predicting rna structure and function. 1989 2006 2009
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PredictingRNA Structure and Function
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1989
2006
2009
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The Ribosome : The protein factory of the cell mainly made of RNA
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Non coding DNA (98.5% human genome)
• Intergenic
• Repetitive elements
• Promoters
• Introns
• untranslated region (UTR)
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Some biological functions of ncRNA
• Control of mRNA stability (UTR)
• Control of splicing (snRNP)
• Control of translation (microRNA)
The function of the RNA molecule depends on its folded structure
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Example:Control of Iron levels by mRNA structure
G U A GC N N N’ N N’ N N’ N N’C N N’ N N’ N N’ N N’ N N’ 5’ 3’
conserved
Iron Responsive ElementIRE
Recognized byIRP1, IRP2
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IRP1/2
5’ 3’F mRNA
5’ 3’TR mRNA
IRP1/2
F: Ferritin = iron storageTR: Transferin receptor = iron uptake
IRE
Low Iron IRE-IRP inhibits translation of ferritinIRE-IRP Inhibition of degradation of TR
High IronIRE-IRP off -> ferritin translated
Transferin receptor degradated
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RNA Structural levels
tRNA
Secondary Structure Tertiary Structure
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RNA Secondary Structure
U U
C G U A A UG C
5’ 3’
5’G A U C U U G A U C
3’
STEM
LOOP• The RNA molecule folds on itself. • The base pairing is as follows: G C A U G U hydrogen bond.
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RNA Secondary structureShort Range Interactions
G G A U
U GC C GG A U A A U G CA G C U U
INTERNAL LOOP
HAIRPIN LOOP
BULGE
STEM
DANGLING ENDS5’ 3’
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long range interactions of RNA secondary structural elements
Pseudo-knot
Kissing hairpins
Hairpin-bulge contact
These patterns are excluded from the prediction schemes as their computation is too intensive.
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Predicting RNA secondary Structure
• Searching for a structure with Minimal
Free Energy (MFE)
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Free energy model• Free energy of structure (at fixed temperature, ionic
concentration) = sum of loop energies
• Standard model uses experimentally determined thermodynamic parameters
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Why is MFE secondary structure prediction hard?
• MFE structure can be found by calculating free energy of all possible structures
• BUT number of potential structures grows exponentially with the number, n, of bases
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RNA folding with Dynamic programming (Zuker and Steigler)
• W(i,j): MFE structure of substrand from i to j
i j
W(i,j)
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RNA folding with dynamic programming• Assume a function W(i,j) which is the MFE for the sequence starting
at i and ending at j (i<j)
• Define scores, for example a base pair’s score is less than a non-pair• Consider 4 possibilities:
– i,j are a base pair, added to the structure for i+1..j-1– i is unpaired, added to the structure for i+1..j– j is unpaired, added to the structure for i..j-1– i,j are paired, but not to each other;
• Choose the minimal energy possibility
i (i+1)
W(i,j)
(j-1) j
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Simplifying Assumptions for Structure Prediction
• RNA folds into one minimum free-energy structure.
• There are no knots (base pairs never cross).
• The energy of a particular base pair in a double stranded regions is calculated independently– Neighbors do not influence the energy.
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Sequence dependent free-energy Nearest Neighbor Model
U U
C G G C A UG CA UCGAC 3’5’
U U
C G U A A UG CA UCGAC 3’5’
Assign negative energies to interactions between base pair regions.Energy is influenced by the previous base pair (not by the base pairs further down).
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Sequence dependent free-energy values of the base pairs
(nearest neighbor model) U U
C G G C A UG CA UCGAC 3’5’
U U
C G U A A UG CA UCGAC 3’5’
Example values:GC GC GC GCAU GC CG UA -2.3 -2.9 -3.4 -2.1
These energies are estimated experimentally from small synthetic RNAs.
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Mfold :Adding Complexity to Energy Calculations
• Positive energy - added for destabilizing regions such as bulges, loops, etc.
• More than one structure can be predicted
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Free energy computation
U UA A G C G C A G C U A A U C G A U A 3’A5’
-0.3
-0.3
-1.1 mismatch of hairpin-2.9 stacking
+3.3 1nt bulge -2.9 stacking
-1.8 stacking
5’ dangling
-0.9 stacking-1.8 stacking
-2.1 stacking
G= -4.6 KCAL/MOL
+5.9 4 nt loop
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Prediction Tools based on Energy Calculation
Fold, Mfold Zucker & Stiegler (1981) Nuc. Acids Res.
