n)
Nussinov Folding
Algorithm
                 ⇢
                     V (i + 1, j 1) + (S[i], S[j])
V (i, j) = max
                     maxik<i {V (i, k) + V (k + 1, j)}
                                                    j




                                 i
Dynamic programming
                                     ...




                                              ...
Nussinov Folding
Algorithm
                 ⇢
                     V (i + 1, j 1) + (S[i], S[j])
V (i, j) = max
                     maxik<i {V (i, k) + V (k + 1, j)}


                                                   . ..

Dynamic programming
Alternate Optimization Goal
  Find the most stable structure: Zuker Algorithm

  The hydrogen bond at a base pair tries to stabilize the
  structure

  Free bases inside a loop tries to disrupt the structure

  Difference between these two is the destabilizing energy


Given a primary structure of an RNA, find the
secondary structure with least total energy
Destabilizing Energy Measure
 Stacked Pair : eS(i, j)

    Stabilizes the structure

    eS(i, j) is negative

 Hairpin : eH(i, j)

    The bigger the loop, the more unstable the structure is

    eH(i, j) depends on |j-i+1|
Destabilizing Energy Measure
 Internal Loop or Bulge : eL(i, j, i', j')

    The bigger the loop is and the more asymmetric the two
    sides are, the more unstable is the structure

    eL(i, j, i', j') depends on (|i'-i+1|+|j'-j+1|) and the asymmetry

 Multi-loop : eM(i1, j1, i2, j2, ..., ik, jk)

    The structure is more unstable if the loop size and k is big
Zuker Algorithm

Finds a secondary structure with minimum total
destabilizing energy

Uses a dynamic Programming

Running Time Exponential
Demo
Conclusion
Summary
  An algorithm that finds a secondary structure
  with the maximum number of base pairs
Future works
  Develop an algorithm that does not make the
  assumption of absence of pseudoknots
  (Gary and Stormo)

  Develop an algorithm that addresses base
  triples and other types of base pairs
Thank you

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RNA Secondary Structure Prediction

  • 1. RNA Secondary Structure Prediction C SC 550 - Spring 2012 Muhammad J. Alam Sumin Byeon
  • 2. RNA Ribonucleic acid Single-stranded molecule Consists of nucleotides Each nucleotide contains a base (A, C, G, U)
  • 3. RNA Structures Primary structure: Linear sequence of nucleotide bases Secondary structure: Hydrogen bonds between bases forming base pairs
  • 4. RNA Structures Hairpin loop Stacked pair Internal loop Bulge Multi loop
  • 5. Problem Definition Input: primary structure of an RNA Goal: to predict the secondary structure Given a primary structure of an RNA, find a secondary structure that maximizes the number of base pairs
  • 7. Different Approaches Physical methods (Kim et al) X-ray diffraction, Nuclear Magnetic Resonance (NMR) Chemical/enzymatic methods (Ehresmann et al) Mutational analysis (Tang and Draper)
  • 8. Prediction with Sequence Only Structure prediction based on multiple RNA sequences which are structurally similar (Sankoff, Gary and Stormo) Structure prediction based on a single RNA sequence Nussinov Folding Algorithm, Zuker Algorithm
  • 9. Assumptions Three base pairs (A-U, C-G, G-U) One base forms at most one base pair Pseudoknots do not occur
  • 10. Pseudoknots c a u g a c g u g u
  • 11. Pseudoknots c a u g a c g u g u
  • 12. Nussinov Folding Algorithm ... 1 2 n Case 1: (1) and (n) form a pair Case 2: There is (k) that is not crossed by any pair where 1 < k < n
  • 13. Nussinov Folding Algorithm ... 1 2 n Case 1: (1) and (n) form a pair V(1, n) = V(2, n-1) + δ(S[1], S[n])
  • 14. Nussinov Folding Algorithm ... 1 2 n Case 1: (1) and (n) form a pair V(1, n) = V(2, n-1) + δ(S[1], S[n]) ⇢ 1, if(x, y) 2 (a, u), (u, a), (c, g), (g, c), (g, u), (u, g) (x, y) = 0, otherwise
  • 15. Nussinov Folding Algorithm ... 1 2 k n Case 1: (1) and (n) form a pair Case 2: There is (k) that is not crossed by any pair where 1 < k < n V(1, n) = V(1, k) + V(k+1, n)
  • 16. Nussinov Folding Algorithm ⇢ V (i + 1, j 1) + (S[i], S[j]) V (i, j) = max maxik<i {V (i, k) + V (k + 1, j)} j i Dynamic programming ... ...
  • 17. Nussinov Folding Algorithm ⇢ V (i + 1, j 1) + (S[i], S[j]) V (i, j) = max maxik<i {V (i, k) + V (k + 1, j)} . .. Dynamic programming
  • 18. Alternate Optimization Goal Find the most stable structure: Zuker Algorithm The hydrogen bond at a base pair tries to stabilize the structure Free bases inside a loop tries to disrupt the structure Difference between these two is the destabilizing energy Given a primary structure of an RNA, find the secondary structure with least total energy
  • 19. Destabilizing Energy Measure Stacked Pair : eS(i, j) Stabilizes the structure eS(i, j) is negative Hairpin : eH(i, j) The bigger the loop, the more unstable the structure is eH(i, j) depends on |j-i+1|
  • 20. Destabilizing Energy Measure Internal Loop or Bulge : eL(i, j, i', j') The bigger the loop is and the more asymmetric the two sides are, the more unstable is the structure eL(i, j, i', j') depends on (|i'-i+1|+|j'-j+1|) and the asymmetry Multi-loop : eM(i1, j1, i2, j2, ..., ik, jk) The structure is more unstable if the loop size and k is big
  • 21. Zuker Algorithm Finds a secondary structure with minimum total destabilizing energy Uses a dynamic Programming Running Time Exponential
  • 22. Demo
  • 23. Conclusion Summary An algorithm that finds a secondary structure with the maximum number of base pairs Future works Develop an algorithm that does not make the assumption of absence of pseudoknots (Gary and Stormo) Develop an algorithm that addresses base triples and other types of base pairs

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