RNA secondary structurecs.ucf.edu/~xiaoman/spring/lecture 9 RNA secondary... · 2020-02-05 ·...
Transcript of RNA secondary structurecs.ucf.edu/~xiaoman/spring/lecture 9 RNA secondary... · 2020-02-05 ·...
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RNA secondary structure
S.R. Eddy. Nature Biotechnology, 22:1457‐1458, 2004
Modified from Jonathan D. Wren’s slides
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RNA Basics
• 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.
2 Hydrogen Bonds3 Hydrogen Bonds – more stable“wobble” pairing – less stable
http://www.bioalgorithms.info/
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RNA types
• transfer RNA (tRNA)• messenger RNA (mRNA)• ribosomal RNA (rRNA)• small interfering RNA (siRNA)• micro RNA (miRNA)• small nucleolar RNA (snoRNA)• Long non‐coding RNA (lncRNA)• …….
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RNA has many biological functions
• Enzymatic reaction (protein synthesis) • 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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Nobel prize1989
Nobel prize2009
Ribozyme
Ribosome
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Protein structures RNA structures
Total 87979 Total 700
1137 7
148516 1524
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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 Structure
Hairpin loop
Junction (Multiloop)
Bulge Loop
Single-StrandedInterior Loop
Stem
Image– Wuchty
Pseudoknot
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RNA Secondary Structure (test)
Image– Wuchty
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E MountainsCircle with lines
Ordered tree
Balanced nested parenthesis
2D model
RNA Structure Representations
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No pseudoknots Pseudoknots
RNA secondary structure representation
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RNA secondary Structure is only an approximation
tRNA
We have to keep in mind that these 2D representations are an approximation – the real structure has 3D
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Some biological functions of non‐coding RNA
• RNA splicing (snRNAs)• Guide RNAs (RNA editing)• Catalysis• Telomere maintenance• Control of translation (miRNAs)
The function of the RNA molecule depends on its folded structure
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Control of iron levels by mRNA secondary structure
G UA G
C NN N’N N’N N’N N’
CN N’ N N’N N’N N’N N’ 5’ 3’
conserved
Iron Responsive Element(IRE)
Recognized byIRP1, IRP2
When cells are deprived of iron, IRP binds to the IRE. If IRE is located at 5'UTR, IRP binding will inhibit translation initiation, else if IRE is at 3'UTR, IRP binding will stabilize mRNA and prevent it from degradation
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IRP1/2
5’ 3’F mRNA
5’ 3’TR mRNA
IRP1/2
F: Ferritin = iron storageTR: Transferrin receptor = iron uptake
IRE
Low IronIRE‐IRP inhibits translation of FerritinIRE‐IRP Inhibition of degradation of TR
High IronIRE‐IRP off ‐> Ferritin translated
Transferrin receptor degraded
Ferritin can store 2,250 iron ions in its globular shell
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Examples of known 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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Sequence Similarity %ID = 34%gurken AAGTAATTTTCGTGCTCTCAACAATTGTCGCCGTCACAGATTGTTGTTCGAGCCGAATCTTACT 64Ifactor ---TGCACACCTCCCTCGTCACTCTTGATTTT-TCAAGAGCCTTCGATCGAGTAGGTGTGCA-- 58
* * *** ** *** *** * * ***** * *
Structural SimilarityH
St
I1
B
I2
H
St
I1
B
I2
gurken64nt stem loop
I Factor58nt stem loop
Structure‐based similarity
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RNA secondary structure prediction
Dynamic programming & free energy minimization
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Predicting RNA Secondary Structure
• According to base pairing rules only, (A‐U, G‐C and wobble pairs G‐U) sequences can potentially form many different structures
• An energy value is associated with each possible structure
• Predict the structure with the minimal free energy (MFE)
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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 sequence independent– Neighbors do not influence the energy
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Trouble with Pseudoknots
• Pseudoknots cause a breakdown in the Dynamic Programming Algorithm.
