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Improved RNA Secondary Structure Prediction by Maximizing Expected Pair Accuracy

Improved RNA Secondary Structure Prediction by Maximizing Expected Pair Accuracy. Zhi John Lu, Jason Gloor, and David H. Mathews University of Rochester Medical Center, Rochester, New York. RNA Secondary and Tertiary Structure:. AAUUGCGGGAAAGGGGUCAA CAGCCGUUCAGUACCAAGUC

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Improved RNA Secondary Structure Prediction by Maximizing Expected Pair Accuracy

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  1. Improved RNA Secondary Structure Prediction by Maximizing Expected Pair Accuracy Zhi John Lu, Jason Gloor, and David H. MathewsUniversity of Rochester Medical Center, Rochester, New York

  2. RNA Secondary and Tertiary Structure: AAUUGCGGGAAAGGGGUCAA CAGCCGUUCAGUACCAAGUC UCAGGGGAAACUUUGAGAUG GCCUUGCAAAGGGUAUGGUA AUAAGCUGACGGACAUGGUC CUAACCACGCAGCCAAGUCC UAAGUCAACAGAUCUUCUGU UGAUAUGGAUGCAGUUCA Cate, et al. (Cech & Doudna). (1996) Science 273:1678. Waring & Davies. (1984) Gene 28: 277.

  3. Gibbs Free Energy Change: Ki = = = Ki/Kj = The structure with the lowest DG° is the most favored at a given temperature.

  4. Nearest Neighbor Model for Free Energy Change of a Sample Hairpin Loop: Mathews et al., J. Mol. Biol., 1999, 288: 911. Mathews et al., PNAS, 2004, 101: 7287.

  5. RNA Secondary Structure Prediction Accuracy: Percentage of Known Base Pairs Correctly Predicted: Mathews, Disney, Childs, Schroeder, Zuker, & Turner. 2004. PNAS 101: 7287.

  6. Limitations to Prediction of the Minimum Free Energy Structure: • A minimum free energy structure provides the single best guess for the secondary structure. • Assumes that: • RNA is at equilibrium • RNA has a single conformation • RNA thermodynamic parameters are without error • Non-nearest neighbor effects • Some sequence-specific stabilities are averaged

  7. A Method that Looks at the Probability of a Structure could be more Informative: • A partition function can be used to determine the probability of a structure at equilibrium.

  8. The Partition Function, Q:

  9. So, what is Q good for? where k is the sum over all structures with the i-j base pair.

  10. Accuracy: • Sensitivity – what percentage of known pairs occur in the predicted structure. • Positive Predictive Value (PPV) – what percentage of predicted pairs occur in the known structure. • PPV ≤ Sensitivity because the structures determined by comparative sequence analysis do not have all pairs and there is a tendency to over-predict base pairs by free energy minimization.

  11. Applying Pi,j to Structure Prediction: PPV PBP≥ 90% PPV PBP≥ 70% PPV PBP> 50% Positive Predictive Value (PPV) PPV PBP≥ 99% PPV PBP≥ 95% Sensitivity Mathews. RNA. 10: 1178. (2004).

  12. Percent of Predicted BP above Threshold: PPV PBP≥ 99% PPV PBP≥ 95% PPV PBP≥ 90% PPV PBP≥ 70% PPV PBP> 50% Mathews. RNA. 10: 1178. (2004).

  13. Color Annotation: E. coli 5S rRNA

  14. Structures Constructed from Highly Probable Pairs: PBP≥ 99% PBP≥ 90% PBP≥ 70% PBP> 50%

  15. “Maximizing Expected Accuracy:”

  16. CONTRAfold: • “Statistical learning method” to predict Pi,j • Generate structures: Where: Bioinformatics. 22: e90-e98. (2006).

  17. Implement Maximum Expectation: • Zhi John Lu, Jason Gloor, David Mathews • Implement dynamic programming algorithm using partition function prediction of Pi,j. • Also implement suboptimal structure prediction. • Alternative hypotheses.

  18. Sensitivity and PPV vs. g:

  19. Comparison:

  20. Summary: • Maximizing expected accuracy can predict structures with greater sensitivity and positive predictive value than free energy minimization. • Maximizing expected accuracy using an underlying thermodynamic model is more accurate than an underlying statistical model.

  21. Methanococcus thermolithotrophicus 5S rRNA (Szymanski et al., 1998): MaxExpect Predicted Structure:

  22. Minimum Free Energy Structure: CONTRAfold Predicted Structure:

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