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Improving Free Energy Functions for RNA Folding

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May be possible to destroy specific sequences of RNA (to ... Crossover. P1. P2. C1. C2. Fit stems of P2 into C1 or C2 randomly. Placement must be conflict free. ... – PowerPoint PPT presentation

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Title: Improving Free Energy Functions for RNA Folding


1
Improving Free Energy Functions for RNA Folding
  • RNA Secondary Structure Prediction

2
Why RNA is Important
  • Machinery of protein construction
  • Catalytic role in cells
  • May be possible to destroy specific sequences of
    RNA (to interrupt protein production)
  • RNase P (Cech/Altman c.1981)

3
RNA Structural Levels
Secondary http//anx12.bio.uci.edu/hudel/bs99a/l
ecture21/lecture2_2.html Tertiary
http//www.leeds.ac.uk/bmb/courses/teachers/trnbal
ls.html
4
Abstracting the problem
A
G
C
G
C
A
U
C
Zuker (1981) Nucleic Acids Research 9(1) 133-149
5
Why it is hard
  • Large search space (hard to enumerate)

Hofacker et al. (1994) Monat. Chem. 125 167-188
6
Why it is hard
  • Secondary structure does not exist.
  • Unlike proteins
  • Putative structures (prone to revision)
  • Quality of Energy Functions
  • Discussed later

7
Current Algorithms
  • Single-Strand
  • Minimum Free Energy (Zuker et. al. 1981)
  • Partition Functions (McCaskill 1990)
  • Comparative Sequence Analysis
  • Max. Weighted Matching (Nussinov et. al. 1978)
  • Stochastic CFG (Sakikibara et. al. 1994)
  • Phylogenetic Trees (Gulko et. al. 1995)
  • Statistical Significance (Noller Woese, early
    80s)

See proposal for references
8
MFE / Tinoco Hypothesis
The free energy of a secondary structure equals
the sum of the free energies of the loops and
stacked pairs
Tinoco et al. (1971) Nature 230 362-367.
9
Proposed System
AAUCG...CUUCUUCCA
2
GA (E)
3
1
MFE (E)
AAUCG...CUUCUUCCA
10
Step I - Calc MFE Structure
  • Given a sequence ? apply the MFE algorithm
  • Generates secondary structure S?

11
Step II - Structural Similarity
  • Given a database of experimentally verified RNA
    structures
  • Let Q? be the database structure most similar to
    S?
  • Based on RNase P Database (Brown 1999)

12
Step III - Construct E
  • Create a new energy function

13
Discussion on E
  • E has global information
  • Global information precludes the use of dynamic
    programming (MFE, Partition)
  • Leaves (stochastic) combinatorial optimization
  • Gradient Descent (no ?E/?S)
  • Genetic Algorithms / Simulated Annealing

14
Step IV - Genetic Algorithm
  • RNA Structural Prediction by GA
  • Input sequence ?
  • Output structure that maximizes E for ?
  • Steady State Genetic Algorithm
  • Pseudoknots forbidden (conflicts)
  • Fitness -E
  • Effect of Similarity(Q?, S?) diminishes with each
    generation (pseudo-SA).

15
Genetic Algorithm - Repn.
  • Stem-loop representation (Chen et. Al. 2000)
  • Window method (EMBOSS Palindrome)

16
Genetic Algorithm - Operators
  • Mutation
  • Add stem from stem pool to a child
  • Crossover

17
Preliminary Results
  • E does not lead to drastic speed up
  • Genetic algorithm is very slow
  • If initial population generated randomly from
    stem pool.
  • Use suboptimal folding for initial population.

18
Preliminary Results Explained
  • The real structure is usually very similar the
    Tinoco optimal structure.
  • View E as a way of choosing among the suboptimal
    structures.

19
Future Work
  • More testing on the entire RNase P Database (gt
    400 structures)
  • Tune E
  • Accuracy comparison to MFE and Partition Function
    Algorithms
  • Parallelize genetic algorithm

20
  • END
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