• In order to form a pseudoknot, checks must be made to ensure base is not already paired – this breaks down the recurrence relations
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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
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Bifurcation
Base Pair Maximization – Dynamic Programming Algorithm
Base pair at i and j
Unmatched at i
Umatched at j
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
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• Alignment Method– Align RNA strand to itself– Score increases for feasible base
pairs• Each score independent of overall
structure• Bifurcation adds extra dimension
Initialize first two diagonal arrays to 0
Fill in squares sweeping diagonally
Bases cannot pair, similarto unmatched alignment
S(i, j – 1)
Bases can pair, similarto matched alignment
S(i + 1, j)
Dynamic Programming –possible paths S(i + 1, j – 1) +1
Base Pair Maximization – Dynamic Programming Algorithm
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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
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Energy Minimization
• Thermodynamic Stability– Estimated using experimental techniques– Theory : Most stable is the most likely
• No pseudoknots due to algorithm limitations• Uses Dynamic Programming alignment technique• Attempts to maximize the score taking into account thermodynamics
• MFOLD and ViennaRNA
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Thermodynamics
• Gibbs Free Energy, G• Describes the energetics of molecules
in aqueous solution. The change in free energy, G, for a chemical process, such as nucleic acid folding, can be used to determine the direction of the process:
• G=0: equilibrium• G>0: unfavorable process• G<0: favorable process
• Thus the natural tendency for biomolecules in solution is to minimize free energy of the entire system (biomolecules + solvent).
• G = H ‐ TS• H is enthalpy, S is entropy, and T
is the temperature in Kelvin.• Molecular interactions, such as
hydrogen bonds, van der Waals and electrostatic interactions contribute to the H term. S describes the change of order of the system.
• Thus, both molecular interactions as well as the order of the system determine the direction of a chemical process.
• For any nucleic acid solution, it is extremely difficult to calculate the free energy from first principle
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Free energy computation
U UA A
G CG C
AG CU AA UC GA 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 4nt loop
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Adding Complexity to Energy Calculations
• Stacking energy ‐We assign negative energies to these between base pair regions.– Energy is influenced by the previous base pair (not by the base pairs further down).
– These energies are estimated experimentally from small synthetic RNAs.
• Positive energy ‐ added for destabilizing regions such as bulges, loops, etc.
• More than one structure can be predicted
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Energy Minimization Drawbacks
• Compute only one optimal structure• Usual drawbacks of purely mathematical approaches– Similar difficulties in other algorithms
• Protein structure• Exon finding
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Prediction Tools based on Energy Calculation
Fold, MfoldZucker & Stiegler (1981) Nuc. Acids Res. 9: 133-
48Zucker (1989) Science 244:48-52
RNAfoldVienna RNA secondary structure serverHofacker (2003) Nuc. Acids Res. 31: 3429-31
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Mfold = ‘Multiple’ Folding• Original (~1980) computed one single minimum
energy folding of RNA• Multiple Folding algorithm ‐ Given RNA:
1) Predict min. free energy G2) Given a set of possible folds F1…Fn, calculate their free
energies H1…Hn
3) Eliminate all folds i with Hi > G+ga) g = G*(P/100)b) P is user defined
4) Compute remaining folds; plot each with all base pairs.
http://www.bioinfo.rpi.edu/applications/mfold/
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Submitting RNA to MFOLD
Use default parameters
Scroll wayyyy down and hit“Fold RNA”
Paste your sequence
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Tools’ Features
• Sub‐optimal structures‐Provide solutions within a specific energy range.
• Constraints‐ Regions known experimentally to be single/double stranded can be defined.
• Statistical significance‐ Currently lacking in energy based methods‐ Recently was suggested to estimate a significant stable and conserved fold in aligned sequences (Washietl ad Hofacker 2004)
• Support by compensatory mutations.
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Compensatory substitutions
Expect areas of basepairing in tRNA to be covarying betweenvarious species
Base pairing creates same stable tRNA structure in organisms
Mutation in one basemakes pairing lessfavorable and breaksdown structure
Covariation ensuresability to base pair is maintained and RNAstructure is conserved
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Evolutionary conservation of RNA molecules 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 alignment about 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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RNA families
• Rfam : General non‐coding RNA database• 379 families annotating 280,000 regions
http://www.sanger.ac.uk/Software/Rfam/
Includes many families of non‐coding RNAs and functionalmotifs, as well as their alignment and secondary structures
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An example of an RNA family miR-1 MicroRNAs
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Summary
• MFOLD and other RNA secondary structure prediction tools rarely give the right answer first (or at all)– Too many possible structures in the low energy neighbourhood
• Can be used as a “first‐pass” tool– Eyeball key conserved motifs– Collect sequences to build a consensus
• Often need to adjust parameters– Use prior knowledge to force base pairing
• Motif‐searching tools can be used to identify conserved secondary structure motifs in a sequence database– Retrieves more results than sequence‐based searches