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Predicting RNA Structure and Function. RNA has many biological functions. Ribozyme. Ribosome. Nobel prize 1989. Nobel prize 2009. The function of the RNA molecule depends on its folded structure.

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slide2

RNA has many biological functions

Ribozyme

Ribosome

Nobel prize 1989

Nobel prize 2009

The function of the RNA molecule depends on its folded structure

slide3
The function of the RNA molecule depends on its folded structureExample: mRNA structure involved in control of Ironlevels

Iron Responsive Element

IRE

G U

A G

CN

N N’

N N’

N N’

N N’

C

N N’

N N’

N N’

N N’

N N’

conserved

Recognized by

IRP1, IRP2

5’

3’

slide4

Low Iron

IRE-IRP inhibits translation of ferritin

IRE-IRP Inhibition of degradation of TR

High Iron

IRE-IRP off -> ferritin translated

Transferin receptor degradated

F: Ferritin = iron storage

TR: Transferin receptor = iron uptake

IRP1/2

IRE

3’

5’

F mRNA

IRP1/2

3’

TR mRNA

5’

rna structural levels
RNA Structural levels

Tertiary Structure

Secondary Structure

tRNA

slide6

Protein structures

RNA structures

Total <2000

Total 72000

Due to the limited amount of data

To date (2012)

Predicting RNA tertiary structure is almost impossible

predicting rna secondary structure
Predicting RNA secondary Structure

Most common approach:

Search for a RNA structure with a

Minimal Free Energy (MFE)

rna secondary structure

3’

G A U C U U G A U C

RNA Secondary Structure
  • The RNA molecule folds on itself.
  • The base pairing is as follows:

G C A U G U

hydrogen bond.

LOOP

U U

C G

U A

A U

G C

5’ 3’

5’

STEM

rna secondary structure short range interactions
RNA Secondary structureShort Range Interactions

HAIRPIN LOOP

G G

A U

U G

C

C G

G A

U A

A U

G C

AG C U U

BULGE

INTERNAL LOOP

STEM

DANGLING ENDS

5’

3’

free energy model
Free energy model

Free energy of a structure is the sum of all interactions energies

Free Energy(E) = E(CG)+E(CG)+…..

Each interaction energy can be calculated thermodynamicly

why is mfe secondary structure prediction hard
Why is MFE secondary structure prediction hard?
  • MFE structure can be found by calculating free energy of all possible structures
  • BUT the number of potential structures grows exponentially with the number, n, of bases
rna folding with dynamic programming zucker and steigler
RNA folding with Dynamic programming (Zucker and Steigler)
  • W(i,j): MFE structure of substrand from i to j

W(i,j)

i

j

rna folding with dynamic programming
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 base pair (CG) =-1 non-pair(CA)=1 (we want a negative score )
  • Consider 4 possibilities:
    • i,jare a base pair, added to the structure for i+1..j-1
    • iis 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;

W(i,j)

i (i+1)

(j-1) j

  • Choose the minimal energy possibility
simplifying assumptions for structure prediction
Simplifying Assumptions for Structure Prediction
  • RNA folds into one minimum free-energy structure.
  • The energy of a particular base can be calculated independently
    • Neighbors do not influence the energy.
sequence dependent free energy nearest neighbor model
Sequence dependent free-energy Nearest Neighbor Model

U U

C G

U A

A U

G C

A UCGAC 3’

U U

C G

G C

A U

G C

A UCGAC 3’

5’

5’

  • Energy is influenced by the previous base pair
  • (not by the base pairs further down).
sequence dependent free energy values of the base pairs nearest neighbor model
Sequence dependent free-energy values of the base pairs (nearest neighbor model)

U U

C G

U A

A U

G C

A UCGAC 3’

U U

C G

G C

A U

G C

A UCGAC 3’

5’

5’

  • These energies are estimated experimentally from small synthetic RNAs.

