RNA Secondary Structure Prediction

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RNA Secondary Structure Prediction

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RNA Secondary Structure Prediction Dynamic Programming Approaches Taken from Sarah Aerni

Outline RNA folding Dynamic programming for RNA secondary structure prediction Covariance model for RNA structure prediction

RNA Basics RNA bases A,C,G,U Canonical Base Pairs A-U G-C G-U 2 Hydrogen Bonds 3 Hydrogen Bonds – more stable 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. Image: http://www.bioalgorithms.info/

RNA Basics transfer RNA (tRNA) messenger RNA (mRNA) ribosomal RNA (rRNA) small interfering RNA (siRNA) micro RNA (miRNA) small nucleolar RNA (snoRNA) http://www.genetics.wustl.edu/eddy/tRNAscan-SE/

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

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

Nussinov's algorithm Base pair maximization

Base Pair Maximization – Dynamic Programming Algorithm S(i,j) is the folding of the subsequence of the RNA strand from index i to index j which results in the highest number of base pairs Simple Example: Maximizing Base Pairing Bifurcation Unmatched at i Umatched at j Base pair at i and j Images – Sean Eddy

S(i, j – 1) S(i + 1, j) S(i + 1, j – 1) +1 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 S(i, j – 1) S(i + 1, j) S(i + 1, j – 1) +1 Images – Sean Eddy

k = 0 : Bifurcation max in this case 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 k = 0 : Bifurcation max in this case S(i,k) + S(k + 1, j) Reminder: For all k S(i,k) + S(k + 1, j) Reminder: For all k S(i,k) + S(k + 1, j) Images – Sean Eddy

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

Exercise Write a python function that takes in input an RNA structure and compute the best score using the Nussinov's algorithm