9:133-148Zucker (1989) Science 244:48-52
RNAfoldVienna RNA secondary structure serverHofacker (2003) Nuc. Acids Res. 31:3429-3431
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Insight from Multiple Alignment
Information from multiple sequence alignment (MSA) can help to predict the probability of positions i,j to be base-paired.
G C C U U C G G G CG A C U U C G G U CG G C U U C G G C C
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Compensatory Substitutions
U U
C G U A A UG CA UCGAC 3’
G C
5’
Mutations that maintain the secondary structure
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RNA secondary structure can be revealed by
identification of compensatory mutations
G C C U U C G G G CG A C U U C G G U CG G C U U C G G C C
U CU GC GN N’G C
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Insight from Multiple Alignment
Information from multiple sequence alignment (MSA) can help to predict theprobability of positions i,j to be base-paired.
•Conservation – no additional information•Consistent mutations (GC GU) – support stem•Inconsistent mutations – does not support stem.•Compensatory mutations – support stem.
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RNAalifold (Hofacker 2002)From the vienna RNA package
Predicts the consensus secondarystructure for a set of aligned RNA sequences by using modified dynamic programming algorithm that addalignment information to the standardenergy model
Improvement in prediction accuracy
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Other related programs
• COVE
RNA structure analysis using the covariance model (implementation of the stochastic free grammar method)
• QRNA (Rivas and Eddy 2001)
Searching for conserved RNA structures
• tRNAscan-SE tRNA detection in genome sequences
Sean Eddy’s Lab WUhttp://www.genetics.wustl.edu/eddy
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RNA families
• Rfam : General non-coding RNA database
(most of the data is taken from specific databases)
http://www.sanger.ac.uk/Software/Rfam/
Includes many families of non coding RNAs and functionalmotifs, as well as their alignment and their secondary structures
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Rfam /Pfam
• Pfam uses the HMMER
(based on Hidden Markov Models)
• Rfam uses the INFERNAL
(based on Covariation Model)
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Rfam (currently version 7.0)
• Different RNA families or functional
Motifs from mRNA, UTRs etc.
View and download multiple sequence alignments Read family annotation Examine species distribution of family members Follow links to other databases
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An example of an RNA family miR-1 MicroRNAs
mir-1 microRNA precursor family This family represents the microRNA (miRNA) mir-1 family. miRNAs are transcribed as ~70nt precursors (modelled here) and subsequently processed by the Dicer enzyme to give a ~22nt product. The products are thought to have regulatory roles through complementarity to mRNA.
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Seed alignment (based on 7 sequences)
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Predicting microRNA target
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Predicting microRNA target genes
• Why is it hard??– Lots of known miRNAs with similar seeds– Base pairing is required only for seed (7 nt)
Very High probability to find by chance
• Initial methods– Look at conserved miRNAs– Look for conserved target sites– Consider the RNA fold
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TargetScan Algorithm by Lewis et al 2003
The Goal – find miRNA candidate target genes of a given miRNA
• Stage 1: Select only the 3’UTR of all genes
– Search for 7nts which are complementary to bases 2-8 from miRNA (miRNA seed”) in 5’UTRs
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TargetScan Algorithm
• Stage 2: Extend seed matches in both directions
– Allow G-U (wobble) pairs
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TargetScan Algorithm
• Stage 3: Optimize base-pairing
in remaining 3’ region of miRNA
(not applied in the later versions)
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• Stage 4: Calculate the folding free energy (G) assigned to each putative miRNA:target interaction using RNAfold
Low energy get high Score
• Stage 5: Calculate a final score for a UTR to be a target
adding evolutionary conservation (by doing the same steps on UTR from other species)
TargetScan Algorithm
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Predicting targets of RNA-binding protein (RBP)
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Predicting RBPs target• Different types of RBPs
– Proteins that regulate RNA stability (bind usually at the 3’UTR)
– Splicing Factors (bind exonic and intronic regions) – ……
• Why is it hard– Different proteins bind different sequences – Most RBP sites are short and degenertaive (e.g.
CTCTCT )
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Predicting Exon Splicing Enhancers ESE-finder (Krainer)
1. Built PSSM for ESE, based on experimental data (SELEX)
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ESE-finder
2. A given sequence is tested against5 PSSM in overlapping windows
3. Each position in the sequence is given a score
4. Position which fit a PSSM (score above a cutoff) are predicted asESEs
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Predicting RBPs target• RNA binding sites can be predicted by
general motif finders
- MEME http://metameme.sdsc.edu/mhmm-overview.html
- DRIM http://bioinfo.cs.technion.ac.il/drim/