Example values:

GC GC GC GC

AU GC CG UA

-2.3 -2.9 -3.4 -2.1

mfold adding complexity to energy calculations
Mfold :Adding Complexity to Energy Calculations
  • Positive energy - added for destabilizing regions such as bulges, loops, etc.
  • More than one structure can be predicted
free energy computation
Free energy computation

U U

A A

G C

G C

A

G C

U A

A U

C G

A U

A3’

A

5’

+5.9 4 nt loop

-1.1 mismatch of hairpin

-2.9 stacking

+3.3 1nt bulge

-2.9 stacking

-1.8 stacking

-0.9 stacking

-1.8 stacking

5’ dangling

-2.1 stacking

-0.3

G= -4.6 KCAL/MOL

-0.3

mfold adding complexity to energy calculations1
Mfold :Adding Complexity to Energy Calculations
  • Positive energy - added for destabilizing regions such as bulges, loops, etc.
  • More than one structure can be predicted
slide20

More than one structure can be predicted for the same RNA

Frey U H et al. Clin Cancer Res 2005;11:5071-5077

©2005 by American Association for Cancer Research

rna fold prediction based on multiple alignment
RNA fold prediction based on 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 C

G A C U U C G G U C

G G C U U C G G C C

compensatory substitutions
Compensatory Substitutions

Mutations that maintain the secondary structure

can help predict the fold

U U

C G

U A

A U

G C

A UCGAC 3’

C

G

5’

rna secondary structure can be revealed by identification of compensatory mutations
RNA secondary structure can be revealed by identification of compensatory mutations

U C

U G

C G

N N’

G C

G C C U U C G G G C

G A C U U C G G U C

G G C U U C G G C C

insight from multiple alignment
Insight from Multiple Alignment

Information from multiple sequence alignment (MSA) can help to predict the

probability 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.
slide26

MicroRNAs

miRNAs are transcribed as ~70nt precursors and subsequently processed by the Dicer enzyme to give a ~22nt product. The products are thought to have regulatory roles through complementarity to mRNA.

two major problems which can be addressed by bioinformatics
Two major problems which can be addressed by bioinformatics
  • How to find microRNA genes?
  • Given a microRNA gene, how to find its targets?
slide29

How to find microRNA genes?

  • Searching for sequences that fold to a hairpin ~70 nt
  • - 20-to 24-nt RNAs derived from endogenous transcripts
  • that form local hairpin structures
  • Concentrating in intragenic regions and introns
  • -miRNA genomic loci are distinct from other types
  • of recognized genes. Usually reside in introns.
      • Filtering out non conserved candidates
      • -Mature and pre-miRNA is usually evolutionary conserved
new human and mouse mirna detected by homology
New human and mouse miRNA detected by homology
  • Entire set of human and mouse pre- and mature miRNA from the miRNA registry was submitted to BLAT search engine against the human genome and then against the mouse genome.
  • Sequences with high % identity were examined for hairpin structure using MFOLD, and 16-nt stretch base paring.
60 new potential mirnas 15 for human and 45 for mouse
60 new potential miRNAs (15 for human and 45 for mouse)
  • Mature miRNA were either perfectly conserved or differed by only 1 nucleotide between human and mouse.

Weber, FEBS 2005

human and mouse mirnas reside in conserved regions
Human and mouse miRNAs reside in conserved regions
  • Mmu-mir-345 resides in AK0476268 RefSeq gene. Human orthologue was found upstream of C14orf69, the best BLAT hit for AK0476268.
predicting microrna target genes
Predicting microRNA target genes

MicroRNA targets are located in 3’ UTRs, and complementing mature microRNAs

  • Why is it hard to find them ??
    • 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
targetscan algorithm by lewis et al 2003
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
targetscan algorithm
TargetScan Algorithm
  • Stage 2: Extend seed matches in both directions
    • Allow G-U (wobble) pairs
targetscan algorithm1
TargetScan Algorithm
  • Stage 3: Optimize base-pairing

in remaining 3’ region of miRNA

(not applied in the later versions)

slide39

TargetScan Algorithm

  • 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)

how to make more accurate predictions
How to make more accurate predictions?
  • Incorporating mRNA UTR structure to predict microRNA targets
    • Make sure the predicted target “accessible”.
    • Not forming basing pairing its self.
how to make more accurate predictions1
How to make more accurate predictions?

Searching for Clusters MicroRNA targets conserve across species. Tends to appear in a cluster.